?2854665 Summary - Canadian Patents Database (2024)

CA 02854665 2014-05-05
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PCT/US2012/064693
GENE EXPRESSION SIGNATURES OF NEOPLASM RESPONSIVENESS TO THERAPY
CROSS-REFERENCE TO RELATED APPLICATION
This application claims the benefit of U.S. Provisional application No.
61/558,402, filed November
10, 2011. The disclosure of the prior application is incorporated by reference
herein in its entirety.
FIELD
This disclosure relates to cancer, and particularly to treatment of a
neoplasm, methods of predicting
treatment responsiveness of a neoplasm, and methods of determining prognosis
of a subject with a
neoplasm.
PARTIES TO JOINT RESEARCH AGREEMENT
This invention was made under Public Health Service Cooperative Research and
Development
Agreement (PHS-CRADA) No. 00836 between the National Institutes of Health
National Cancer Institute
and Syndax Pharmaceuticals, Inc.
BACKGROUND
Histone deacetylase (HDAC) inhibitors (HDACi) and mechanistic target of
Rapamycin (mTOR)
inhibitors (mTORi) are known anti-cancer agents. The combined use of these
agents is known to have anti-
cancer efficacy against certain neoplasm subtypes; however, this combined
treatment is not efficacious in all
neoplasm subtypes, and is not efficacious against all neoplasms within a
particular subtype.
SUMMARY
There is a need, for example, for methods of identifying neoplasms that are
sensitive to
mTORi/HDACi combination therapy, as well as for methods that enable
determination of the likely outcome
(e.g., prognosis) of a neoplasm or a subject having a neoplasm. Accordingly,
disclosed herein are gene
expression signatures indicative of neoplasms that are sensitive to
mTORi/HDACi combination therapy.
Detection of such a signature in a neoplasm sample from a subject can be used
to identify a subject having a
neoplasm sensitive to mTORi/HDACi combination therapy, as well as for
identifying a therapeutically
effective amount of such therapy for use in the subject.
Unexpectedly, these gene expression signatures are also useful for prognosis.
Thus, in some
embodiments, detection of one of the gene expression signatures in a neoplasm
sample from a subject
indicates a poor prognosis.
The foregoing and other objects, features, and advantages of the embodiments
will become more
apparent from the following detailed description, which proceeds with
reference to the accompanying
figures.
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BRIEF DESCRIPTION OF THE DRAWINGS
FIGs. IA-1D are a series graphs illustrating in vitro and in vivo studies of
growth inhibition. (A)
Combination treatment with entinostat (also known as MS-275) and sirolimus
(also known as Rapamycin)
was synergistic in its effect on growth inhibition in 90% of multiple myeloma
(MM), mouse plasmacytoma
(PCT) and mantle cell lymphoma (MCL) cell lines tested. The bar graph shows,
in order, sirolimus,
Entinostat, and combination sirolimus and Entinostat, treatment for each cell
line. (B) Time course photon
flux imaging of L363 xenografts during treatment with vehicle (control) or
entinostat (10 and 20 mg/kg),
sirolimus (2.5 and 5 mg/kg), and the combination (2.5 mg/kg of sirolimus and
20 mg/kg entinostat). (C)
Tumor weights of L363 xenografts at the conclusion of treatment. There were no
palpable tumors in the
mice receiving combination treatment. (D) Tumor weights of U266 xenografts
after twelve weeks of
treatment (except for untreated controls, which were collected at four weeks).
In all panels, (*) represents p
value <0.05 for the combination treatment relative to vehicle and single agent
treatments (ANOVA,
Bonferroni's multiple comparisons test).
FIGs. 2A-2P are a series of graphs illustrating dose response curves in a
panel of cell lines. Single
agent dose response curves for (A,B) L363 (MM), (C,D) EJM (MM), (E,F) JeKo
(MCL), (G,H) 5P53
(MCL), (IõJ) M0PC265 (PCT), (K,L) MOPC460 (PCT) cell lines. For fine-tuning
the combination dose in
L363 (MM) cells, CompuSyn analyses of the dose-responses for L363 cells was
performed and is shown in
the (M) dose-effect curve, (N) the combination index plot and (0) the
normalized isobologram. (P) Single
agent and combination treatment had little effect on viability of PBMCs from
healthy volunteers (n = 2) at
24 or 48 hours.
FIGs. 3A-3B are a set of graphs illustrating body weight of control and drug-
treated tumor bearing
nude mice over time. (A) L363 or (B) U266 xenografts with vehicle (control) or
treatment with MS-275 (10
and 20 mg/kg), Rapamycin (2.5), and the combination (2.5 mg/kg of Rapamycin
and either 10 or 20 mg/kg
MS-275). Animals in the control arm of the U266 study were euthanized at 4
weeks due to tumor burden.
FIGs. 4A-4C are a set of digital images illustrating Western showing analysis
of (A) L363, (B)
U266, (C) 5P53, and cell lysates from either untreated cells or cells treated
with the indicated single agent or
combination of agents. S6 phosphorylation and H3/H4 acetylation (AcH3/H4) are
targets of mTOR and
HDAC inhibition, respectively.
FIGs. 5A-5E are a series of graphs and digital images illustrating cell cycle
and apoptosis analysis
of cells treated with Rapamycin, MS-275, or a combination thereof. Cell cycle
analysis of (A) U266 and (B)
L363 cells, control or treated with drugs for 48 hours. Cells were treated
individually with entinostat, or
sirolimus, or in combination with either simultaneous or sequential treatment.
In sequential experiments, the
first agent listed was added 24 hours prior to the addition of the second
agent. Percentage of (C) U266 or (D)
L363 cells in apoptosis was determined by Annexin V at 48 hours. Western blot
of (E) U266 or L363 lysates
after 48 hours of control, sirolimus, entinostat, or combination treatment
probed for cleaved PARP.
FIGs. 6A-6E are a series of graphs and digital images illustrating flow
cytometry analysis studies.
Flow cytometry analysis of L363 for phospho-proteins: (A) 4 hour and 48 hour
untreated cells and cells
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treated with single agents or the combination stained with p-AKTs' 473
antibody; (B) 4 hour untreated, single
agent or combination treated cells stained with p-ERK1/2Thr202/TYr204
antibody. p-AKTser473 levels were
increased slightly by sirolimus compared to other treatments and untreated
cells. Combination treated cells
had lower p-AKTser473 levels and considerably lower p-ERK1/2Thr202/Tyr204
levels compared to levels in cells
individual drug treatment. Cell cycle analysis of (C) SP53 (MCL) cell line
treated with single drugs and
combination at 48 hours. R 1+M2 indicates that sirolimus was given 24 hours
prior to entinostat treatment;
M1+R2 indicates that entinostat treatment preceded sirolimus by 24 hours.
Western blot of control, single
agent, and combination treated (D) L363 or (E) U266 cells at 48 hours.
FIG. 7 is a diagram illustrating analytic workflow for microarray data pre-
processing and analyses
of variance (ANOVA). The 1647 genes selected with the ANOVA models were used
to generate a network
of highly co-expressed genes by weighted gene co-expression network analysis
WGCNA.
FIGs. 8A-8D are a series of volcano plots of statistical significance against
expression change in the
set of genes analyzed with the ANOVA models. On the y-axis, negative 10g10 of
p-values from an ANOVA
test are plotted and the log2 fold changes in expression on the x-axis. Genes
with statistically significant
treatment at the 0.01 significance level are shaded medium grey. Genes with
expression change greater than
two-fold lie outside the vertical lines and are colored with a darker shadow.
The Q-values (Storey and
Tibshirani, Proc. Natl. Acad. Sci. U.S.A., 100:9440-9445, 2003) indicate the
range of false discovery rates
for the gene selections at the 0.01 significance level. (A) Additive two-way
ANOVA main effect for the MS-
275 treatment. (B) Additive two-way ANOVA main effect for the Rapamycin
treatment. (C) Full two-way
ANOVA interaction effect for the MS-275 and Rapamycin treatments. (D) One-way
ANOVA contrast for
the combined treatment effect.
FIGs. 9A-9B are a series of graphs illustrating modular network construction.
(A) Average
hierarchical clustering dendrogram of genes using the one minus topological
overlap dissimilarity metric
(Langfelder, BMC Bioinformatics., 9:559, 2008). Branches of the dendrogram
comprise densely
interconnected, highly co-expressed genes (modules), assigned the original
module colors (top bar) and the
final merged module colors (bottom bar). The original modules were identified
with the Dynamic Cut Tree
algorithm and summarized by their first principal component of the expression
values (module eigengene).
Modules with highly correlated eigengenes (correlation coefficient > 0.80)
were merged into the final
modules. The Gray module contains the unassigned genes. (B) Scale-free
topology fit of the weighted gene
co-expression network (soft threshold p = 8). On the x-axis, 10g10 of
connectivity (k) is plotted, on the y-
axis log10 of the proportion of nodes having given connectivity (p(k)). The
distribution of total connectivity
(left) and intramodular connectivity (right) was examined. The straight line
shows the power-law fit and the
curved line shows the exponentially truncated power-law fit.
FIGs. 10A-10E are a series of graphs and charts illustrating network
visualizations and module
selection to identify the genes affected by both inhibitors. (A) Gene average
linkage hierarchical clustering
on topological overlap-based dissimilarity and drug-specific module
partitioning. Five modules designated
as blue, red, darkgreen, springgreen, and orange were identified. (B) The
criteria for selection of the drug-
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related modules linked the ANOVA assessments of treatment effects of sirolimus
and entinostat with the
network module topology. On top are bar plots of the Pearson's correlation
coefficients (r) of intramodular
connectivity (kIN) and gene significance (GS) values in a module, and on the
bottom are bar plots of the
mean gene significance in a module (MS SEM). An asterisk indicates that
module relevance to a drug
treatment was significant (p<0. 01). (C) Network of the 901 most connected
nodes (genes) from the drug-
specific modules (Cytoscape edge-weighted, spring-embedded layout algorithm).
At least 901 genes were
affected by single and double agent treatment. Nodes are colored by module
assignment, and sizes are
proportional to within-module connectivity. (D) Venn diagram showing the
number of genes with
expression changes related to the individual or combination drug treatments.
(E) Heatmaps of networks by
module, corresponding to significant drug-specific effects (white,
upregulated; black, downregulated):
Cooperative Combination (blue), Neutral Combination (orange), entinostat
(springgreen), entinostat
(darkgreen), and sirolimus (red). Expression values are mean centered by rows.
The eigengene values
summarize the major vector (first principal component) of expression in a
module. At least 126 genes
contributing to the synergy of the drug combination were identified.
FIGs. 11A-11B are a series of scatter plots illustrating the relationship
between drug treatment-
based gene significance (negative log10 P-value from two-way ANOVA models) and
intramodular
connectivity for each network module identified. The vertical line indicates
the 0.01 threshold of gene
significance. A regression line has been added to each plot. Box plots above
the scatter plots depict the
distribution of gene significance in a module; the additional vertical line
crossing the inter-quartile box is the
mean significance in a module. Pearson's correlation coefficient (R) and its
significance (Bonferroni
corrected P-values), as well as the module significance score (the mean GS)
are reported below each plot.
Genes in the blue and orange modules were affected by both drugs. Genes in the
spring- and dark green
modules were affected by MS-275 and genes in the red module were affected by
rapamycin/sirolimus.
FIG. 12 is a diagram illustrating the functional enrichment of genes
cooperatively regulated by
mTORi/HDACi and REVIGO visualization of functionally-related GO terms for the
Cooperative
Combination (blue) module.
FIGs. 13A-13D are a series of graphs and digital images illustrating hub gene
RRM2 validation.
RRM2 is involved in DNA replication. (A) Cooperative module genes co-expressed
with the RRM2 hub
gene (scaled kIN=0.67). Node size is proportional to intra-modular
connectivity (scaled kIN from 0.37 to 1);
the edge color darkens with an increase in pairwise adjacency (between 0.30-
0.91, and corresponds to
correlation coefficient 0.86-0.99); node label (starred/not starred) depicts
the expression fold up-/down-
regulation due to combination treatment). (B) Graph of RRM2 expression from
microarray; the broken line
indicates expected additive effect. (C) Comparison of RRM2 expression between
healthy donor CD138+
cells and CD138+ cells from newly diagnosed and treatment relapsed patients in
the G5E6477 patient
dataset (Irizarry et al., Biostatistics, 4:249-264 2003). Western blot of
lysates from the L363 cell line treated
for 48 hours with sirolimus (10 nM), entinostat (0.5 [tM), or Triapine (1
[tM), or combinations thereof. (D-
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E). L363 cell viability after 48 hour treatment with triapine and/or
sirolimus. Significance between
treatments was determined by repeated measures ANOVA with the Bonferroni
correction.
FIG. 14 shows a workflow schematic detailing filtering steps to define genes
cooperatively affected
by mTORi/HDACi combination treatment and associated with survival in MM
patients.
FIGs. 15A-15B are a set of graphs illustrating enrichment of genes regulated
by the drug
combination in gene sets comparing patient and healthy plasma cells. (A) The
expression pattern
representing the disease signature, assessed by comparing relapsed MM patients
with healthy controls (t-
statistic, right) was the opposite of the drug response signature, assessed by
treating L363 cells with the drug
combination (fold change, left). Node color reflects the direction of gene
expression: white genes
overexpressed (patients) or up-regulated (drug treated cell line) and black is
under-expressed (patients) or
down-regulated (drug treated cell line). Note: Of the 901 top connected genes
(FIG. 4C), 594 were available
for gene expression analysis in dataset G5E6477. (B) Gene set enrichment
analysis (GSEA) of the
combination cooperative (blue) module up- and down-regulated genes. One-way
ANOVA contrast t-
statistics were used to rank the genes according to their correlation with
either the Multiple Myeloma
phenotype (red bar) or the healthy donor phenotype (blue bar). The graph on
the bottom of each panel
represents the ranked, ordered list of ¨13,000 unique genes. Black vertical
lines show the position of
individual genes from a gene set module in the ordered list of genes. The
green line is the profile of the
running sum of the weighted enrichment score with the maximum deviation from
zero encountered in the
random walk (ES). The normalized enrichment score (NES) is the enrichment
score adjusted for variation in
the gene set size. GSEA was performed for the four groups of multiple myeloma
patients reported in
G5E6477 (Carrasco et al., Cancer Cell, 9:313-325, 2009; Chng et al., Cancer
Res. 67:2982-2989, 2007).
FIGs. 16A-16B are a set of graphs illustrating GSEA enrichment score curves.
Gene set enrichment
analysis (GSEA) was performed with the network module gene sets, for which at
least ten genes were
available in the MM patient data (Red_UP and Orange_DOWN sets were excluded
because of small number
of genes). One-way ANOVA contrast t-statistics were used to rank the genes
according to their correlation
with either the Multiple Myeloma phenotype (red bar) or the healthy donor
phenotype (blue bar). The graph
on the bottom of each panel represents the ranked, ordered list of ¨13,000
unique genes. Black vertical lines
show the position of individual genes from a gene set module in the ordered
list of genes. The green line is
the profile of the running sum of the weighted enrichment score with the
maximum deviation from zero
encountered in the random walk (ES). The normalized enrichment score (NES) is
the enrichment score
adjusted for variation in the gene set size. GSEA were performed for the four
groups of multiple myeloma
patients in G5E6477 (Irizarry et al., Biostatistics, 4:249-264 2003). Some of
the genesets enriched in new
and relapsed patients were also enriched in SMM (smoldering myeloma) and
monoclonal gammopathy of
undetermined significance (MGUS) patients.
FIG. 17 depicts a schematic diagram of the ten-fold cross-validation and
single split validation
scheme for the training and testing of the multivariate survival risk
predictor (37 genes) using the principal
component method of Bair and Tibshirani (J Biol Chem.;284:18085-18095, 2009)
and BRB-ArrayTools
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software. The patient dataset published by Than et al. (GSE4581; Blood,
108:2020-2028, 2006) was used to
build the predictor.
FIGs. 18A-18C are a series of graphs illustrating that the expression of
cooperative (blue) module
genes correlates with survival in multiple myeloma patients. Kaplan-Meier
survival curves showing overall
survival in patients: (A) (left) Cross-validated "training set" stratified
into low risk (N=106) and high risk
(N=101) groups (principal components classifier). Permutation P-value computed
for the log-rank test.
(right) Single split test set stratified into low risk (N=97) and high risk
(N=110) groups. Asymptotic p-values
were computed for the log-rank test. (B) Survival predictor gene expression
(median centered) heatmap of
207 patients in test set. Samples are ordered by increasing risk score from
the survival classifier and plotted
above the heatmap. Black bars indicate death. (C) Cytoscape graph of 37
cooperative module genes in the
survival prediction model. Top: node color (red/green) depicts the expression
fold up-/down-regulation due
to combination. Bottom: node color reflects value of univariate Cox regression
coefficients: (white,
increased risk of death associated with increasing gene expression; black,
increased risk of death associated
with decreasing gene expression). Node size reflects scaled intramodular
connectivity, and hub genes are
grouped on the left side of each sub-network. Increased adjacency (higher
connection strength between
nodes) is indicated by darker edge color. The drug combination effects are
opposite to the gene expression
associated with poor prognosis, except KIAA201 (triangular shape).
FIGs. 19A-19C are a series of graphs illustrating that expression of drug-
response network genes
correlates with survival in multiple myeloma patients. The 901 genes of the
entire drug response network
were input in the multivariate predictor algorithm; 124 genes were selected as
the survival classifier. Kaplan-
Meier survival curves showing overall survival in patients: (A) Cross-
validated "training set" stratified into
low risk and high risk groups (principal components classifier). Permutation P-
value computed for the log-
rank test. (B) Single split test set stratified into low risk and high risk
groups. Asymptotic p-values were
computed for the log-rank test. (C) Survival predictor gene expression (median
centered) heatmap (124
genes) of 207 patients in test set. Samples are ordered by increasing risk
score from the survival classifier
and plotted above the heatmap. Black bars indicate death. These data indicate
that some patients are likely to
derive benefit from single agent treatment even though most patients would be
likely to benefit from the
combination.
FIGs. 20A-20B are a set of graphs illustrating Passing-Bablok linear
regression analysis of the drug
dose effect on L363 transcriptional profiles of the 37 genes linked to a
survival signature; two different
concentrations of sirolimus (1 or 10 nM) were compared when given in
combination with 0.5mM entinostat.
(A) Regression of the mean expression values for the 37 genes in the survival
signature from GEP
experiments between the two different concentrations of Rapamycin (1 or 10
nM). The correlation
(Pearson's r = 0.9) between the two drug concentrations for expression of the
37 genes was significant (p<
2.216). (B) The treatment effect of the two different drug combinations is
depicted as a log2 fold change in
gene expression of the combination treatment versus untreated L363 cells. Gene
order on the x-axis is
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determined by the degree of difference in gene expression fold change between
the two sirolimus doses.
These data demonstrate that the 37 gene set acts in a pharmacodynamic manner.
FIG. 21 is a heatmap depicting mean centered expression of the 37 genes
(cooperative survival
classifier) in a panel of untreated Human MM cell lines. For comparison, the
differential expression (log2
fold change) between normal healthy donor CD138+ cells and cells from newly
diagnosed or treatment
refractory MM patients (GSE6477; Carrasco et al., Cancer Cell, 9:313-325,
2009; Chng et al., Cancer Res.
67:2982-2989, 2007). The majority of the 37 genes are overexpressed in MM cell
lines with only a few
showing underexpression. For newly diagnosed (NEW) and relapsed (REL), black:
MM vs ND<O, grey:
MM vs ND>0, white: gene not available on the chip (6 genes).
FIG. 22 is a graph depicting the high correlation between expression fold
change detection in
combination treated L363 cells between the Affymetrix0 microarray platform and
the Nanostring0 probe-
based gene expression platform
FIG. 23 is a heatmap showing log2 expression fold change of 19 survival-
associated, cooperatively
affected genes in the MM cell line L363 as detected by microarray and
Nanostring0 platforms. Log2
expression fold change is shown for single agent Rapamycin, MS-275, and
panobinostat (a pan-HDAC
inhibitor), as well as the combination of Rapamycin/MS-275, and
Rapamycin/panobinostat.
FIG. 24 is a heatmap showing log2 expression fold change of 19 survival-
associated, cooperatively
affected genes in the human MM cell line U266 as detected by the Nanostring0
platform. Log2 expression
fold change is shown for single agent Rapamycin, MS-275, and panobinostat (a
pan-HDAC inhibitor), as
well as the combination of Rapamycin/MS-275, and Rapamycin/panobinostat.
FIGs. 25A-25D are a set of plots of log2 fold change expression (untreated vs.
Rapamycin+MS-
275) of the survival-associated 37-gene cooperative drug response signature in
15 human MM cell lines and
1 human breast cancer cell line (MCF-7) for comparison. Shaded grey bars on
each graph depict the log2
expression fold change of R+M treated L363 (Combination responsive cell line)
as a comparator. The r
value for each line is the comparison of its response with L363. Of particular
note, KMS-26, KMS-18, OCI-
MY5, KMS-20, and EJM all have <EC50 response to this combination dose (10 nM
Rapamycin + 500 nM
MS-275 for 48 hours).
FIG. 26 is a heatmap of log2 fold change expression (untreated vs.
Rapamycin+MS-275) of the
survival-associated 37-gene cooperative drug response signature in 15 human MM
cell lines and 1 human
breast cancer cell line (MCF-7) for comparison. Of particular note, KMS-26,
KMS-18, OCI-MY5, KMS-20,
and EJM all have <EC50 response to this combination dose (10 nM Rapamycin +
500 nM MS-275 for 48
hours).
FIGs. 27A-27C are a set of heatmaps illustrating the intensity of gene
expression in a series of cell
lines before and after mTORi/HDACi combination treatment. The log2 gene
expression intensity before
(FIG. 27A) and after (FIG. 27B) Rapamycin/MS-275 combination treatment of the
survival-associated 37-
gene cooperative drug response signature in 15 human MM cell lines and one
human breast cancer cell line
(MCF-7; for comparison) is shown. Euclidean hierarchical clustering was used
to cluster the genes and cell
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lines based on untreated expression. Of particular note, KMS-26, KMS-18, OCI-
MY5, KMS-20, and EJM
all have <EC50 response to this combination dose (10 nM Rapamycin + 500 nM MS-
275 for 48 hours). The
pharmacodynamic nature of this gene expression classifier is further
illustrated in FIG. 27C, where the log2
fold change of gene expression is shown as measured at 8, 24, and 48 hour time
points after in vitro
combination treatment.
FIG. 28 is a series of digital images illustrating Western blots showing
protein expression of 11
survival-associated cooperative drug response signature genes in untreated and
R+M combination treated
(48 hours) human MM cell lines.
FIG. 29 is a graph illustrating the distribution of patient groups classified
by the 37-gene
mTORi/HDACi signature in the seven molecular subtypes of MM (CD-1, CD-2
(CCND1/CCND3
subgroups 1 and 2), HY (hyperdiploid), LB (low bone disease), MF (MAF/MAFB),
MS (MMSET), PR
(proliferation subgroup)) as defined in GSE4581 (Zhan et al., Blood, 108:2020-
2028, 2006). The graph
shows survival rate on the Y-axis and survival time on the X-axis.
FIG. 30 illustrates the distribution of patient groups classified by the 37-
gene mTORi/HDACi
signature between patients having a HIGH or LOW Proliferation Index (PI)
scores. The average expression
of the 11 PI genes (Than et al., Blood, 108:2020-2028, 2006) was taken for
each patient. HIGH PI defined as
index higher than median PI of all 414 patients, and LOW PI defined as index
lower than median. The 37
genes act in a fashion unlinked to proliferative index, despite the fact that
most patients with a high
proliferative index are likely to benefit from the drug combination.
FIG. 31 shows Kaplan-Meier Survival curves for patient groups classified by
the 37-gene
mTORi/HDACi signature within the seven molecular subtypes of MM (CD-1, CD-2
(CCND1/CCND3
subgroups 1 and 2), HY (hyperdiploid), LB (low bone disease), MF (MAF/MAFB),
MS (MMSET), PR
(proliferation subgroup)) as defined in G5E4581 (Zhan et al., Blood, 108:2020-
2028, 2006).
FIG.s 32A-32BB show a series of charts illustrating the use of the identified
genes in the Blue
module gene expression signature for the prognosis of several different tumor
types including squamous cell
lung carcinoma (B-C), cutaneous melanoma (D-E), pleomorphic liposarcoma (F-G),
colon adenoma (H-I),
multiple myeloma (J-K), papillary renal cell carcinoma (L-M), melanoma (N-0),
glioblastoma (P-Q),
chronic lymphocytic leukemia (R-S), invasive breast carcinoma stroma (T-U),
ovarian serous
cystadenocarcinoma (V-W), invasive breast carcinoma (X-Y), glioblastoma (Z-
AA), mantle cell lymphoma
(BB). The genes analyzed are indicated on each chart, and were analyzed using
ONCOMINETm (Compendia
Bioscience, Ann Arbor, MI). Chart A summarizes the unique expression of the
analyzed gene signatures
across several tumor types.
FIG. 33. Human cell lines for Burkitt's lymphoma and melanoma, and a mouse
prostate cancer cell
line respond to the drug combination in a synergistic fashion with respect to
cell proliferation.
FIG. 34. is a heatmap showing log2 expression fold change of the 37 survival-
associated,
cooperatively affected genes in Burkitt's lymphoma, human melanoma and a mouse
prostate cancer cell line
as detected by the Nanostring platform. Log2 expression fold change is shown
for single agent Rapamycin,
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MS-275, and panobinostat (a pan-HDAC inhibitor), as well as the combination of
Rapamycin/MS-275, and
Rapamycin/panobinostat.
FIG. 35 is a heatmap illustrating the mean centered expression of the 37 genes
(cooperative survival
classifier) in a panel of untreated Human Breast Cancer cell lines. This
heatmap demonstrates that there are
a number of human breast cancer cell lines that are likely to respond to the
drug combination.
FIGs. 36A-36C are a series of heatmaps showing log2 expression fold change of
the survival-
associated, 37-gene mTORi/HDACi signature in the human breast cancer cell
lines (A) MCF-7, (B) MD-
MBA-231, and (C) MD-MBA-468 as detected by the Nanostring0 platform. Log2
expression fold change is
shown for single agent Rapamycin (10 nM), MS-275 (100 nM), as well as the
combination of
Rapamycin/MS-275 (10 nM/100 nM).
FIG. 37 shows a graph depicting an example application of the Sensitivity
Index for the 37-gene
signature. Here, this equation is applied to the in vitro data collected on
the Nanostring0 platform (see FIG.
26), a rule for classifying future sample was developed using 14 multiple
myeloma cell lines treated with the
combination of 10 nM rapamycin and 500 nM MS-275 for 48 hours. Cell lines were
considered sensitive to
the combination treatment if at least 50% decrease in viability was observed.
The midpoint between the
means of the sensitivity index (SI) of the two classes was determined as the
threshold value (SI=1.91) for
classification of a new sample based on expression changes in the 37 genes due
to the combination
treatment. To estimate the prediction error leave-one-out cross-validation
procedure (Simon et al., J. Nat.
Cancer Inst., 95:14-18, 2003) was used and 86% of the cell lines were
classified correctly.
DETAILED DESCRIPTION
I. Abbreviations
ATAD2 ATPase family, AAA domain containing 2
BLM Bloom syndrome, RecQ helicase-like
C9orf140 Chromosome 9 open reading frame 140
CCNB2 Cyclin B2
CDC20 Cell division cycle 20 hom*olog (S. cerevisiae)
CDC25A Cell division cycle 25 hom*olog A (S. pombe)
CDC6 Cell division cycle 6 hom*olog (S. cerevisiae)
CDCA3 Cell division cycle associated 3
CDCA5 Cell division cycle associated 5
cDNA Complementary deoxyribonucleic acid
E2F2 E2F transcription factor 2
EST Expressed sequence tag
GSEA Gene Set Enrichment Analysis
HDAC Histone deacetylase
HDACi Histone deacetylase inhibitor
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HJURP Holliday junction recognition protein
HLA-DPB1 Major histocompatibility complex, class II, DP beta 1
KIF22 Kinesin family member 22
KIF2C Kinesin family member 2C
LDHA Lactate dehydrogenase A
MCL Mantle cell lymphoma
MCM2 Minichromosome maintenance complex component 2
MCM4 Minichromosome maintenance complex component 4
MCM5 Minichromosome maintenance complex component 5
MGUS monoclonal gammopathy of undetermined significance
mTOR Mechanistic Target of Rapamycin
mTORi Mechanistic Target of Rapamycin inhibitor
MYBL2 V-myb myeloblastosis viral oncogene hom*olog (avian)-like
2
NCAPH Non-SMC condensin I complex, subunit H
NSDHL NAD(P) dependent steroid dehydrogenase-like
PCT Plasmacytoma
PHC3 Polyhomeotic hom*olog 3 (Drosophila)
PHF19 PHD finger protein 19
PBMC Peripheral blood mononuclear cell
RAD51 RAD51 hom*olog (RecA hom*olog, E. coli) (S. cerevisiae)
RRM2 Ribonucleotide reductase M2
SLC19A1 Solute carrier family 19 (folate transporter), member 1
SMM Smoldering myeloma
SPAG5 Sperm associated antigen 5
STK6 Aurora kinase A
SUV39H1 Suppressor of variegation 3-9 hom*olog 1 (Drosophila)
TACC3 Transforming, acidic coiled-coil containing protein 3
TMEM48 Transmembrane protein 48
TRIP13 Thyroid hormone receptor interactor 13
UBE2C Ubiquitin-conjugating enzyme E2C
ZNF107 Zinc finger protein 107
II. Terms
Unless otherwise noted, technical terms are used according to conventional
usage. Definitions of
common terms in molecular biology may be found in Benjamin Lewin, Genes V,
published by Oxford
University Press, 1994 (ISBN 0-19-854287-9); Kendrew et al. (eds.), The
Encyclopedia of Molecular
Biology, published by Blackwell Science Ltd., 1994 (ISBN 0-632-02182-9); and
Robert A. Meyers (ed.),
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Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published
by VCH Publishers,
Inc., 1995 (ISBN 1-56081-569-8).
Unless otherwise explained, all technical and scientific terms used herein
have the same meaning as
commonly understood by one of ordinary skill in the art to which the
embodiments belong. The word "or" is
intended to include "and" unless the context clearly indicates otherwise.
Hence "comprising A or B" means
including A, or B, or A and B. It is further to be understood that all base
sizes or amino acid sizes, and all
molecular weight or molecular mass values, given for nucleic acids or
polypeptides are approximate, and are
provided for description. Although methods and materials similar or equivalent
to those described herein can
be used in the practice or testing of the present embodiments, suitable
methods and materials are described
below. All publications, patent applications, patents, and other references
mentioned herein are incorporated
by reference in their entirety. The nucleic acid and/or protein sequences
corresponding to all GenBank
Accession Nos. mentioned herein are incorporated by reference in their
entirety as present in GenBank on
October 21, 2011. In case of conflict, the present specification, including
explanations of terms, will control.
In addition, the materials, methods, and examples are illustrative only and
not intended to be limiting.
In order to facilitate review of the various embodiments, the following
explanations of specific terms
are provided:
Antibody: A polypeptide ligand comprising at least a light chain or heavy
chain immunoglobulin
variable region which specifically recognizes and binds an epitope of an
antigen, such as one of the proteins
disclosed herein or a fragment thereof. Antibodies are composed of a heavy and
a light chain, each of which
has a variable region, termed the variable heavy (VH) region and the variable
light (VL) region. Together,
the VH region and the VL region are responsible for binding the antigen
recognized by the antibody. This
includes intact immunoglobulins and the variants and portions of them well
known in the art, such as Fab'
fragments, F(ab)'2 fragments, single chain Fv proteins ("scFv"), and disulfide
stabilized Fv proteins
("dsFv"). The term also includes recombinant forms such as chimeric antibodies
(for example, humanized
murine antibodies), heteroconjugate antibodies (such as, bispecific
antibodies). See also, Pierce Catalog and
Handbook, 1994-1995 (Pierce Chemical Co., Rockford, IL); Kuby, Immunology, 3rd
Ed., W.H. Freeman &
Co., New York, 1997.
Array: An arrangement of molecules, such as biological macromolecules (such as
peptides or
nucleic acid molecules) or biological samples (such as tissue sections), in
addressable locations on or in a
substrate. A "microarray" is an array that is miniaturized so as to require or
be aided by microscopic
examination for evaluation or analysis. Arrays are sometimes called chips or
biochips.
The array of molecules ("features") makes it possible to carry out a very
large number of analyses
on a sample at one time. In certain example arrays, one or more molecules
(such as an oligonucleotide
probe) will occur on the array a plurality of times (such as twice), for
instance to provide internal controls.
The number of addressable locations on the array can vary, for example from at
least one, to at least 3, at
least 10, at least 20, at least 30, at least 50, at least 75, at least 100, at
least 150, at least 200, at least 300, at
least 500, least 550, at least 600, at least 800, at least 1000, at least
10,000, or more. In some examples,
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arrays include positive and/or negative controls, such as housekeeping
markers. In particular examples, an
array includes nucleic acid molecules, such as oligonucleotide sequences that
are at least 15 nucleotides in
length, such as about 15-40 nucleotides in length.
Breast cancer: A neoplasm of breast tissue that is or has potential to be
malignant. The most
common type of breast cancer is breast carcinoma, such as ductal carcinoma.
Ductal carcinoma in situ is a
non-invasive neoplastic condition of the ducts. Lobular carcinoma is not an
invasive disease but is an
indicator that a carcinoma may develop. Infiltrating (malignant) carcinoma of
the breast can be divided into
stages (I, IIA, IIB, IIIA, IIIB, and IV). See, for example, Bonadonna et al.,
(eds), Textbook of Breast
Cancer: A clinical Guide the Therapy, 311; London, Taylor & Francis, 2006.
Chemotherapeutic agent: Any chemical agent with therapeutic usefulness in the
treatment of
diseases characterized by abnormal cell growth. Such diseases include
neoplasms (e.g., tumors) and cancer.
For example, chemotherapeutic agents are useful for the treatment of cancer,
including breast cancer and
multiple myeloma. In one embodiment, a chemotherapeutic agent is an inhibitor
of HDAC or mTOR
activity, such as MS-275 or Rapamycin, respectively. One of skill in the art
can readily identify a
chemotherapeutic agent of use (see for example, Slapak and Kufe, Principles of
Cancer Therapy, Chapter 86
in Harrison's Principles of Internal Medicine, 14th edition; Perry et al.,
Chemotherapy, Ch. 17 in Abeloff,
Clinical Oncology 2nd ed., 0 2000 Churchill Livingstone, Inc; Baltzer, L.,
Berkery, R. (eds): Oncology
Pocket Guide to Chemotherapy, 2nd ed. St. Louis, Mosby-Year Book, 1995;
Fischer, D.S., Knobf, M.F.,
Durivage, H.J. (eds): The Cancer Chemotherapy Handbook, 4th ed. St. Louis,
Mosby-Year Book, 1993;
Chabner and Longo, Cancer Chemotherapy and Biotherapy: Principles and Practice
(4th ed.). Philadelphia:
Lippincott Williams & Wilkins, 2005; Skeel, Handbook of Cancer Chemotherapy
(6th ed.). Lippincott
Williams & Wilkins, 2003). Combination chemotherapy is the administration of
more than one agent to treat
cancer (e.g., a combination of HDACi and mTORi for treatment of multiple
myeloma).
Exemplary chemotherapeutic agents include microtubule binding agents, DNA
intercalators or
cross-linkers, DNA synthesis inhibitors, DNA and/or RNA transcription
inhibitors, antibodies, kinase
inhibitors, and gene regulators.
Control: A sample or standard used for comparison with an experimental sample.
In some
embodiments, the control is a sample obtained from a healthy patient or a non-
neoplasm tissue sample
obtained from a patient diagnosed with cancer. In other embodiments, the
control is a neoplasm tissue
sample obtained from a patient diagnosed with cancer. In some embodiments, the
control is a neoplasm
tissue sample obtained from a patient diagnosed with cancer, where the patient
has not received
mTORi/HDACi combination therapy for the neoplasm. In still other embodiments,
the control is a historical
control or standard reference value or range of values (such as a previously
tested control sample, such as a
group of cancer patients with known prognosis or outcome, or group of samples
that represent baseline or
normal values, such as the expression level of one or more genes listed in
Table 6 or Table 7 in non-
neoplasm tissue).
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Contacting: Placement in direct physical association, for example solid,
liquid or gaseous forms.
Contacting includes, for example, direct physical association of fully- and
partially-solvated molecules.
Decrease or Reduce: To reduce the quality, amount, or strength of something;
for example a
reduction in tumor burden. In one example, a therapy reduces a neoplasm (such
as the size of a neoplasm,
the number of neoplasms, the metastasis of a neoplasm, or combinations
thereof), or one or more symptoms
associated with a neoplasm, for example, as compared to the response in the
absence of the therapy. In a
particular example, a therapy decreases the size of a neoplasm, the number of
neoplasms, the metastasis of a
neoplasm, or combinations thereof, subsequent to the therapy, such as a
decrease of at least 10%, at least
20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at
least 80%, or at least 90%. Such
decreases can be measured using, e.g., the methods disclosed herein.
Detecting: To identify the existence, presence, or fact of something. General
methods of detecting
are known to the skilled artisan and may be supplemented with the protocols
and reagents disclosed herein.
For example, included herein are methods of detecting gene expression in a
sample or a subject.
Determining or detecting the level of expression of a gene product: Detection
of a level of
expression in either a qualitative or quantitative manner, for example by
detecting nucleic acid molecules or
proteins, for instance using routine methods known in the art.
Diagnosis: The process of identifying a disease by its signs, symptoms and
results of various tests.
The conclusion reached through that process is also called "a diagnosis."
Forms of testing commonly
performed include blood tests, medical imaging, urinalysis, and biopsy.
Differential expression: A difference, such as an increase or decrease, in the
amount of messenger
RNA, the conversion of mRNA to a protein, or both. In some examples, the
difference is relative to a control
or reference value, such as an amount of gene expression in tissue not
affected by a disease, such as from
sample isolated from a cell or tissue that is not neoplastic or from a
different subject who does not have a
neoplasm. Alternatively, the difference may be relative to another time point,
to a treated (or untreated)
sample, or any other variable selected. Detecting a differential level of
expression can include measuring a
difference in gene or protein expression, such as a difference in level of
expression of one or more genes or
proteins, such as the genes listed in Table 6 or Table 7 or proteins encoded
thereby.
Gene expression: The process by which the coded information of a gene is
converted into an
operational, non-operational, or structural part of a cell, such as the
synthesis of a protein. Gene expression
can be influenced by external signals. For instance, exposure of a cell to a
hormone may stimulate
expression of a hormone-induced gene. Different types of cells can respond
differently to an identical signal.
Expression of a gene also can be regulated anywhere in the pathway from DNA to
RNA to protein.
Regulation can include controls on transcription, translation, RNA transport
and processing, degradation of
intermediary molecules such as mRNA, or through activation, inactivation,
compartmentalization or
degradation of specific protein molecules after they are produced.
The expression of a nucleic acid molecule can be altered relative to a normal
(wild type) nucleic
acid molecule. Alterations in gene expression, such as differential
expression, include but are not limited to:
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(1) overexpression; (2) underexpression; or (3) suppression of expression.
Alternations in the expression of a
nucleic acid molecule can be associated with, and in fact cause, a change in
expression of the corresponding
protein.
Specific examples of ovarian endothelial cell tumor-associated molecules that
are up-regulated in
ovarian tumor endothelial cells are provided in Tables 2 and 4. Specific
examples of ovarian endothelial cell
tumor-associated molecules that are down-regulated in ovarian tumor
endothelial cells are listed in Table 3.
For example, EZH2, EGFL6, TNFAIP6, TWIST1, STC1, HOP, CSPG2, and PLXDC1 are
upregulated or
increased in expression in ovarian tumor endothelial cells, while TLOC1 and
H565T2 are downregulated or
decreased in expression in such cells.
Controls or standards for comparison to a sample, for the determination of
differential expression,
include samples believed to be normal (in that they are not altered for the
desired characteristic, for example
a sample from a subject who does not have cancer, such as ovarian cancer) as
well as laboratory values, even
though possibly arbitrarily set, keeping in mind that such values can vary
from laboratory to laboratory.
Laboratory standards and values may be set based on a known or determined
population value and
can be supplied in the format of a graph or table that permits comparison of
measured, experimentally
determined values.
Gene expression signature: A gene expression signature includes a distinct or
identifiable pattern
of levels of gene expression, for instance a pattern of high and low levels of
expression of a defined set of
genes or gene-indicative nucleic acids such as ESTs or cDNAs or the protein
encoded by a gene. In some
examples, as few as three genes provides a signature, but more genes can be
used in a signature, for
example, at least five, at least six, at least ten, at least twelve, at least
twenty, at least twenty-five, at least
thirty, at least thirty-five, at least thirty-seven, or at least forty or
more. A gene expression signature can be
linked to a tissue or cell type (such as a neoplasm cell), to a particular
stage of normal tissue growth or
disease progression (such as advanced cancer), metastatic potential,
responsiveness to a therapy, or to any
other distinct or identifiable condition that influences gene expression in a
predictable way. Gene expression
signatures can include relative as well as absolute expression levels of
specific genes, and can be viewed in
the context of a test sample compared to a baseline or control gene expression
profile (such as a sample from
the same tissue type from a subject who does not have a neoplasm). In one
example, a gene expression
signature in a subject is read on an array (such as a nucleic acid or protein
array).
Histone Deacetylase (HDAC): A zinc hydrolase that modulates gene expression
through removal
of the acetyl group on &-N-acetyl lysine on the N-terminal tails of histones
(e.g., H2A, H2B, H3 and H4),
resulting in a closed nucleosomal structure. There are at least 18 HDACs in
humans, which have been
divided into four classes based on cellular localization and function (for
review, see, e.g., Federico ad
Bagella, J. Biomed. Biotechnol., 2011:475641, 2011; Laneand and Chabner, J.
Clin. Oncol., 27:5459-5468,
2009). Class I includes HDACs 1, 2, 3, and 8 which are all nuclear and
ubiquitously expressed. Class 11,
being able to shuttle back and forth between the nucleus and the cytoplasm and
believed to be tissue
restricted, includes HDACs 4, 5, 6, 7, 9, and 10; within this class, HDACs 6
and 10 (class fib) have two
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catalytic sites, are expressed only in the cytoplasm, and are involved in a
variety of biological processes.
Class 111 contains the structurally diverse NAD-F-dependent sirtuin family,
which does not act primarily on
histories (Blander and Guarente, Ann. Rev. Biochem., 73:417-435, 2004).
Finally, the ubiquitously expressed
HDAC11 represents Class IV, Nonhistone-molecules are also a target of HDACs
(e.g., p53, E2F, GATA-1,
YY1, RelA, Mad-Max, c-Myc, NF- KB, HIF-la, Ku70, a-tubulin, STAT3, Hsp90,
TFIIE, TFIIF, and
hormone receptors).
Histone Deacetylase Inhibitor (HDACi): An agent that reduces HDAC activity.
The agent can be
a competitive or noncompetitive HDAC inhibitor, and can interfere with
deacetylase activity by affecting the
enzymatic activity, disrupting the spatial conformation of the deacetylase, or
interfering with transcription or
translation pathways leading to production of the deacetylase. An HDACi can be
any type of agent,
including, but not limited to, chemical compounds, proteins, peptidomimetics,
and antisense molecules or
ribozymes. In several examples, the HDACi is MS-275, a HDAC inhibitor with
high affinity for HDACs 1
and 3 that is in clinical testing for both solid tumors and lymphomas (Kummar
et al., Clin Cancer Res.,
13:5411-5417, 2007; Gore et al., Clin Cancer Res., 14:4517-4525, 2008; Gojo et
al., Blood, 109:2781-2790,
2007; Hess-Stumpp, Int Biochem Cell Biol., 39:1388-1405, 2007)
Histone deacetylase inhibitor (HDACi) and mTOR inhibitor (mTORi) combination
therapy:
Treatment of a neoplasm (e.g., a multiple myeloma neoplasm) with a
therapeutically effective amount of a
combination of HDACi and mTORi. The HDACi and mTORi can be administered
simultaneously, or
sequentially.
Hybridization: To form base pairs between complementary regions of two strands
of DNA, RNA,
or between DNA and RNA, thereby forming a duplex molecule. Hybridization
conditions resulting in
particular degrees of stringency will vary depending upon the nature of the
hybridization method and the
composition and length of the hybridizing nucleic acid sequences. Generally,
the temperature of
hybridization and the ionic strength (such as the Na + concentration) of the
hybridization buffer will
determine the stringency of hybridization. Calculations regarding
hybridization conditions for attaining
particular degrees of stringency are discussed in Sambrook et al., (1989)
Molecular Cloning, second edition,
Cold Spring Harbor Laboratory, Plainview, NY (chapters 9 and 11).
Isolated: A biological component (such as a nucleic acid, peptide, protein or
protein complex, for
example an antibody) that has been substantially separated, produced apart
from, or purified away from
other biological components in the cell of the organism in which the component
naturally occurs, for
instance, other chromosomal and extrachromosomal DNA and RNA, and proteins.
Thus, isolated nucleic
acids, peptides and proteins include nucleic acids and proteins purified by
standard purification methods.
The term also embraces nucleic acids, peptides and proteins prepared by
recombinant expression in a host
cell, as well as, chemically synthesized nucleic acids. A isolated nucleic
acid, peptide or protein, for example
an antibody, can be at least 50%, at least 60%, at least 70%, at least 80%, at
least 90%, at least 95%, at least
96%, at least 97%, at least 98%, or at least 99% pure.
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Label: An agent capable of detection, for example by ELISA, spectrophotometry,
flow cytometry,
or microscopy. For example, a label can be attached to a nucleic acid molecule
or protein, thereby permitting
detection of the nucleic acid molecule or protein. Examples of labels include,
but are not limited to,
radioactive isotopes, enzyme substrates, co-factors, ligands, chemiluminescent
agents, fluorophores, haptens,
enzymes, and combinations thereof. Methods for labeling and guidance in the
choice of labels appropriate
for various purposes are discussed for example in Sambrook et al. (Molecular
Cloning: A Laboratory
Manual, Cold Spring Harbor, New York, 1989) and Ausubel et al. (In Current
Protocols in Molecular
Biology, John Wiley & Sons, New York, 1998).
Mechanistic Target of Rapamycin (mTOR): A protein kinase of the PI3K/Akt
signaling pathway
that is ubiquitously expressed within cells and is a validated target in the
treatment of certain cancer types
(see, e.g., Dancey et al., J. Nat. Rev. Gin. null., 7:209-219, 2010).
Activation of mTOR in response to
growth, nutrient and energy signals leads to an increase in protein synthesis,
which may contribute to
neoplasm development. The mTOR signaling network plays a regulatory role in
protein translation, cell
growth and proliferation, metabolism, and autophagy, and is at the interface
of both growth factor- and
nutrient-sensing pathways (Zoncu et al., Nat Rev Mol Cell Biol., 12:21-35,
2011; Laplante and Sabatini,
Curr Biol., 19:R1046-R1052, 2009; Meric-Bernstam and Gonzalez-Angulo, J Clin
Oncol., 27:2278-2287,
2009; Guertin and Sabatini, Cancer Cell., 12:9-22, 2007). A representative
GenBank Accession No, for
mTOR nucleotide sequence is NM_004958 and a representative GenBank accession
No. for mTOR protein
sequence is NP_004949, both of which are incorporated by reference as provided
in GenBank on October
21, 2011.
mTOR inhibitor (mTORi): An agent that reduces mTOR activity. The agent can be
a competitive
or noncompetitive mTOR inhibitor, and can interfere with mTOR activity by
affecting mTOR kinase
activity, disrupting the spatial conformation of the mTOR kinase, or
interfering with transcription or
translation pathways leading to production of mTOR. The mTORi can be any
agent, including, but not
limited to, chemical compounds, proteins, peptidomimetics, and antisense
molecules or ribozymes. Non-
limiting examples of mTOR inhibitors include Rapamycin (sirolimus; Wyeth),
Rapamycin derivatives
(a.k.a., "rapalogs"; e.g., temsirolimus (CCI-779; Wyeth); everolimus (RAD001;
Novartis); and
ridaforolimus (deforolimus; AP23573; Ariad Pharmaceuticals)), and small-
molecule mTOR kinase
inhibitors (e.g., AZD8055 (AstraZeneca); PKI-179 (Wyeth); PKI-587 (Wyeth);
XL765 (Exelixis); NvP-
BEZ235 (Novartis)).
Multiple myeloma (MM): A malignancy of terminally differentiated antibody
secreting B cells
with ¨20,000 new cases diagnosed yearly in the United States (Jemal et al., CA
Cancer J Clin., 60:277-300,
2010). MM is characterized by the accumulation of clonal plasma cells in the
bone marrow (BM) and
osteolytic bone lesions. The person of ordinary skill is familiar with tests
used to determine the presence and
severity of MM. For example, the Dune-Salmon staging system divides MM
patients into three stages:
Stages I, II, and III, corresponding to low, intermediate, and high cell mass,
depending upon the severity of
anemia, calcium level, kidney function, presence or absence of bone lesions,
and the quantity of abnormal
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proteins. Approximately 25 percent of people with MM have high-risk disease.
Treatment options include
chemotherapy, treatment with immune modulating medications, and Autologous
Stem Cell Transplant
(ASCT) (Attal et al., N. Engl. J. Med., 1996; 335:91-97; Barlogie et al.,
Blood, 1997; 89:789-793).
However, patients invariably relapse, and MM remains a universal fatal
disease. See, e.g., Rajkumar and
Kyle, (eds), Treatment of Multiple Myeloma and Related Disorders, 1st;
Cambridge University Press, New
York, 2006.
Neoplasm: An abnormal growth of tissue forming as a result of Neoplasia.
Neoplasia is the
abnormal proliferation of cells, whether malignant or benign, including
abnormal growth of all pre-
cancerous and cancerous cells and tissues. A tumor is a type of neoplasm; for
example, non-limiting
examples of neoplasms include solid and non-solid (e.g., hollow or liquid
filled) tumors. A neoplasm also
includes an abnormal growth of tissue associated with neoplasia of
hematological cells (e.g., a hematological
neoplasm, such as that occurring in lymphoma, leukemia, and myeloma).
The amount of a tumor or neoplasm in an individual is the "tumor burden,"
which can be measured
as the total volume, number, metastasis, or combinations thereof of neoplasm
or neoplasms (e.g., tumor or
tumors) in a subject. A tumor or neoplasm that does not metastasize is
referred to as "benign." A tumor or
neoplasm that invades the surrounding tissue and/or can metastasize is
referred to as "malignant."
Neoplasms and tumors of the same tissue type are primary neoplasms or tumors
originating in a
particular organ (such as breast). Neoplasms and tumors of the same tissue
type may be divided into
neoplasms or tumors of different sub-types. For examples, breast cancer tumors
can be divided into ductal
and lobular carcinomas, among others.
Oligonucleotide probes and primers: A probe includes an isolated nucleic acid
(usually of 100 or
fewer nucleotide residues) attached to a detectable label or reporter
molecule, which is used to detect a
complementary target nucleic acid molecule by hybridization and detection of
the label or reporter. Primers
are short nucleic acids, usually DNA oligonucleotides, of about 15 nucleotides
or more in length. Primers
may be annealed to a complementary target DNA strand by nucleic acid
hybridization to form a hybrid
between the primer and the target DNA strand, and then extended along the
target DNA strand by a DNA
polymerase enzyme. Primer pairs (one "upstream" and one "downstream") can be
used for amplification of a
nucleic acid sequence, for example by polymerase chain reaction (PCR) or other
in vitro nucleic-acid
amplification methods. One of skill in the art will appreciate that the
hybridization specificity of a particular
probe or primer increases with its length. Thus, for example, a probe or
primer comprising 20 consecutive
nucleotides will anneal to a target with a higher specificity than a
corresponding probe or primer of only 15
nucleotides. Thus, in order to obtain greater specificity, probes and primers
can be selected that comprise
about 20, 25, 30, 35, 40, 50 or more consecutive nucleotides.
Pharmaceutically acceptable carriers: The pharmaceutically acceptable carriers
provided herein
are conventional. Remington's Pharmaceutical Sciences, by E. W. Martin, Mack
Publishing Co., Easton,
PA, 15th Edition (1975), describes compositions and formulations suitable for
pharmaceutical delivery of
the fusion proteins herein disclosed.
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In general, the nature of the carrier will depend on the particular mode of
administration being
employed. For instance, parenteral formulations usually include injectable
fluids that include
pharmaceutically and physiologically acceptable fluids such as water,
physiological saline, balanced salt
solutions, aqueous dextrose, glycerol or the like as a vehicle. For solid
compositions (e.g., powder, pill,
tablet, or capsule forms), conventional non-toxic solid carriers can include,
for example, pharmaceutical
grades of mannitol, lactose, starch, or magnesium stearate. In addition to
biologically-neutral carriers,
pharmaceutical compositions to be administered can contain minor amounts of
non-toxic auxiliary
substances, such as wetting or emulsifying agents, preservatives, and pH
buffering agents and the like, for
example sodium acetate or sorbitan monolaurate.
Polypeptide: A polymer in which the monomers are amino acid residues that are
joined together
through amide bonds. When the amino acids are alpha-amino acids, either the L-
optical isomer or the D-
optical isomer can be used, the L-isomers being preferred. The terms
"polypeptide" or "protein" as used
herein are intended to encompass any amino acid sequence and include modified
sequences such as
glycoproteins. A polypeptide includes both naturally occurring proteins, as
well as those that are
recombinantly or synthetically produced.
Conservative substitutions replace one amino acid with another amino acid that
is similar in size,
hydrophobicity, etc. Variations in the cDNA sequence that result in amino acid
differences, whether
conservative or not, should be minimized in instances where it is desirable to
preserve the functional and
immunologic identity of the encoded protein. The immunologic identity of the
protein may be assessed by
determining if it is recognized by an antibody; a variant that is recognized
by such an antibody is
immunologically conserved. Any cDNA sequence variant will preferably introduce
no more than twenty,
and preferably fewer than ten amino acid substitutions into the encoded
polypeptide. Variant amino acid
sequences may, for example, be 80%, 90%, 95%, 98% or 99% identical to the
native amino acid sequence.
Prognosis: A prediction of the course of a disease, such as cancer (for
example, breast cancer or
multiple myeloma). The prediction can include determining the likelihood of a
subject to develop
aggressive, recurrent disease, to develop one or more metastases, to survive a
particular amount of time (e.g.,
determining the likelihood that a subject will survive 1, 2, 3 or 5 years), to
respond to a particular therapy
(e.g., mTORi/HDACi combination therapy), to be resistant to a particular
therapy (e.g., mTORi/HDACi
combination therapy), to develop resistance to a particular therapy (e.g.,
mTORi/HDACi combination
therapy) or combinations thereof. The prediction can also include determining
whether a subject has, or is
likely to have, a malignant or a benign neoplasm.
Rapalog: An mTOR inhibitor that is structurally and functionally related to
Rapamycin.
Sample (or biological sample): A biological specimen, for example, a
biological specimen
containing lipid, carbohydrate, DNA, RNA (including mRNA), protein, or
combinations thereof, obtained
from a subject. In several examples, a sample is composed of macromolecular
components, together or
separated, obtained from biological material. Examples include, but are not
limited to, peripheral blood,
urine, saliva, tissue biopsy (e.g., bone marrow biopsy), needle aspirate (,
surgical specimen, and autopsy
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material. In some examples, a sample includes a neoplasm sample, such as a
fresh, frozen, or fixed neoplasm
sample.
Sensitive to treatment with: A condition (e.g., a neoplasm) that is responsive
to an initial (and in
some examples subsequent) therapy or treatment. For example, a condition
(e.g., a neoplasm) that is
statistically significantly responsive to an initial (and in some examples,
subsequent) therapy or treatment. In
an example, sensitivity refers to the responsiveness of a disease or symptom
or progression thereof, such as the
growth of a cancer, to an agent (such as a therapeutic agent, for example an
HDACi or mTORi) or
combination of agents (such a combination of one or more HDACi and mTORi). For
example, an increased
(relative) sensitivity refers to a state in which a neoplasm is more
responsive to a given therapy or therapeutic
agent or treatment, as compared to a neoplasm that is not sensitive to the
treatment.
In certain examples, sensitivity or responsiveness of a cancer/neoplasm can be
assessed using any
endpoint indicating a benefit to the subject, including, without limitation:
(1) inhibition, to some extent, of
neoplasm growth, including slowing down and complete growth arrest; (2)
reduction in the number of
neoplasm cells; (3) reduction in neoplasm size or volume; (4) inhibition (such
as reduction, slowing down or
complete stopping) of neoplasm cell infiltration into adjacent peripheral
organs and/or tissues; (5) inhibition
(such as reduction, slowing down or complete stopping) of metastasis; (6)
enhancement of anti-neoplasm
immune response, which may, but does not have to, result in the regression or
rejection of the neoplasm; (7)
relief, to some extent, of one or more symptoms associated with the neoplasm;
(8) increase in the length of
survival following treatment; and/or (9) decreased mortality at a given point
of time following treatment.
In some examples, sensitivity of a cancer/neoplasm to treatment can be
assessed before treatment to
determine if the cancer/neoplasm will respond to the treatment. In further
examples, sensitivity of a
cancer/neoplasm to treatment can be assessed after treatment of the
cancer/neoplasm to determine if the
cancer/neoplasm is responding to the treatment. In some embodiments,
sensitivity of a cancer/neoplasm to
treatment can be assessed after initiation of treatment (for example, no more
than 8 hours, no more than 12
hours, no more than 1 day, no more than 2 days, no more than 3 days, no more
than 4 days, no more than 5
days, no more than 6 days, no more than 1 week, no more than 2 weeks, no more
than 3 weeks or no more
than 1 month, such as 8 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5
days, six days, 1 week, 2 weeks, 3
weeks or 1 month, following initiation of treatment), to determine if the
neoplasm is responding to the
treatment. In some such embodiments, the neoplasm has a response that includes
changes in gene expression
that can be detected before a physical response (such as reduction of tumor
burden) is detectable.
Sequence identity/similarity: The identity/similarity between two or more
nucleic acid sequences,
or two or more amino acid sequences, is expressed in terms of the identity or
similarity between the
sequences. Sequence identity can be measured in terms of percentage identity;
the higher the percentage, the
more identical the sequences are. Sequence similarity can be measured in terms
of percentage similarity
(which takes into account conservative amino acid substitutions); the higher
the percentage, the more similar
the sequences are. hom*ologs or orthologs of nucleic acid or amino acid
sequences possess a relatively high
degree of sequence identity/similarity when aligned using standard methods.
This hom*ology is more
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significant when the orthologous proteins or cDNAs are derived from species
which are more closely related
(such as human and mouse sequences), compared to species more distantly
related (such as human and C.
elegans sequences).
Methods of alignment of sequences for comparison are well known in the art.
Various programs and
alignment algorithms are described in: Smith & Waterman, Adv. Appl. Math.
2:482, 1981; Needleman &
Wunsch, J. Mol. Biol. 48:443, 1970; Pearson & Lipman, Proc. Natl. Acad. Sci.
USA 85:2444, 1988; Higgins
& Sharp, Gene, 73:237-44, 1988; Higgins & Sharp, CABIOS 5:151-3, 1989; Corpet
et al., Nuc. Acids Res.
16:10881-90, 1988; Huang et al. Computer Appls. in the Biosciences 8, 155-65,
1992; and Pearson et al.,
Meth. Mol. Bio. 24:307-31, 1994. Altschul et al., J. Mol. Biol. 215:403-10,
1990, presents a detailed
consideration of sequence alignment methods and hom*ology calculations.
The NCBI Basic Local Alignment Search Tool (BLAST) (Altschul et al., J. Mol.
Biol. 215:403-10,
1990) is available from several sources, including the National Center for
Biological Information (NCBI,
National Library of Medicine, Building 38A, Room 8N805, Bethesda, MD 20894)
and on the Internet, for
use in connection with the sequence analysis programs blastp, blastn, blastx,
tblastn and tblastx. Additional
information can be found at the NCBI web site.
BLASTN is used to compare nucleic acid sequences, while BLASTP is used to
compare amino acid
sequences. If the two compared sequences share hom*ology, then the designated
output file will present those
regions of hom*ology as aligned sequences. If the two compared sequences do not
share hom*ology, then the
designated output file will not present aligned sequences.
Once aligned, the number of matches is determined by counting the number of
positions where an
identical nucleotide or amino acid residue is presented in both sequences. The
percent sequence identity is
determined by dividing the number of matches either by the length of the
sequence set forth in the identified
sequence, or by an articulated length (such as 100 consecutive nucleotides or
amino acid residues from a
sequence set forth in an identified sequence), followed by multiplying the
resulting value by 100. For
example, a nucleic acid sequence that has 1166 matches when aligned with a
test sequence having 1154
nucleotides is 75.0 percent identical to the test sequence
(1166+1554*100=75.0). The percent sequence
identity value is rounded to the nearest tenth. For example, 75.11, 75.12,
75.13, and 75.14 are rounded down
to 75.1, while 75.15, 75.16, 75.17, 75.18, and 75.19 are rounded up to 75.2.
The length value will always be
an integer. In another example, a target sequence containing a 20-nucleotide
region that aligns with 20
consecutive nucleotides from an identified sequence as follows contains a
region that shares 75 percent
sequence identity to that identified sequence (that is, 15+20*100=75).
For comparisons of amino acid sequences of greater than about 30 amino acids,
the Blast 2
sequences function is employed using the default BLOSUM62 matrix set to
default parameters, (gap
existence cost of 11, and a per residue gap cost of 1). hom*ologs are typically
characterized by possession of
at least 70% sequence identity counted over the full-length alignment with an
amino acid sequence using the
NCBI Basic Blast 2.0, gapped blastp with databases such as the nr or swissprot
database. Queries searched
with the blastn program are filtered with DUST (Hanco*ck and Armstrong, 1994,
Comput. Appl. Biosci.
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10:67-70). Other programs use SEG. In addition, a manual alignment can be
performed. Proteins with even
greater similarity will show increasing percentage identities when assessed by
this method, such as at least
about 75%, 80%, 85%, 90%, 95%, 98%, or 99% sequence identity with the proteins
listed in Table 6 or
Table 7.
When aligning short peptides (fewer than around 30 amino acids), the alignment
is be performed
using the Blast 2 sequences function, employing the PAM30 matrix set to
default parameters (open gap 9,
extension gap 1 penalties). Proteins with even greater similarity to the
reference sequence will show
increasing percentage identities when assessed by this method, such as at
least about 60%, 70%, 75%, 80%,
85%, 90%, 95%, 98%, 99% sequence identity with the proteins listed in Table 6
or Table 7. When less than
the entire sequence is being compared for sequence identity, hom*ologs will
typically possess at least 75%
sequence identity over short windows of 10-20 amino acids, and can possess
sequence identities of at least
85%, 90%, 95% or 98% depending on their identity to the reference sequence.
Methods for determining
sequence identity over such short windows are described at the NCBI web site.
One indication that two nucleic acid molecules are closely related is that the
two molecules
hybridize to each other under stringent conditions, as described above.
Nucleic acid sequences that do not
show a high degree of identity may nevertheless encode identical or similar
(conserved) amino acid
sequences, due to the degeneracy of the genetic code. Changes in a nucleic
acid sequence can be made using
this degeneracy to produce multiple nucleic acid molecules that all encode
substantially the same protein.
Such hom*ologous nucleic acid sequences can, for example, possess at least
about 60%, 70%, 80%, 90%,
95%, 98%, or 99% sequence identity with the genes listed in Table 6 or Table 7
as determined by this
method. An alternative (and not necessarily cumulative) indication that two
nucleic acid sequences are
substantially identical is that the polypeptide which the first nucleic acid
encodes is immunologically cross
reactive with the polypeptide encoded by the second nucleic acid.
One of skill in the art will appreciate that the particular sequence identity
ranges are provided for
guidance only; it is possible that strongly significant hom*ologs could be
obtained that fall outside the ranges
provided.
Subject: Any mammal, such as humans, non-human primates, pigs, sheep, cows,
rodents and the
like. In two non-limiting examples, a subject is a human subject or a murine
subject. Thus, the term
"subject" includes both human and veterinary subjects.
Therapeutically effective amount: The amount of an agent (such as a HDACi or
mTORi) that
alone or together with one or more additional agents (for example, a HDACi and
mTORi combination),
induces a desired response, such as, for example treatment of a neoplasm in a
subject. Ideally, a
therapeutically effective amount provides a therapeutic effect without causing
a substantial cytotoxic effect
in the subject.
In one example, a desired response is to decrease the size, volume, or number
(such as metastases)
of a neoplasm in a subject. For example, the agent or agents can decrease the
size, volume, or number of
neoplasms by a desired amount, for example by at least 5%, at least 10%, at
least 15%, at least 20%, at least
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25%, at least 30%, at least 50%, at least 75%, at least 90%, or at least 95%
as compared to a response in the
absence of the agent. In another example, a desired response is to increase
the survival time or time of
progression free survival by a desired amount, for example by at least 5%, at
least 10%, at least 15%, at least
20%, at least 25%, at least 30%, at least 50%, at least 75%, at least 90%, or
at least 95% or more, as
compared to a response in the absence of the agent.
Several preparations disclosed herein are administered in therapeutically
effective amounts. A
therapeutically effective amount of a combination of HDACi and mTORi that is
administered to a human or
veterinary subject will vary depending upon a number of factors associated
with that subject, for example
the overall health of the subject. A therapeutically effective amount of a
combination of HDACi and mTORi
can be determined by varying the dosage and measuring the resulting
therapeutic response, such as the
regression of a neoplasm. Therapeutically effective amounts also can be
determined through various in vitro,
in vivo or in situ immunoassays. The disclosed agents can be administered in a
single dose, or in several
doses, as needed to obtain the desired response. However, the therapeutically
effective amount of one or
more agents can depend on the source applied, the subject being treated, the
severity and type of the
condition being treated, and the manner of administration.
Treating or Treatment: A therapeutic intervention (e.g., administration of a
therapeutically
effective amount of a combination of HDACi and mTORi) that reduces a sign or
symptom of a disease or
pathological condition related to a disease (such as a neoplasm). Treatment
can also induce remission or cure
of a condition, such as a neoplasm. In particular examples, treatment includes
preventing a neoplasm, for
example by inhibiting the full development of a neoplasm, such as preventing
development of a metastasis
or the development of a primary neoplasm. Prevention does not require a total
absence of a neoplasm.
Reducing a sign or symptom associated with a neoplasm can be evidenced, for
example, by a
delayed onset of clinical symptoms of the disease in a susceptible subject
(such as a subject having a
neoplasm which has not yet metastasized), a reduction in severity of some or
all clinical symptoms of the
disease, a slower progression of the disease (for example by prolonging the
life of a subject having
neoplasm), a reduction in the number of relapses of the disease, an
improvement in the overall health or
well-being of the subject, or by other parameters well known in the art that
are specific to the particular
neoplasm.
Tumor burden: The total volume, number, metastasis, or combinations thereof of
neoplasm or
neoplasms (e.g., tumor or tumors) in a subject.
Under conditions sufficient for: A phrase that is used to describe any
environment that permits a
desired activity. In one example the desired activity is formation of an
immune complex. In particular
examples the desired activity is treatment of a neoplasm.
III. Genes Included in One or More of the Disclosed Gene Signatures
ATPase family, AAA domain containing 2 (ATAD2): Also known as Cancer-
Associated AAA
Nuclear Coregulator (e.g., GenBank Gene ID No: 29028). Nucleic acid and amino
acid sequences for
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ATAD2 are publicly available. For example, GenBank Accession No. NM_014109
discloses an exemplary
human ATAD2 nucleic acid sequence, and GenBank Accession No. NP_054828
discloses an exemplary
human ATAD2 protein sequence, both of which are incorporated by reference as
provided in GenBank on
October 21, 2011. One of skill in the art can identify additional ATAD2
sequences and variants thereof.
Aurora kinase A (STK6): Also known as Serine/Threonine Protein Kinase 15
(STK15), hom*olog
of Mouse STK6 (STK6), Aurora-Related Kinase 1 (ARK1), Aurora/Ipll-Like Kinase
(AIK), Aurora 2 and
BTAK (e.g., GenBank Gene ID No: 6790). Nucleic acid and amino acid sequences
for STK6 are publicly
available. For example, GenBank Accession Nos. NM_198436, NM_198437,
NM_003600, NM_198433,
NM_198434 and NM_198435 disclose exemplary human STK6 nucleic acid sequences,
and GenBank
Accession No. NP_940838, NP_940839, NP_003591, NP_940835, NP_940836 and
NP_940837 disclose
exemplary human STK6 protein sequences, all of which are incorporated by
reference as provided in
GenBank on October 21, 2011. One of skill in the art can identify additional
STK6 sequences and variants
thereof.
Bloom syndrome, RecQ helicase-like (BLM): Also known as DNA Helicase, RECP-
Like, Type 2
(e.g., GenBank Gene ID No: 641). Nucleic acid and amino acid sequences for BLM
are publicly available.
For example, GenBank Accession No. NM_014109 discloses an exemplary human BLM
nucleic acid
sequence, and GenBank Accession No. NP_000048 discloses an exemplary human BLM
protein sequence,
both of which are incorporated by reference as provided in GenBank on October
21, 2011. One of skill in
the art can identify additional BLM sequences and variants thereof.
Cell division cycle 6 hom*olog (S. cerevisiae) (CDC6): Also known as Cell
Division Cycle 18 (S.
pombe), hom*olog-Like (CDC18) and Cell Cycle Controller CDC6 (e.g., GenBank
Gene ID No:
990).Nucleic acid and amino acid sequences for CDC6 are publicly available.
For example, GenBank
Accession No. NM_001254 discloses an exemplary human CDC6 nucleic acid
sequence, and GenBank
Accession No. NP_001245 discloses an exemplary human CDC6 protein sequence,
both of which are
incorporated by reference as provided in GenBank on October 21, 2011. One of
skill in the art can identify
additional CDC6 sequences and variants thereof.
Cell division cycle 20 hom*olog (S. cerevisiae) (CDC20): Also known as Cell-
Division Cycle
Protein 20 (e.g., GenBank Gene ID No: 991). Nucleic acid and amino acid
sequences for CDC20 are
publicly available. For example, GenBank Accession No. NM_001255 discloses an
exemplary human
CDC20 nucleic acid sequence, and GenBank Accession No. NP_001246 discloses an
exemplary human
CDC20 protein sequence, both of which are incorporated by reference as
provided in GenBank on October
21, 2011. One of skill in the art can identify additional CDC20 sequences and
variants thereof.
Cell division cycle 25 hom*olog A (S. pombe) (CDC25A): Known as CDC25A (e.g.,
GenBank
Gene ID No: 993). Nucleic acid and amino acid sequences for CDC25A are
publicly available. For example,
GenBank Accession Nos. NM_001789 and NM_201567 disclose exemplary human CDC25A
nucleic acid
sequences, and GenBank Accession Nos. NP_001780 and NP_963861 disclose
exemplary human CDC25A
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protein sequences, all of which are incorporated by reference as provided in
GenBank on October 21, 2011.
One of skill in the art can identify additional CDC25A sequences and variants
thereof.
Cell division cycle associated 3 (CDCA3): Also known as Trigger of Mitotic
Entry 1 (TOME 1)
(e.g., GenBank Gene ID No: 83461). Nucleic acid and amino acid sequences for
CDCA3 are publicly
available. For example, GenBank Accession No. NM_031299 discloses an exemplary
human CDCA3
nucleic acid sequence, and GenBank Accession No. NP_112589 discloses an
exemplary human CDCA3
protein sequence, both of which are incorporated by reference as provided in
GenBank on October 21, 2011.
One of skill in the art can identify additional CDCA3 sequences and variants
thereof.
Cell division cycle associated 5 (CDCA5): Also known as Sororin (e.g., GenBank
Gene ID No:
113130). Nucleic acid and amino acid sequences for CDCA5 are publicly
available. For example, GenBank
Accession No. NM_080668 discloses an exemplary human CDCA5 nucleic acid
sequence, and GenBank
Accession No. NP_542399 discloses an exemplary human CDCA5 protein sequence,
both of which are
incorporated by reference as provided in GenBank on October 21, 2011. One of
skill in the art can identify
additional CDCA5 sequences and variants thereof.
Chromosome 9 open reading frame 140 (C9orf140): Also known as p42.3 (e.g.,
GenBank Gene
ID No: 89958). Nucleic acid and amino acid sequences for C9orf140 are publicly
available. For example,
GenBank Accession No. NM_178448 discloses an exemplary human C9orf140 nucleic
acid sequence, and
GenBank Accession No. NP_848543.2 discloses an exemplary human C9orf140
protein sequence, both of
which are incorporated by reference as provided in GenBank on October 21,
2011. One of skill in the art can
identify additional C9orf140 sequences and variants thereof.
Cyclin B2 (CCNB2): Also known as G2/Mitotic-Specific Cyclin-B2 (e.g., GenBank
Gene ID No:
9133). Nucleic acid and amino acid sequences for CCNB2 are publicly available.
For example, GenBank
Accession No. NM_004701 discloses an exemplary human CCNB2 nucleic acid
sequence, and GenBank
Accession No. NP_004692 discloses an exemplary human CCNB2 protein sequence,
both of which are
incorporated by reference as provided in GenBank on October 21, 2011. One of
skill in the art can identify
additional CCNB2 sequences and variants thereof.
E2F transcription factor 2 (E2F2): Known as E2F2 (e.g., GenBank Gene ID No:
1870). Nucleic
acid and amino acid sequences for E2F2 are publicly available. For example,
GenBank Accession No.
NM_004091 discloses an exemplary human E2F2 nucleic acid sequence, and GenBank
Accession No.
NP_004082 discloses an exemplary human E2F2 protein sequence, both of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional E2F2
sequences and variants thereof.
Holliday junction recognition protein (HJURP): Also known as FAKTS (e.g.,
GenBank Gene ID
No: 55355). Nucleic acid and amino acid sequences for HJURP are publicly
available. For example,
GenBank Accession No. NM_018410 discloses an exemplary human HJURP nucleic
acid sequence, and
GenBank Accession No. NP_060880 discloses an exemplary human HJURP protein
sequence, both of
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which are incorporated by reference as provided in GenBank on October 21,
2011. One of skill in the art can
identify additional HJURP sequences and variants thereof.
Hs.193784: Nucleic acid sequences for Hs.193784 are publicly available. For
example, GenBank
Accession No. BF476076 discloses an exemplary human Hs.193784 nucleic acid
sequence which is
Hs.202577: Nucleic acid sequences for Hs.202577 are publicly available. For
example, GenBank
Accession No. AU144961 discloses an exemplary human Hs.202577 nucleic acid
sequence, which is
incorporated by reference as provided in GenBank on October 21, 2011. One of
skill in the art can identify
KIAA2013: Also known as MGC33867 (e.g., GenBank Gene ID No: 90231). Nucleic
acid and
amino acid sequences for KIAA2013 are publicly available. For example, GenBank
Accession No.
NM_138346 discloses an exemplary human KIAA2013 nucleic acid sequence, and
GenBank Accession No.
NP_612355 discloses an exemplary human KIAA2013 protein sequence, both of
which are incorporated by
Kinesin family member 22 (KIF22): Also known as Kinesin-Like 4 (KNSL4),
Kinesin-Like DNA-
Binding Protein (KID); Origin Of Plasmid DNA Replication-Binding Protein (OBP)
and Orip-Binding
Protein (e.g., GenBank Gene ID No: 3835). Nucleic acid and amino acid
sequences for KIF22 are publicly
Kinesin family member 2C (KIF2C): Also known as Kinesin-Like 6 (KNSL6) and
Mitotic
25 Centromere-Associated Kinesin (MCAK) (e.g., GenBank Gene ID No: 11004).
Nucleic acid and amino acid
sequences for KIF2C are publicly available. For example, GenBank Accession No.
NM_006845 discloses an
exemplary human KIF2C nucleic acid sequence, and GenBank Accession No.
NP_006836 discloses an
exemplary human KIF2C protein sequence, both of which are incorporated by
reference as provided in
GenBank on October 21, 2011. One of skill in the art can identify additional
KIF2C sequences and variants
30 thereof.
Lactate dehydrogenase A (LDHA): Also known as LDH, Subunit M (e.g., GenBank
Gene ID No:
3939). Nucleic acid and amino acid sequences for LDHA are publicly available.
For example, GenBank
Accession Nos. NM_001165416, NM_001165415, NM_001165414, NM_005566,
NM_001135239 and
NR_028500 disclose exemplary human LDHA nucleic acid sequences, and GenBank
Accession Nos.
35 NP_001158888, NP_001158887, NP_001158886, NP_005557 and NP_001128711
disclose exemplary
human LDHA protein sequences, all of which are incorporated by reference as
provided in GenBank on
October 21, 2011. One of skill in the art can identify additional LDHA
sequences and variants thereof.
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Major histocompatibility complex, class II, DP beta 1 (HLA-DPB1): Also known
as HLA-DP
Histocompatibility Type, Beta-1 Subunit (e.g., GenBank Gene ID No: 3115).
Nucleic acid and amino acid
sequences for HLA-DPB1 are publicly available. For example, GenBank Accession
No. NM_002121
discloses an exemplary human HLA-DPB1 nucleic acid sequence, and GenBank
Accession No. NP_002112
discloses an exemplary human HLA-DPB1 protein sequence, both of which are
incorporated by reference as
provided in GenBank on October 21, 2011. One of skill in the art can identify
additional HLA-DPB1
sequences and variants thereof.
Minichromosome maintenance complex component 2 (MCM2): Also known as Mitotin,
Cell
Division Cycle-Like 1 (CDCL1) and Nuclear Protein BM28 (BM28) (e.g., GenBank
Gene ID No: 4171).
Nucleic acid and amino acid sequences for MCM2 are publicly available. For
example, GenBank Accession
No. NM_004526 discloses an exemplary human MCM2 nucleic acid sequence, and
GenBank Accession No.
NP_004517 discloses an exemplary human MCM2 protein sequence, both of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional MCM2
sequences and variants thereof.
Minichromosome maintenance complex component 4 (MCM4): Also known as hom*olog
of cell
division cycle 21 (S. pombe) (e.g., GenBank Gene ID No: 4173). Nucleic acid
and amino acid sequences for
MCM4 are publicly available. For example, GenBank Accession Nos. NM_005914 and
NM_182746
disclose exemplary human MCM4 nucleic acid sequences, and GenBank Accession
Nos. NP_005905 and
NP_877423 disclose exemplary human MCM4 protein sequences, all of which are
incorporated by reference
as provided in GenBank on October 21, 2011. One of skill in the art can
identify additional MCM4
sequences and variants thereof.
Minichromosome maintenance complex component 5 (MCM5): Also known as cell
division
cycle 46 (CDC46) (e.g., GenBank Gene ID No: 4174). Nucleic acid and amino acid
sequences for MCM5
are publicly available. For example, GenBank Accession No. NM_006739 discloses
an exemplary human
MCM5 nucleic acid sequence, and GenBank Accession No. NP_006730 discloses an
exemplary human
MCM5 protein sequence, both of which are incorporated by reference as provided
in GenBank on October
21, 2011. One of skill in the art can identify additional MCM5 sequences and
variants thereof.
NAD(P) dependent steroid dehydrogenase-like (NSDHL): Also known as H105E3
(e.g.,
GenBank Gene ID No: 50814). Nucleic acid and amino acid sequences for NSDHL
are publicly available.
For example, GenBank Accession Nos. NM_015922 and NM_001129765 disclose
exemplary human
NSDHL nucleic acid sequences, and GenBank Accession Nos. NP_057006 and
NP_001123237 disclose
exemplary human NSDHL protein sequences, all of which are incorporated by
reference as provided in
GenBank on October 21, 2011. One of skill in the art can identify additional
NSDHL sequences and variants
thereof.
Non-SMC condensin I complex, subunit H (NCAPH): Also known as condensin I
complex, non-
SMC subunit H, chromosome-associated protein H (CAPH) (e.g., GenBank Gene ID
No: 23397). Nucleic
acid and amino acid sequences for NCAPH are publicly available. For example,
GenBank Accession No.
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NM_015341 discloses an exemplary human NCAPH nucleic acid sequence, and
GenBank Accession No.
NP_056156 discloses an exemplary human NCAPH protein sequence, both of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional
NCAPH sequences and variants thereof.
PHD finger protein 19 (PHF19): Also known as Growth Arrest- and DNA Damage-
Inducible
Gene GADD45, Beta (GADD45B) (e.g., GenBank Gene ID No: 4616). Nucleic acid and
amino acid
sequences for PHF19 are publicly available. For example, GenBank Accession No.
NM_015675 discloses
an exemplary human PHF19 nucleic acid sequence, and GenBank Accession No.
NP_056490 discloses an
exemplary human PHF19 protein sequence, both of which are incorporated by
reference as provided in
GenBank on October 21, 2011. One of skill in the art can identify additional
PHF19 sequences and variants
thereof.
Polyhomeotic hom*olog 3 (Drosophila) (PHC3): Also known as Early development
regulatory
protein 3 (e.g., GenBank Gene ID No: 80012). Nucleic acid and amino acid
sequences for PHC3 are
publicly available. For example, GenBank Accession No. NM_024947 discloses an
exemplary human PHC3
nucleic acid sequence, and GenBank Accession No. NP_079223 discloses an
exemplary human PHC3
protein sequence, both of which are incorporated by reference as provided in
GenBank on October 21, 2011.
One of skill in the art can identify additional PHC3 sequences and variants
thereof.
RAD51 hom*olog (RecA hom*olog, E. coli) (S. cerevisiae) (RAD51): Also known as
hom*olog of
RADS lA (S. cerevisiae) (RAD51A), Recombination Protein A (RECA) and hom*olog
of RECA, (E. COLI)
(e.g., GenBank Gene ID No: 5888). Nucleic acid and amino acid sequences for
RAD51 are publicly
available. For example, GenBank Accession Nos. NM_002875, NM_001164269,
NM_133487 and
NM_001164270 disclose exemplary human RAD51 nucleic acid sequences, and
GenBank Accession Nos.
NP_002866, NP_001157741, NP_597994 and NP_001157742 disclose exemplary human
RAD51 protein
sequences, all of which are incorporated by reference as provided in GenBank
on October 21, 2011. One of
skill in the art can identify additional RAD51 sequences and variants thereof.
Ribonucleotide reductase M2 (RRM2): Also known as Ribonucleotide Reductase,
Small Subunit;
Ribonucleotide Reductase, R2 Subunit (R2) (e.g., GenBank Gene ID No: 6241).
Nucleic acid and amino acid sequences for RRM2 are publicly available. For
example, GenBank Accession
Nos. NM_001165931 and NM_001034 disclose exemplary human RRM2 nucleic acid
sequences, and
GenBank Accession Nos. NP_001159403 and NP_001025 disclose exemplary human
RRM2 protein
sequences, all of which are incorporated by reference as provided in GenBank
on October 21, 2011. One of
skill in the art can identify additional RRM2 sequences and variants thereof.
Solute carrier family 19 (folate transporter), member 1 (SLC19A1): Also known
as Folate
Transporter (FOLT); Reduced Folate Carrier 1 (RFC1); Intestinal Folate Carrier
1 (IFC1) (e.g., GenBank
Gene ID No: 6573). Nucleic acid and amino acid sequences for SLC19A1 are
publicly available. For
example, GenBank Accession Nos. NM_001205207, NM_194255 and NM_001205206
disclose exemplary
human SLC19A1 nucleic acid sequences, and GenBank Accession Nos. NP_001192136,
NP_919231 and
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NP_001192135 disclose exemplary human SLC19A1 protein sequences, all of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional
SLC19A1 sequences and variants thereof.
Sperm associated antigen 5 (SPAG5): Also known as Astrin (e.g., GenBank Gene
ID No: 10615).
Nucleic acid and amino acid sequences for SPAG5 are publicly available. For
example, GenBank Accession
No. NM_006461 discloses an exemplary human SPAG5 nucleic acid sequence, and
GenBank Accession No.
NP_006452discloses an exemplary human SPAG5 protein sequence, both of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional
SPAG5 sequences and variants thereof.
Suppressor of variegation 3-9 hom*olog 1 (Drosophila) (SUV39H1): Also known as
Drosophila
SU(VAR)3-9, hom*olog 1 (e.g., GenBank Gene ID No: 6839). Nucleic acid and amino
acid sequences for
SUV39H1 are publicly available. For example, GenBank Accession No. NM_003173
discloses an
exemplary human SUV39H1 nucleic acid sequence, and GenBank Accession No.
NP_003164 discloses an
exemplary human SUV39H1 protein sequence, both of which are incorporated by
reference as provided in
GenBank on October 21, 2011. One of skill in the art can identify additional
SUV39H1 sequences and
variants thereof.
Thyroid hormone receptor interactor 13 (TRIP13): Also known as Human
Papillomavirus Type
16 El Protein-Binding Protein (16E1BP) (e.g., GenBank Gene ID No: 9319).
Nucleic acid and amino acid
sequences for TRIP13 are publicly available. For example, GenBank Accession
Nos. NM_004237 and
NM_001166260 disclose exemplary human TRIP13 nucleic acid sequences, and
GenBank Accession Nos.
NP_004228 and NP_001159732 disclose exemplary human TRIP13 protein sequences,
all of which are
incorporated by reference as provided in GenBank on October 21, 2011. One of
skill in the art can identify
additional TRIP13 sequences and variants thereof.
Transforming, acidic coiled-coil containing protein 3 (TACC3): known as TACC3
(e.g.,
GenBank Gene ID No: 10460). Nucleic acid and amino acid sequences for TACC3
are publicly available.
For example, GenBank Accession No. NM_006342 discloses an exemplary human
TACC3 nucleic acid
sequence, and GenBank Accession No. NP_006333 discloses an exemplary human
TACC3 protein
sequence, both of which are incorporated by reference as provided in GenBank
on October 21, 2011. One of
skill in the art can identify additional TACC3 sequences and variants thereof.
Transmembrane protein 48 (TMEM48): Also known as hom*olog of S. cerevisiae NDC1
(NDC1)
(e.g., GenBank Gene ID No: 55706). Nucleic acid and amino acid sequences for
TMEM48 are publicly
available. For example, GenBank Accession Nos. NM_018087, NM_001168551 and
NR_033142 disclose
exemplary human TMEM48 nucleic acid sequences, and GenBank Accession Nos.
NP_060557 and
NP_001162023 disclose exemplary human TMEM48 protein sequences, all of which
are incorporated by
reference as provided in GenBank on October 21, 2011. One of skill in the art
can identify additional
TMEM48 sequences and variants thereof.
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Ubiquitin-conjugating enzyme E2C (UBE2C): Also known as Ubiquitin-Conjugating
Enzyme
UBCH10 (UBCH10) (e.g., GenBank Gene ID No: 11065). Nucleic acid and amino acid
sequences for
UBE2C are publicly available. For example, GenBank Accession Nos. NM_181800,
NM_181799,
NM_007019, NM_181801 and NM_181803 disclose exemplary human UBE2C nucleic acid
sequences, and
GenBank Accession Nos. NP_861516, NP_861515, NP_008950, NP_861517, NP_861518
and NP_861519
disclose exemplary human UBE2C protein sequences, all of which are
incorporated by reference as provided
in GenBank on October 21, 2011. One of skill in the art can identify
additional UBE2C sequences and
variants thereof.
v-myb myeloblastosis viral oncogene hom*olog (avian)-like 2 (MYBL2): Also known
as myb-
related gene BMYB (e.g., GenBank Gene ID No: 4605). Nucleic acid and amino
acid sequences for MYBL2
are publicly available. For example, GenBank Accession No. NM_002466 discloses
an exemplary human
MYBL2 nucleic acid sequence, and GenBank Accession No. NP_002457 discloses an
exemplary human
MYBL2 protein sequence, both of which are incorporated by reference as
provided in GenBank on October
21, 2011. One of skill in the art can identify additional MYBL2 sequences and
variants thereof.
Zinc finger protein 107 (ZNF107): Also known as ZFD25 and Zinc Finger Protein
588 (ZNF588)
(e.g., GenBank Gene ID No: 51427). Nucleic acid and amino acid sequences for
ZNF107 are publicly
available. For example, GenBank Accession Nos. NM_016220 and NM_001013746
disclose exemplary
human ZNF107 nucleic acid sequences, and GenBank Accession Nos. NP_057304 and
NP_001013768
disclose exemplary human ZNF107 protein sequences, all of which are
incorporated by reference as
provided in GenBank on October 21, 2011. One of skill in the art can identify
additional ZNF107 sequences
and variants thereof.
III. Overview of Several Embodiments
Methods of determining if a neoplasm (e.g., a tumor) is sensitive to treatment
with
mTORi/HDACi combination therapy, methods of treating such neoplasms, and
arrays useful for performing
these methods are disclosed herein. Further disclosed are methods of
prognosis, for example, methods of
determining if a subject with a neoplasm has a decreased relative likelihood
or time of survival.
Additionally, methods of identifying a subject with a neoplasm not needing (or
less likely to benefit from)
adjuvant chemotherapy are disclosed.
In some embodiments, a method of determining if a neoplasm is sensitive to
treatment with histone
deacetylase inhibitor (HDACi) and mechanistic Target of Rapamycin (mTOR)
inhibitor (mTORi)
combination therapy is provided. This method includes comparing the level of
expression in a neoplasm
sample from a subject of three or more (such as at least six) genes listed in
Table 6 to a control level of
expression of the same three or more genes and identifying the neoplasm as
sensitive to treatment with
HDACi and mTORi combination therapy if there is a difference in the level of
expression of the three or
more genes in the neoplasm sample as compared to the control. In some
embodiments, the methods further
include detecting the level of expression in the neoplasm sample from the
subject of the three or more (such
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as at least six) genes listed in Table 6. In some embodiments, comparing the
level of expression in the
neoplasm sample from the subject includes comparing the expression of at least
three (such as at least six, or
each of the) genes selected from the group consisting of ATAD2, BLM, C9orf140,
CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577,
KIAA2013,
KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51,
RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, ZNF107. In
some
embodiments, comparing the level of expression in the neoplasm sample from the
subject includes
comparing the expression of at least three genes selected from CDC25A, E2F2,
RRM2, RAD51, SPAG5,
and MCM4. In some embodiments, the difference in the level of expression
includes an increase in the level
of expression of one or more of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 and/or a decrease in the level of expression of one or more
of Hs.193784, Hs.202577,
HLA-DPB1, and PHC3. In some embodiments, the difference in the level of
expression includes an
increase in the level of expression of each of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,
MYBL2,
NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48,
TRIP13, UBE2C, and ZNF107 and/or a decrease in the level of expression of each
of Hs.193784,
Hs.202577, HLA-DPB1, and PHC3. In some embodiments, the difference in the
level of expression
includes an increase in expression of CDC25A, E2F2, RRM2, RAD51, SPAG5, and
MCM4. In some such
embodiments, the difference in the level of expression includes an increase in
an aggregate gene expression
value calculated from the level of expression of two or more of the genes
selected from ATAD2, BLM,
C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIAA2013,
KIF22,
KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or a
decrease in an
aggregate gene expression value calculated from the level of expression of two
or more of the genes selected
from Hs.193784, Hs.202577, HLA-DPB1, and PHC3.
In other embodiments, a method of determining prognosis of a subject with a
neoplasm is provided.
This method includes detecting the level of expression in a neoplasm sample
from a subject of three or more
(such as at least six) genes listed in Table 6, comparing the level of
expression in a neoplasm sample from a
subject of three or more genes listed in Table 6 to a control level of
expression of the same three or more
genes, and identifying the subject as having a poor prognosis if there is a
difference in the level of
expression of the three or more genes in the neoplasm sample as compared to
the control. In some
embodiments, the methods further include detecting the level of expression in
the neoplasm sample from the
subject of the three or more genes listed in Table 6. In some such
embodiments, the three or more genes
comprise at least three (or at least six or each of the) genes selected from
the group consisting of ATAD2,
BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-
DPB1,
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Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,

TRIP13, UBE2C, ZNF107. In some embodiments, comparing the level of expression
in the neoplasm
sample from the subject includes comparing the expression of at least three
(such as at least six) genes from
CDC25A, E2F2, RRM2, RAD51, SPAG5, and MCM4. In some embodiments, the
difference in the level of
expression includes an increase in expression of one or more of ATAD2, BLM,
C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,

MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,

TMEM48, TRIP13, UBE2C, and ZNF107 and/or a decrease in the level of expression
of one or more of
Hs.193784, Hs.202577, KIAA2013, HLA-DPB1, and PHC3. In some embodiments, the
difference in the
level of expression includes an increase in expression of CDC25A, E2F2, RRM2,
RAD51, SPAG5, and
MCM4. In some such embodiments, the difference in the level of expression
includes an increase in an
aggregate gene expression value calculated from the level of expression of two
or more genes selected from
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
KIF22,
KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or a
decrease in an
aggregate gene expression value calculated from the level of expression of two
or more genes selected from
Hs.193784, Hs.202577, KIAA2013, HLA-DPB1, and PHC3. In some embodiments, the
poor prognosis
includes decreased overall survival, decreased relapse-free survival,
decreased metastasis-free survival, or a
combination of two or more thereof.
In several embodiments, comparing the level of expression of a gene to a
control level of expression
include detecting the level of expression of the gene.
Some embodiments include a method of treating a subject with a neoplasm. Such
embodiments
include selecting a subject with a neoplasm determined to be sensitive to
treatment with histone deacetylase
inhibitor (HDACi) and mechanistic Target of Rapamycin (mTOR) inhibitor (mTORi)
combination therapy
according to the methods provided herein and administering a therapeutically
effective amount of HDACi
and mTORi combination therapy to the subject, wherein the HDACi and mTORi
combination therapy treats
the neoplasm in the subject. In some such embodiments, The HDACi comprises MS-
275, Panobinostat,
Vorinostat, or a combination of two or more thereof. In other embodiments, the
mTORi comprises
rapamycin, temsirolimus, ridaforolimus, everolimus or a combination of two or
more thereof. In some
embodiments, wherein the neoplasm is determined not to be sensitive to
mTORi/HDACi combination
therapy, the neoplasm is treated with an alternate therapy.
Still other embodiments include a method of identifying a subject with a
neoplasm not needing
adjuvant chemotherapy. For example, such methods include comparing detecting
the level of expression in a
sample from the neoplasm of three or more (such as at least six) genes listed
in Table 6 to a control level of
expression of the same three or more genes, wherein the neoplasm is an
estrogen receptor-positive breast
neoplasm, wherein the subject is not in need of adjuvant chemotherapy if there
is not a difference between
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the level of expression of the three or more genes in the sample from the
neoplasm as compared to the
control. In some such examples, the method further includes detecting the
level of expression in the sample
from the neoplasm of the three or more genes listed in Table 6.
In several embodiments of the provided methods, detecting the level of
expression of three or more
(such as at least six) genes includes detecting the level of expression of at
least one nucleic acid molecule.
For example, several of the provided methods include microarray analysis,
nuclease protection assay, real-
time quantitative polymerase chain reaction, or Nanostring0 assay. In other
embodiments of the provided
methods, detecting the level of expression of the three or more genes
comprises detecting the level of
expression of three or more proteins encoded by genes listed in Table 6. Such
methods can include, for
example, detecting the level of expression of the three or more proteins
comprises protein microarray
analysis. In several embodiments of the methods described herein, the control
level of expression of the
three or more genes comprises the level of expression of the three or more
genes in a control sample.
In several of the methods described herein, the neoplasm is one of the
following: multiple myeloma,
mantle cell lymphoma, Burkitt's lymphoma, breast, melanoma, sarcoma, prostate,
lung, leukemia, renal,
colon or brain neoplasm. Additional embodiments include a solid support having
arrayed thereon at least one
nucleic acid probe or antibody specific for each of three or more (such as at
least six) genes selected from
the group consisting of genes listed in Table 6 or protein encoded therefrom
and at least one probe or
antibody specific for a control. In some such embodiments, the three or more
genes include at least three (or
at least six or each of the) genes selected from the group consisting of
ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C,
ZNF107.
IV. Methods of Determining Neoplasm Sensitivity or Prognosis
Described herein is the identification of gene signatures that indicate
whether a neoplasm (such as
a multiple myeloma neoplasm) is sensitive to mTORi/HDACi combination therapy
and/or that correlate with
the prognosis of a subject with a neoplasm. In some embodiments, using a gene
signature to determine
whether a neoplasm is sensitive to mTORi/HDACi combination therapy includes
predicting whether
mTORi/HDACi combination therapy will successfully treat the neoplasm, for
example by increasing
survival of the subject with the neoplasm. In other examples, using a gene
signature to determine the
prognosis includes predicting the outcome (such as chance of survival) of the
subject with a neoplasm. In
still other embodiments, using a gene signature to determine if a neoplasm is
sensitive to mTORi/HDACi
combination therapy includes predicting the response of the neoplasm to
mTORi/HDACi therapy following
initiation of mTORi/HDACi therapy. The disclosed methods optionally include
detecting the expression
level of three or more (such as at least six) genes listed in Table 6 or Table
7 in a neoplasm sample obtained
from a subject with the neoplasm. For example, some embodiments include
detecting and/or comparing the
expression level of three or more genes (such as at least 3, at least 4, at
least 5, at least 6, at least 7, at least 8,
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at least 9, at least 10, at least 11, at least 12, at least 13, at least 14,
at least 15, at least 16, at least 17, at least
18, at least 19, at least 20, at least 21, at least 22, at least 23, at least
24, at least 25, at least 26, at least 27, at
least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at
least 34, at least 35, at least 36, or at
least 37 genes, for example, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, or 37 genes) in a neoplasm sample
obtained from a subject with the
neoplasm, wherein the genes are selected from the group consisting of ATAD2,
BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107.
In some embodiments, the methods include detecting and/or comparing the
expression level of CDC25A,
E2F2, RRM2, RAD51, SPAG5, and MCM4. In some embodiments, the methods include
detecting and/or
comparing the expression level of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C,
LDHA, MCM2,
MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 in a neoplasm sample
obtained from a
subject with the neoplasm. In further embodiments, the method includes
detecting the expression level of
three or more (such as 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20,21, 22,23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 107, 108, 109, 110,
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123 or 124) of the
genes disclosed in Table 6. In
some embodiments, the methods also include comparing the expression level of
the three or more (such as at
least six) genes in the neoplasm sample to their expression level in a control
and identifying the neoplasm as
sensitive to treatment with mTORi/HDACi combination therapy if there is a
difference in expression level
(such as an increase or a decrease in expression) of the three or more genes
in the neoplasm sample as
compared to the control.
Several embodiments include identification of a gene expression signature
including gene
expression upregulation or downregulation, or both, compared to a control, as
listed for three or more more
genes (such as six; for example, the 37 genes of the blue module) listed in
one of columns (1)-(4) of Table 6
or Table 7. For example, some embodiments include identifying a gene
expression signature as shown in
Table 6 or Table 7 for three or more genes (such as at least 3, at least 4, at
least 5, at least 6, at least 7, at
least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at
least 14, at least 15, at least 16, at least 17,
at least 18, at least 19, at least 20, at least 21, at least 22, at least 23,
at least 24, at least 25, at least 26, at
least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at
least 33, at least 34, at least 35, at least
36, or at least 37 genes, for example, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, or 37 genes) in a neoplasm
sample obtained from a subject
with the neoplasm, wherein the genes are selected from the group consisting of
ATAD2, BLM, C9orf140,
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CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107.
In some embodiments, the methods include identifying the gene expression
signature as shown in Table 6 or
Table 7 for ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5,
E2F2, HJURP,
HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4,
MCM5,
MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 in a neoplasm sample obtained from a
subject with the
neoplasm. In further embodiments, the method includes identifying a gene
expression signature as shown for
three or more (such as 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20,21, 22,23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,
104, 105, 106, 107, 108, 109, 110,
112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123 or 124) of the
genes disclosed in Table 6. In
some embodiments, the methods include comparing the gene expression signature
of the three or more (such
as at least six) genes in the neoplasm sample to the gene expression signature
of the corresponding genes in
a control. In additional embodiments, the methods include detecting the level
of gene expression in the
sample to identify the disclosed gene expression signature.
In some embodiments, a gene expression signature including gene expression
upregulation or
downregulation, or both, compared to a control, as listed for three or more
more genes (such as six; for
example, the 37 genes of the blue module) listed in column (1) of Table 6 is
used to identify a subject with a
neoplasm (such as multiple myeloma) having poor prognosis. In one embodiment,
a gene expression
signature including gene expression upregulation or downregulation, or both,
compared to a control, as listed
for three or more more genes (such as six; for example, the 37 genes of the
blue module) listed in column (3)
of Table 6 is used to identify a neoplasm (such as multiple myeloma) as
sensitive to mTORi/HDACi
combination treatment before initiation of mTORi/HDACi combination treatment.
In another embodiment, a
gene expression signature including gene expression upregulation or
downregulation, or both, compared to a
control, as listed for three or more more genes (such as six; for example, the
37 genes of the blue module)
listed in column (4) of Table 6 is used to identify a neoplasm (such as
multiple myeloma) as sensitive to
HDACi/mTORi therapy following initiation of mTORi/HDACi combination treatment
(for example, 8
hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, six days, 1 week, 2
weeks, 3 weeks or 4 weeks
following initiation of therapy).
Expression levels of the disclosed genes can be detected using any suitable
means known in the art.
For example, detection of gene expression can be accomplished by detecting
nucleic acid molecules (such as
RNA) using nucleic acid amplification methods (such as RT-PCR), array analysis
(such as microarray
analysis), ribonuclease protection assay, bead-based assays, or Nanostringa
Detection of gene expression
can also be accomplished using assays that detect the proteins encoded by the
genes, including
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immunoassays (such as ELISA, Western blot, RIA assay, or protein arrays).
Additional methods of detecting
gene expression are well known in the art, and representative examples are
described in greater detail below.
Several embodiments include comparing the expression level of one or more
genes with a control.
The control can be any suitable control against which to compare expression
level of a gene (such as three or
more of the genes disclosed in Table 6 or Table 7) in a neoplasm sample. In
some embodiments, the control
is the expression level of a gene or genes in a non-neoplasm tissue. In some
examples, the non-neoplasm
tissue is obtained from the same subject, such as non-neoplasm tissue that is
adjacent to the neoplasm. In
other examples, the non-neoplasm tissue is obtained from a healthy control
subject. In other embodiments,
the control is a reference value or ranges of values. For example, the
reference value can be derived from the
average expression values obtained from a group of healthy control subjects or
non-neoplasm tissue from a
group of cancer patients. In some examples, the control includes a level of
expression of a gene signature
(such as normalized expression or aggregate values described below) from a
control or reference dataset
(such as microarray data from one or more neoplasms or non-neoplasm tissue,
such as publicly available
datasets). In other examples, the control includes expression level of one or
more housekeeping genes
(which can include, but are not limited to beta-actin, hypoxanthine
phosphoribosyltransferase (HPRT),
glyceraldehyde-3-phosphate dehydrogenase (GAPDH), glucuronidase (GUS),
transferrin receptor (TFRC),
and/or peptidylprolyl isomerase A (PPIA)) in the neoplasm sample.
In some embodiments, the expression level of the disclosed genes (such as
three or more of the
genes listed in Table 6 or Table 7) is normalized relative to the expression
level of one or more
housekeeping genes in the same neoplasm sample. In some examples, an aggregate
value is obtained by
calculating the level of expression of each of the genes (e.g., each of the
genes in a gene expression
signature) and using a positive or negative weighting for each gene depending
on whether it is positively or
negatively regulated by a condition (e.g., mTORi/HDACi combination therapy or
survival risk score). In
some examples, normalized expression of the gene (or normalized expression of
the gene signature) or an
aggregate value is determined to be increased or decreased as compared to
median normalized expression of
the gene (or gene signature) or an aggregate value for a set of neoplasms. In
some examples, the median
normalized expression or aggregate value is obtained from publicly available
microarray datasets, such as
breast cancer or multiple myeloma microarray datasets. In one example, a
median normalized expression or
aggregate value for the gene signature is determined using the microarray
datasets utilized in Example 1,
below.
In some embodiments, a score is calculated from the normalized expression
level measurements.
The score can be utilized to provide cut off points to identify a neoplasm as
sensitive or less likely to be
sensitive to mTORi/HDACi therapy, subjects at risk (such as low, medium, or
high risk) for neoplasm
recurrence or progression and/or low, medium, or high sensitivity to a therapy
(such as mTORi/HDACi
combination therapy). In some examples, the cut-off points are determined
using training and validation
datasets. In one example, a supervised approach is utilized to establish the
cut-off that distinguishes
responders from non-responders (such as mTORi/HDACi combination therapy
responders/non-responders),
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for example by comparing gene signature expression in responders and non-
responders. In other examples,
an unsupervised approach is utilized to empirically determine a cut-off level
(for example, top 50% vs.
bottom 50% or top tercile vs. bottom tercile) that is predictive of outcome.
The cut-off determined in the
training set is tested in one or more independent validation sets. In one
example, the GSE4581 dataset is
utilized as a training dataset and/or validation dataset.
In some examples, the results of the gene expression analysis are provided to
a user (such as a
clinician or other health care worker, laboratory personnel, or patient) in a
perceivable output that provides
information about the results of the analysis. In some examples, the output
can be a paper output (for
example, a written or printed output), a display on a screen, a graphical
output (for example, a graph, chart,
or other diagram), or an audible output.
In some examples, the output is a numerical value (such as an expression level
of one or more of the
genes listed in Table 6 or Table 7, or a gene expression signature listed for
three or more of the genes listed
in Table 6 or Table 7) in the sample or a relative amount of one or more of
the disclosed genes in the sample
as compared to a control. In additional examples, the output is a graphical
representation, for example, a
graph that indicates the value (such as amount or relative amount) of one or
more of the disclosed genes in
the sample from the subject on a standard curve. In a particular example, the
output (such as a graphical
output) shows or provides a cut-off value or level that indicates that the
neoplasm is sensitive to
mTORi/HDACi combination therapy and/or the subject has a poor prognosis if the
value or level is above
the cutoff and indicates that the neoplasm is less likely to be sensitive to
mTORi/HDACi combination
therapy and/or the subject has a good prognosis if the value or level is below
the cut-off. In some examples,
the output is communicated to the user, for example by providing an output via
physical, audible, or
electronic means (for example by mail, telephone, facsimile transmission,
email, or communication to an
electronic medical record).
The output can provide quantitative information (for example, an amount of one
or more of the
disclosed genes in a sample or an amount of one or more of the disclosed genes
relative to a control sample
or control value) or can provide qualitative information (for example, a
determination of mTORi/HDACi
combination therapy sensitivity and/or a prognosis). In additional examples,
the output can provide
qualitative information regarding the relative amount of one or more of the
disclosed genes in the sample,
such as identifying presence of an increase in one or more of the disclosed
genes relative to a control, a
decrease in one or more of the disclosed genes relative to a control, or no
difference in one or more of the
disclosed genes relative to a control.
In some examples, the gene expression analysis may include determination of
other clinical
information (such as determining the amount of one or more additional cancer
biomarkers in the sample). In
some examples, the gene expression analysis includes an array, such as an
oligonucleotide or antibody array
and the output of the test includes quantitative or qualitative information
about one or more of the disclosed
genes, as well as quantitative or qualitative information about one or more
additional genes.
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A. Identification of a neoplasm sensitive to mTORi/HDACi therapy
Neoplasms that will respond to therapy (prognostic identification of
neoplasms)
In some embodiments of the disclosed methods, detecting a difference in the
level of expression of
three or more (such as at least six) genes listed in Table 6 or Table 7 in the
neoplasm sample relative to the
control can be used to determine whether a neoplasm is sensitive to
mTORi/HDACi combination therapy,
for example, before mTORi/HDACi therapy is initiated. For example, some
embodiments include detecting
a difference in the expression level of three or more (such as at least 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
35, 36 or at least 37) genes in a
neoplasm sample obtained from a subject with the neoplasm compared to a
control, wherein at least three of
the genes are selected from the group consisting of ATAD2, BLM, C9orf140,
CCNB2, CDC20, CDC25A,
CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013,
KIF22, KIF2C,
LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107. Detecting a
difference in the
expression level of these genes compared to the control indicates that the
neoplasm is sensitive to
mTORi/HDACi combination therapy. In some examples, an increase in expression
level of one or more of
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19,
RAD51,
RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107
and/or a
decrease in expression of one or more of Hs.193784, Hs.202577, HLA-DPB1, and
PHC3 in the neoplasm
sample relative to the control indicates that the neoplasm is sensitive to
mTORi/HDACi combination
therapy. In other examples, an increase in expression level of ATAD2, BLM,
C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2,
MCM4,
MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and a decrease in expression of
Hs.193784, Hs.202577,
HLA-DPB1, and PHC3 in the neoplasm sample relative to the control indicates
that the neoplasm is
sensitive to mTORi/HDACi combination therapy. In some embodiments, a
statistically significant increase
or decrease in the expression level of the three or more genes (such as an
increase or decrease of at least
about 1-fold (100%), for example, at least about 1.5-fold, about 2-fold, about
2.5-fold, about 3-fold, about 4-
fold, about 5-fold, about 7-fold or about 10-fold) indicates that the neoplasm
is sensitive to mTORi/HDACi
combination therapy.
In other examples, detection of a gene expression signature as shown for three
of more of the genes
listed in Table 6 or Table 7 as determined by normalized expression or an
aggregate value as compared to a
control indicates that the neoplasm is sensitive to mTORi/HDACi combination
therapy. In some
embodiments, detection of a gene expression signature as shown in Table 6 or
Table 7 for three or more
(such as at least 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36 or at least 37) of the ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A,
CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013,
KIF22, KIF2C,
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LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 genes as
determined by
normalized expression or an aggregate value or a Sensitivity Index (SI) score
as compared to a control
indicates that the neoplasm is sensitive to mTORi/HDACi combination therapy.
Neoplasms responding to therapy (pharmacodynamic identification of neoplasms)
In some embodiments of the disclosed methods, detecting a difference in the
level of expression of
three or more (such as at least six) genes listed in Table 6 or Table 7 in the
neoplasm sample relative to the
control can be used to determine the pharmacodynamic effect of mTORi/HDACi
therapy on the neoplasm.
In several such embodiments, detecting a difference in the level of expression
of three or more (such as at
least six) genes listed in Table 6 or Table 7 is used to determine whether a
neoplasm is responding (e.g., on a
molecular level) to mTORi/HDACi combination therapy after mTORi/HDACi therapy
is initiated (for
example, 8 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, six days, 1
week, 2 weeks, 3 weeks or 4
weeks following initiation of therapy). One non-limiting example of the
advantage of this approach to
determine whether a neoplasm (or a subject with a neoplasm) has a favorable
molecular response to the
mTORi/HDACi treatment before a physical response (such as reduction of tumor
burden) can be detected.
For example, some embodiments include detecting a difference in the expression
level of three or more
(such as at least 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36 or at least 37) genes in a neoplasm sample obtained
from a subject with the
neoplasm compared to a control (such as a control neoplasm sample obtained
from the subject before
therapy was initiated), wherein at least three of the genes are selected from
the group consisting of ATAD2,
BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-
DPB1,
Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13, UBE2C, and ZNF107. Detecting a difference in the expression level of
these genes compared to the
control indicates that the neoplasm is sensitive to mTORi/HDACi combination
therapy. In some examples, a
decrease in expression level of one or more of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A,
CDC6, CDCA3, CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4,
MCM5,
MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48, TRIP13, UBE2C, and ZNF107 and/or an increase in expression of one or
more of Hs.193784,
Hs.202577, HLA-DPB1, and PHC3 in the neoplasm sample relative to the control
indicates that the
neoplasm is responding to mTORi/HDACi combination therapy. In other examples,
an decrease in
expression level of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3,
CDCA5, E2F2,
HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107
and an increase in expression of Hs.193784, Hs.202577, HLA-DPB1, and PHC3 in
the neoplasm sample
relative to the control indicates that the neoplasm is responding to
mTORi/HDACi combination therapy. In
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some embodiments, a statistically significant increase or decrease in the
expression level of the three or
more genes (such as an increase or decrease of at least about 1-fold (100%),
for example, at least about 1.5-
fold, about 2-fold, about 2.5-fold, about 3-fold, about 4-fold, about 5-fold,
about 7-fold or about 10-fold)
indicates that the neoplasm is responding to mTORi/HDACi combination therapy.
In some embodiments, identification of a gene expression signature including
gene expression
upregulation or downregulation, or both, compared to a control, as listed for
three or more more genes (such
as at least six; for example, the 37 genes of the blue module) in column (4)
of Table 6 is used to identify a
neoplasm responding to mTORi/HDACi combination therapy. In several
embodiments, the gene expression
signature can be determined by normalized expression or an aggregate value or
SI score.
In some embodiments, a SI score is used to identify a neoplasm as sensitive to
mTORi/HDACi
therapy. For example, the SI score can be calculated as the mean of the
absolute value change in normalized
gene expression for each of the genes detected, wherein the change in gene
expression is a change in gene
expression compared to a control. For example, the control can be a set value
of gene expression, a detected
gene expression from a control sample, such as healthy tissue sample, or a
neoplasm sample that has not
been treated. In some non-limiting embodiments, a control neoplasm sample can
be obtained from a subject
before initiation of mTORi/HDACi therapy, and one or more samples can be taken
following initiation of
mTORi/HDACi therapy. In one embodiment, the SI score can be calculated
according to the following
formula:
1 n
S/ Ill 612XRm, 1092X(mTil
i=t
wherein SI is the Sensitivity Index Score, n is the number of genes analyzed,
XRAth is the normalized gene
expression measured after treatment with mTORi/HDACi therapy, and XuNT, is the
normalized gene
expression measured before treatment with mTORi/HDACi therapy. For example, in
some embodiments, a
control neoplasm sample can be obtained from a subject before initiation of
mTORi/HDACi therapy, and
one or more samples can be taken following initiation of mTORi/HDACi therapy.
In one embodiment, the
SI score can be calculated according to the following formula:
1 37
S/ =- i 612XRm, 1092X(mTil
37
i=t
wherein SI is the Sensitivity Index Score, XRAth is the normalized gene
expression measured after treatment
with mTORi/HDACi therapy, and XuNT, is the normalized gene expression measured
before treatment with
mTORi/HDACi therapy. In one example (as shown in the above formula), the
expression level of all 37
genes of the blue module listed in Table 6 (ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22,
KIF2C, LDHA,
MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1,
SPAG5,
STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107) can be measured in
the control
sample and the sample obtained from the subject following initiation of
mTORi/HDACi therapy. The SI
score can then be used to identify a neoplasm as a sensitive (or not) to the
mTORi/HDACi therapy. One non-
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limiting example of the advantages of this approach is that the SI score can
be used to define whether a
neoplasm (or a subject with a neoplasm) has a favorable molecular response to
the mTORi/HDACi
treatment. In this example, upon determination of a non-sensitive SI score of
a neoplasm after initial
mTORi/HDACi treatment, a clinician may choose to discontinue mTORi/HDACi
therapy, as the patient
would not be predicted to receive clinical benefit. The person of ordinary
skill in the art will appreciate that
the SI score indicative of a neoplasm sensitive to mTORi/HDACi treatment will
vary, for example, based on
the dosage of the treatment and particular mTORi and HDACi used.
B. Identification of an optimal dosage of mTORi for use with mTORi/HDACi
combination therapy
Some examples include identification of an optimal dosage of mTORi for use in
mTORi/HDACi
combination treatment of a subject. For example, in some embodiments the gene
expression level in the
neoplasm sample of three or more (such as at least six) genes listed in Table
6 or Table 7 is correlated with a
control to determine the optimal dosage of mTORi for use with mTORi/HDACi
combination therapy for the
subject. For example, some embodiments include correlation of the expression
level of three or more (such
as at least 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36 or at least 37) genes in a neoplasm sample obtained
from a subject with the neoplasm
with a control, wherein at least three of the genes are selected from the
group consisting of ATAD2, BLM,
C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1,
Hs.193784,
Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH,
NSDHL,
PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107. In some examples, the level of gene expression can be
determined before, during
and/or after mTORi/HDACi combination therapy to determine the optimal dosage
of HDACi during a
course of therapy (for example, to determine if the optimal dosage of HDACi
has increased or decreased
during the course of therapy).
In other examples, a gene expression signature in the neoplasm sample as shown
for three of more
of the genes listed in Table 6 or Table 7 as determined by normalized
expression or an aggregate value is
correlated with a control to identify an optimal dosage of mTORi for use in
mTORi/HDACi combination
treatment of the subject. Several embodiments include correlation of a gene
expression signature as shown in
Table 6 or Table 7 for three or more (such as at least 3,4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or at least
37) of the ATAD2, BLM,
C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1,
Hs.193784,
Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH,
NSDHL,
PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 genes as determined by normalized expression or an aggregate
value with a control to
identify an optimal dosage of mTORi for use in mTORi/HDACi combination
treatment of the subject. In
some examples, the gene expression signature can be determined before, during
and/or after mTORi/HDACi
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combination therapy to determine the optimal dosage of mTORi during a course
of therapy (for example, to
determine if the optimal dosage of mTORi has increased or decreased during the
course of therapy).
In several examples, the control includes response expression profiles of the
three or more (such as
at least six) genes from a neoplasm treated with mTORi/HDACi combination
therapy. In other examples, the
control includes expression profiles of the three or more (such as at least
six) genes from an in vitro analysis
of mTORi/HDACi combination therapy, for example from treatment of neoplasm
cells (such as a multiple
myeloma cell line) with mTORi/HDACi combination therapy. In several examples,
the gene expression
profile from the in vitro analysis is correlated with the gene expression
profile from a neoplasm sample to
identify the optimal mTORi dosage for use for mTORi/HDACi combination therapy
for the neoplasm. Such
correlation methods are known to the skilled artisan (see, e.g., examples of
such methods provided in Tanaka
et al., J. Clin. Oncol., 26:1596-1602, 2008, which is incorporated by
reference herein). Thus, in some
examples, the gene expression level of ATAD2, BLM, C9orf140, CCNB2, CDC20,
CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22,
KIF2C, LDHA,
MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1,
SPAG5,
STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 in the neoplasm is
correlated with the
gene expression level of these genes from an in vitro analysis of mTORi/HDACi
combination therapy to
determine the optimal mTORi dosage for mTORi/HDACi combination therapy for the
neoplasm. In some
examples, the gene expression level (or gene signature) (y) can be used to
determine an optimal dosage
correlation value (x) that correlates the optimal mTORi dose (z) for
combination mTORi/HDACi therapy for
the neoplasm in the following manner: y = -0.563046 + 1.025323x, wherein the
optimal dosage (z) is
correlated with the optimal dosage correlation value according to known
methods (e.g., methods provided in
Tanaka et al., J. Clin. Oncol., 26:1596-1602, 2008, which is incorporated by
reference herein)
In several examples, the HDACi includes MS-275 and the mTORi includes
Rapamycin.
C. Determining Prognosis of a Subject with a Neoplasm
In some embodiments of the disclosed methods, detecting a difference in the
level of expression of
three or more (such as at least six) genes listed in Table 6 or Table 7 in a
neoplasm sample relative to a
control (e.g., expression of the three or more genes in a control sample) is
used to determine a prognosis for
the neoplasm in a subject (such as, for example, squamous cell lung carcinoma,
cutaneous melanoma,
pleomorphic liposarcoma, colon adenoma, multiple myeloma, papillary renal cell
carcinoma, melanoma,
glioblastoma, chronic lymphocytic leukemia, invasive breast carcinoma stroma,
ovarian serous
cystadenocarcinoma, invasive breast carcinoma, glioblastoma, mantle cell
lymphoma, or a breast neoplasm
or multiple myeloma neoplasm in a subject). For example, some embodiments
include detecting a difference
in the expression level of three or more (such as at least 3, 4, 5, 6,7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or at
least 37) genes in a neoplasm sample
obtained from a subject with the neoplasm compared to a control, wherein at
least three of the genes are
selected from the group consisting of ATAD2, BLM, C9orf140, CCNB2, CDC20,
CDC25A, CDC6,
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CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22,
KIF2C, LDHA,
MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1,
SPAG5,
STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107. Detecting a
difference in the
expression level of these genes compared to the control indicates that the
neoplasm has a poor prognosis. For
some examples, an increase in expression level of three or more (such as at
least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36 or at least 37) of
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
KIF22,
KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or a
decrease in
expression level of one or more of Hs.193784, Hs.202577, KIAA2013, HLA-DPB1,
and PHC3 in the
neoplasm sample relative to a control genes indicates that the neoplasm has a
poor prognosis. In other
examples, an increase in expression level of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURPõ KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH,

NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 and a decrease in expression of Hs.193784, Hs.202577,
KIAA2013, HLA-DPB1, and
PHC3 in the neoplasm sample relative to the control indicates that the
neoplasm has a poor prognosis. In
some embodiments, a statistically significant increase or decrease in the
expression level of the three or
more genes (such as an increase or decrease of at least about 1-fold (100%),
for example, at least about 1.5-
fold, about 2-fold, about 2.5-fold, about 3-fold, about 4-fold, about 5-fold,
about 7-fold or about 10-fold)
indicates that the neoplasm has a poor prognosis.
In other examples, detection of a gene expression signature as shown for three
or more of the genes
listed in Table 6 or Table 7 as determined by normalized expression or an
aggregate value as compared to a
control indicates that the neoplasm has a poor prognosis. In some embodiments,
detection of a gene
expression signature as shown in Table 6 or Table 7 for three or more (such as
at least 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36 or at least
37) of the ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5,
E2F2, HJURP,
HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4,
MCM5,
MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 genes as determined by normalized
expression or an
aggregate value as compared to a control indicates that the neoplasm has a
poor prognosis.
In several embodiments, detection of a difference in gene expression or a gene
expression signature
that indicates that a neoplasm has a poor prognosis, further indicates that
the subject with the neoplasm has a
poor prognosis.
Poor prognosis can refer to any negative clinical outcome, such as, but not
limited to, a decrease in
likelihood of survival (such as overall survival, relapse-free survival, or
metastasis-free survival), a decrease
in the time of survival (e.g., less than 5 years, or less than one year),
presence of a malignant neoplasm, an
increase in the severity of disease, resistance to therapy (e.g., resistance
to mTORi/HDACi combination
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therapy), a decrease in response to therapy (e.g., development of resistance
to mTORi/HDACi combination
therapy), an increase in neoplasm recurrence, an increase in metastasis, or
the like. In particular examples, a
poor prognosis is a decreased chance of survival (for example, a survival time
of equal to or less than 60
months, such as 50 months, 40 months, 30 months, 20 months, 12 months, 6
months, or 3 months, or less,
from time of diagnosis or first treatment). The relative "poorness" of a
prognosis, in various examples, may
be in comparison to historical measure of other subjects with the same or
similar neoplasm or cancer, or
similar presentation of symptoms of neoplasm or cancer, for example.
In other embodiments of the disclosed methods, detecting no significant
difference in expression
level (such as no statistically significant difference) of three or more (such
as at least six) genes listed in
Table 6 or Table 7 in the neoplasm sample (such as a breast neoplasm or
multiple myeloma neoplasm
sample) relative to the control indicates that the subject has a good
prognosis. In still other examples,
detecting no statistically significant increase or decrease in expression of
the gene expression signature as
determined by normalized expression or an aggregate value as compared to a
control indicates that the
subject has a good prognosis.
In several embodiments, detection of a difference in gene expression or a gene
expression signature
that indicates that a neoplasm has a good prognosis, further indicates that
the subject with the neoplasm has
a good prognosis.
Good prognosis can refer to any positive clinical outcome, such as, but not
limited to, an increase in
likelihood of survival (such as overall survival, relapse-free survival, or
metastasis-free survival), an increase
in the time of survival (e.g., more than 5 years, more than one year, or more
than two months), absence or
reduction of a malignant neoplasm or tumor burden, a decrease in the severity
of disease, likelihood of
benefit of the subject to therapy (e.g., mTORi/HDACi combination therapy), an
increase in response to
therapy (e.g., mTORi/HDACi combination therapy), an decrease in neoplasm
recurrence, or the like. In
some examples, a good prognosis includes an increased chance of survival (for
example increased overall
survival, relapse-free survival, or metastasis-free survival). In an example,
an increased chance of survival
includes a survival time of at least 24 months from time of diagnosis, such as
24 months, 36 months, 48
months, 60 months, 72 months, 84 months, 96 months, 120 months, 150 months, or
more from time of
diagnosis or first treatment. The relative "goodness" of a prognosis, in
various examples, may be in
comparison to historical measure of other subjects with the same or similar
neoplasm or cancer, or similar
presentation of symptoms of neoplasm or cancer, for example.
In some embodiments, detection of a neoplasm with a good prognosis prior to
treatment with
mTORi/HDACi therapy can be used to identify a subject as likely to benefit
from mTORi/HDACi therapy.
In some embodiments, a prognostic index (PI) score is used to stratify
subjects likely versus unlikely to
benefit from combined mTORi/HDACi therapy. In some embodiments, the gene
expression level in a
neoplasm sample from a subject for three or more (such as at least six or
each) of the 37 genes listed in
Table 7 is determined and a corresponding PI score is calculated according to
the following formula:
PI = Liwi xi - 4.552161
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wherein w, and x, are the weight (as defined in Table 7), and logged gene
expression of the ith gene as
detected in the neoplasm sample prior to treatment. In some embodiments,
calculation of a PI score of
> -0.061194 using the above formula for a neoplasm sample from a subject
indicates that the subject is likely
to benefit from mTORi/HDACi therapy.
D. Identifying the Need of Adjuvant Chemotherapy in a Subject with an Estrogen
Receptor-positive
Breast Cancer Neoplasm
In some embodiments, determining the prognosis of a subject with a neoplasm
includes identifying a
subject with an estrogen receptor-positive breast cancer neoplasm not needing
adjuvant chemotherapy.
Methods and reagents for identifying an estrogen receptor-positive breast
neoplasm are well known to the
person of ordinary skill (see, e.g., van't Veer et al., Nature, 415:530-536,
2002; van de Vijver et al., N. Engl.
J. Med., 347:1999-2009, 2002). For example, in some embodiments of the
disclosed methods, detecting a
difference in the level of expression of three or more genes listed in Table 6
or Table 7 in an estrogen
receptor-positive breast cancer neoplasm sample from the subject relative to a
control can be used to
determine if the subject is in need of adjuvant chemotherapy. For example,
some embodiments include
detecting a difference in the expression level of three or more (such as at
least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36 or at least 37)
genes in the neoplasm sample obtained from the subject compared to a control,
wherein at least three of the
genes are selected from the group consisting of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A,
CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013,
KIF22, KIF2C,
LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2,
SLC19A1,
SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107. Detecting a
difference in the
expression level of these genes compared to the control indicates that the
subject is in need of adjuvant
chemotherapy for treatment of the estrogen receptor-positive breast cancer
neoplasm. In some embodiments,
an increase in expression level of three or more (such as at least 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36
or at least 37) of ATAD2, BLM,
C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIF22, KIF2C,
LDHA,
MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or a decrease in
expression level of one
or more of Hs.193784, Hs.202577, KIAA2013, HLA-DPB1, and PHC3 in the neoplasm
sample relative to a
control indicates that the subject is in need of adjuvant chemotherapy. In
other examples, detecting an
increase in expression level of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURPõ KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL,

PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13,
UBE2C, and
ZNF107 and a decrease in the expression level of Hs.193784, Hs.202577,
KIAA2013, HLA-DPB1, and
PHC3 in the neoplasm sample relative to the control indicates that the subject
is in need of adjuvant
chemotherapy. In some embodiments, a statistically significant increase or
decrease in the expression level
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of the three or more genes (such as an increase or decrease of at least about
1-fold, for example, at least
about 1.5-fold, about 2-fold, about 2.5-fold, about 3-fold, about 4-fold,
about 5-fold, about 7-fold or about
10-fold) indicates that the neoplasm has a poor prognosis.
In other examples, detection of a gene expression signature as shown for three
of more of the genes
listed in Table 6 or Table 7 as determined by normalized expression or an
aggregate value as compared to a
control indicates that subject is in need of adjuvant chemotherapy for the
estrogen receptor-positive breast
neoplasm. In some embodiments, detection of a gene expression signature as
shown in Table 6 or Table 7
for three or more (such as at least 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36 or at least 37) of the ATAD2,
BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107
genes as determined by normalized expression or an aggregate value as compared
to a control indicates that
the subject is in need of adjuvant chemotherapy for the estrogen receptor-
positive breast neoplasm.
In other embodiments of the disclosed methods, detecting no significant
difference in expression
level (such as no statistically significant difference) of three or more genes
listed in Table 6 or Table 7 in the
estrogen receptor-positive breast neoplasm sample relative to the control
indicates that the subject is not in
need of adjuvant chemotherapy for the estrogen receptor-positive breast
neoplasm. For example, some
embodiments include detecting no significant difference in the expression
level of three or more (such as at
least 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36 or at least 37) genes in the neoplasm sample obtained from the
subject compared to a control,
wherein at least three of the genes are selected from the group consisting of
ATAD2, BLM, C9orf140,
CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107.
If no significant difference in expression level (such as no statistically
significant difference) of the three or
more genes in the neoplasm sample relative to the control is detected, then
adjuvant chemotherapy is not
needed to treat the neoplasm. In some such embodiments no significant
difference in the expression level of
each of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2,
HJURP,
HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4,
MCM5,
MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 compared to a control indicates that
the estrogen
receptor-positive neoplasm is not in need of adjuvant chemotherapy.
In still other examples, detecting no statistically significant increase or
decrease in expression of the
gene expression signature as determined by normalized expression or an
aggregate value as compared to a
control indicates that the subject has a good prognosis.
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In other examples, detection of no significant (such as no statistically
significant) expression of a
gene expression signature as shown for three of more of the genes listed in
Table 6 or Table 7 as determined
by normalized expression or an aggregate value as compared to a control
indicates that the subject is in not
need of adjuvant chemotherapy for the estrogen receptor-positive breast
neoplasm. In some embodiments,
detection of no significant (such as no statistically significant) expression
of a gene expression signature as
shown in Table 6 or Table 7 for three or more (such as at least 3, 4, 5, 6,7,
8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36
or at least 37) of the ATAD2,
BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-
DPB1,
Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13, UBE2C, and ZNF107 genes as determined by normalized expression or an
aggregate value as
compared to a control indicates that the subject is in not need of adjuvant
chemotherapy for the estrogen
receptor-positive breast neoplasm.
E. Computer-based implementation of certain embodiments
As used herein, "a computer-based system" refers to the hardware means,
software means, and data
storage means used to analyze information of the present embodiments. In some
embodiments, the
computer-based systems include a central processing unit (CPU), input means,
output means, and data
storage means. A skilled artisan can readily appreciate that any one of the
currently available computer-
based systems are suitable for use in the present embodiments. The data
storage means may comprise any
manufacture comprising a recording of the present information as described
above, or a memory access
means that can access such a manufacture.
The analytic methods described herein can be implemented by use of computer
systems. For
example, any of the comparison steps described above may be performed by means
of software components
loaded into a computer or other information appliance or digital device. When
so enabled, the computer,
appliance or device may then perform the above-described steps to assist the
analysis of values associated
with a one or more genes (for example a value that correlates with the
expression of a particular gene in the
manner described above, or for comparing such associated values. The above
features embodied in one or
more computer programs may be performed by one or more computers running such
programs.
In some embodiments, a computer system suitable for implementation of the
disclosed analytic
methods includes internal components and is linked to external components. The
internal components of this
computer system include a processor element interconnected with main memory.
The external components
include mass storage. This mass storage can be one or more hard disks (which
are typically packaged
together with the processor and memory). Such hard disks are preferably of 1
GB or greater storage
capacity. Other external components include user interface devices, which can
be a monitor, together with
inputting device, which can be a "mouse", or other graphic input devices,
and/or a keyboard. A printing
device can also be attached to the computer. Typically, computer system is
also linked to network link,
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which can be part of an Ethernet link to other local computer systems, remote
computer systems, or wide
area communication networks, such as the Internet. This network link allows
the computer system to share
data and processing tasks with other computer systems.
Loaded into memory during operation of this system are several software
components, which are
both standard in the art and special to the instant disclosure. These software
components collectively cause
the computer system to function according to the disclosed methods. In some
embodiments, the software
components are stored on mass storage. In some embodiments, the software
components include an
operating system, which is responsible for managing the computer system and
its network interconnections.
This operating system can be, for example, of the Microsoft Windows' family,
such as Windows 7, or earlier
or later versions. The software components also include common languages and
functions conveniently
present on this system to assist programs implementing the disclosed methods.
Many high or low level
computer languages can be used to program the analytic methods. Instructions
can be interpreted during run-
time or compiled. Preferred languages include C/C++, FORTRAN, R and JAVA .
Most preferably, the
methods are programmed in mathematical software packages that allow symbolic
entry of equations and
high-level specification of processing, including algorithms to be used,
thereby freeing a user of the need to
procedurally program individual equations or algorithms. Such packages include
Matlab from Mathworks
(Natick, Mass.), Mathematica from Wolfram Research (Champaign, Ill.), and S-
Plus from Math Soft
(Cambridge, Mass.). In an exemplary implementation, to practice the methods, a
user first loads microarray
experiment data into the computer system. These data can be directly entered
by the user or from other
computer systems linked by the network connection, or on removable storage
media such as a CD-ROM,
floppy disk, tape drive, ZIP drive or through the network. Next the user
causes execution of expression
profile analysis software, which performs the disclosed methods.
In another exemplary implementation, a user first loads microarray experiment
data into the
computer system. This data is loaded into the memory from the storage media or
from a remote computer,
for example, from a dynamic geneset database system, through the network. Next
the user causes execution
of software that performs the comparison of gene expression data from a
neoplasm sample with a control (as
described herein) to detect a difference of gene expression between the
neoplasm sample and the control.
Alternative computer systems and software for implementing the analytic
methods of this will be
apparent to one of skill in the art.
Thus, any of the disclosed methods can be implemented as computer-executable
instructions stored
on one or more computer-readable storage media (e.g., non-transitory computer-
readable media, such as one
or more optical media discs, volatile memory components (such as DRAM or
SRAM), or nonvolatile
memory components (such as hard drives)) and executed on a computer (e.g., any
commercially available
computer, including smart phones or other mobile devices that include
computing hardware). Any of the
computer-executable instructions for implementing the disclosed techniques as
well as any data created and
used during implementation of the disclosed embodiments can be stored on one
or more computer-readable
media (e.g., non-transitory computer-readable media). The computer-executable
instructions can be part of,
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for example, a dedicated software application or a software application that
is accessed or downloaded via a
web browser or other software application (such as a remote computing
application). Such software can be
executed, for example, on a single local computer (e.g., any suitable
commercially available computer) or in
a network environment (e.g., via the Internet, a wide-area network, a local-
area network, a client-server
network (such as a cloud computing network), or other such network) using one
or more network computers.
For clarity, only certain selected aspects of the software-based
implementations are described. Other details
that are well known in the art are omitted. For example, it should be
understood that the disclosed
technology is not limited to any specific computer language or program. For
instance, the disclosed
technology can be implemented by software written in C++, Java, R, Perl,
JavaScript, Adobe Flash, or any
other suitable programming language. Likewise, the disclosed technology is not
limited to any particular
computer or type of hardware. Certain details of suitable computers and
hardware are well known and need
not be set forth in detail in this disclosure.
Furthermore, any of the software-based embodiments (comprising, for example,
computer-
executable instructions for causing a computer to perform any of the disclosed
methods) can be uploaded,
downloaded, or remotely accessed through a suitable communication means. Such
suitable communication
means include, for example, the Internet, the World Wide Web, an intranet,
software applications, cable
(including fiber optic cable), magnetic communications, electromagnetic
communications (including RF,
microwave, and infrared communications), electronic communications, or other
such communication means.
Any of the computer-readable media herein can be non-transitory (e.g., memory,
magnetic storage,
optical storage, or the like). Any of the storing actions described herein can
be implemented by storing in
one or more computer-readable media (e.g., computer-readable storage media or
other tangible media). Any
of the things described as stored can be stored in one or more computer-
readable media (e.g., computer-
readable storage media or other tangible media).
Any of the methods described herein can be implemented by computer-executable
instructions in
(e.g., encoded on) one or more computer-readable media (e.g., computer-
readable storage media or other
tangible media). Such instructions can cause a computer to perform the method.
The technologies described
herein can be implemented in a variety of programming languages. Any of the
methods described herein can
be implemented by computer-executable instructions stored in one or more
computer-readable storage
devices (e.g., memory, magnetic storage, optical storage, or the like). Such
instructions can cause a computer
to perform the method.
Some embodiments include a method performed by a computer system, the computer
system
including a screen, software that displays gene expression levels on the
screen, a keyboard or mouse for
interfacing with the software, and a memory that stores a list or lists of the
expression levels of genes in a
neoplasm sample. The method includes, for example, analyzing the list or lists
of the level of expression in a
neoplasm sample of three or more genes listed in Table 6 to a control level of
expression data set of the same
three or more genes; and identifying the neoplasm as sensitive to treatment
with HDACi and mTORi
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combination therapy when an increase or decrease in the level of expression of
the three or more genes in
the neoplasm sample relative to the control exceeds a predefined limit.
Additional embodiments include a method implemented at least in part by a
computer, the method
comprising receiving a gene expression dataset (e.g., a list of gene
expression levels) comprising a gene
expression level for each of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6,
CDCA3,
CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C,
LDHA, MCM2,
MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107. The gene expression level
of genes in the
dataset is compared to a control gene expression level of the same genes and a
difference in the gene
expression level of the genes in the dataset as compared to the control gene
expression level of the same
genes is calculated (for example, as described herein). In several
embodiments, the calculated difference in
the gene expression level of the genes in the dataset as compared to the
control gene expression level of the
same genes is displayed in a user interface. In additional embodiments, the
method further includes
identifying the neoplasm as sensitive to treatment with HDACi and mTORi
combination therapy if there is a
difference in the gene expression level of the genes in the dataset as
compared to the control gene expression
level of the same genes.
In other embodiments, one or more computer-readable storage devices comprising
computer-
executable instructions for performing any one or more of the methods
described herein are provided.
V. Detecting Gene Expression Level
As described below, the level of expression of genes listed in Table 6 or
Table 7 in a sample can be
detected using any one of a number of methods well known in the art. Although
exemplary methods are
provided, the disclosure is not limited to such methods. Detection of
expression level of either mRNA or
protein is contemplated herein.
The disclosure includes isolated nucleic acid molecules that include specified
lengths of nucleotide
sequences, such as the nucleotide sequences of the genes listed in Table 6 or
Table 7. Such molecules can
include at least 10, at least 15, at least 20, at least 25, at least 30, at
least 35, at least 40, at least 45, at least 50,
or more consecutive nucleotides of these sequences or more, and can be
obtained from any region of the
disclosed genes. In some examples, particular oligonucleotides and
oligonucleotide analogs can include linear
sequences up to about 200 nucleotides in length, for example a sequence (such
as DNA or RNA) that is at least
6 nucleotides, for example at least 8, at least 10, at least 15, at least 20,
at least 21, at least 25, at least 30, at
least 35, at least 40, at least 45, at least 50, at least 100, or even at
least 200 nucleotides long, or from about 6
to about 50 nucleotides, for example about 10-25 nucleotides, such as 12, 15,
or 20 nucleotides. In one
example, an oligonucleotide is a short sequence of nucleotides of at least one
of the genes disclosed in Table 6
or Table 7, for example at least one of ATAD2, BLM, C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3,
CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C,
LDHA, MCM2,
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MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107.
A. Methods for Detecting Nucleic Acids
Gene expression level can be determined by detecting mRNA encoding the gene of
interest. Thus,
the disclosed methods can include determining mRNA encoding three or more of
the genes disclosed in
Table 6 or Table 7 and described herein. In particular examples, mRNA encoding
three or more of ATAD2,
BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-
DPB1,
Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13, UBE2C, and ZNF107 is detected. In some examples, the mRNA is
quantitated.
In some examples, the disclosed genes are detected utilizing an
oligonucleotide probe. Such probes
include short sequence of nucleotides, such as at least 8, at least 10, at
least 15, at least 20, at least 21, at
least 25, or at least 30 nucleotides in length, used to detect the presence of
a complementary sequence by
molecular hybridization.
RNA can be isolated from a sample of a neoplasm (for example, a breast
neoplasm or multiple
myeloma neoplasm) from a subject, a sample of adjacent non-neoplasm tissue
from the subject, a sample of
neoplasm-free tissue from a normal (healthy) subject, or combinations thereof,
using methods well known to
one skilled in the art, including commercially available kits. General methods
for mRNA extraction are well
known in the art and are disclosed in standard textbooks of molecular biology,
including Ausubel et al.,
Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods
for RNA extraction from
paraffin embedded tissues are disclosed, for example, in Rupp and Locker,
Biotechniques 6:56-60 (1988),
and De Andres et al., Biotechniques 18:42-44 (1995). In one example, RNA
isolation can be performed
using a purification kit, buffer set and protease from commercial
manufacturers, such as QIAGEN
(Valencia, CA), according to the manufacturer's instructions. For example,
total RNA from cells (such as
those obtained from a subject) can be isolated using QIAGEN RNeasy0 mini-
columns. Other commercially
available RNA isolation kits include MASTERPUREO Complete DNA and RNA
Purification Kit
(EPICENTRE Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion,
Inc.). Total RNA from
tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from
neoplasm or other
biological sample can also be isolated, for example, by cesium chloride
density gradient centrifugation.
Methods of gene expression level profiling include methods based on
hybridization analysis of
polynucleotides, methods based on sequencing of polynucleotides, and
proteomics-based methods. In some
examples, mRNA expression level in a sample is quantified using Northern
blotting or in situ hybridization
(Parker & Barnes, Methods in Molecular Biology 106:247-283, 1999); RNAse
protection assays (Hod,
Biotechniques 13:852-4, 1992); and PCR-based methods, such as reverse
transcription polymerase chain
reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-4, 1992) or
quantitative real-time PCR.
Alternatively, antibodies can be employed that can recognize specific
duplexes, including DNA duplexes,
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RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Bead-based
multiplex assays
(such as Luminex xMAPO assay) can also be utilized. Representative methods for
sequencing-based gene
expression analysis include Serial Analysis of Gene Expression (SAGE), and
gene expression analysis by
massively parallel signature sequencing (MPSS). In one example, RT-PCR can be
used to compare mRNA
levels in different samples, for example in normal and neoplasm tissues, with
or without drug treatment, to
characterize patterns of gene expression levels, to discriminate between
closely related mRNAs, and to
analyze RNA structure.
Methods for quantitating mRNA are well known in the art. In some examples, the
method utilizes
RT-PCR. For example, extracted RNA can be reverse-transcribed using a
GeneAmp() RNA PCR kit (Perkin
Elmer, Calif., USA), following the manufacturer's instructions. In some
embodiments, gene expression
levels can be determined using a gene expression analysis technology that
measure mRNA in solution.
Examples of such gene expression analysis technologies include, but not
limited to, RNAscopeTM, RT-PCR,
Nanostring , QuantiGene0, gNPAO., microarray, and sequencing. For example,
methods of Nanostring
use labeled reporter molecules, referred to as labeled "nanoreporters," that
are capable of binding individual
target molecules. Through the nanoreporters' label codes, the binding of the
nanoreporters to target
molecules results in the identification of the target molecules. Methods of
Nanostring are described in U.S.
Pat. No. 7,473,767 (see also, Geiss, Nature Biotechnology, 26, 317-325, 2008).
For example, TaqMan() RT-PCR can be performed using commercially available
equipment. The
system can include a thermocycler, laser, charge-coupled device (CCD) camera,
and computer. The system
amplifies samples in a 96-well format on a thermocycler. During amplification,
laser-induced fluorescent
signal is collected in real-time through fiber optics cables for all 96 wells,
and detected at the CCD. The
system includes software for running the instrument and for analyzing the
data.
To minimize errors and the effect of sample-to-sample variation, RT-PCR can be
performed using
an internal standard. The ideal internal standard is expressed at a constant
level among different tissues, and
is unaffected by an experimental treatment. RNAs commonly used to normalize
patterns of gene expression
are mRNAs for the housekeeping genes GAPDH, 13-actin, and 18S ribosomal RNA.
A variation of RT-PCR is real time quantitative RT-PCR, which measures PCR
product
accumulation through a dual-labeled fluorogenic probe (e.g., TAQMANO probe).
Real time PCR is
compatible both with quantitative competitive PCR, where internal competitor
for each target sequence is
used for normalization, and with quantitative comparative PCR using a
normalization gene contained within
the sample, or a housekeeping gene for RT-PCR (see Heid et al., Genome
Research 6:986-994, 1996).
Quantitative PCR is also described in U.S. Pat. No. 5,538,848. Related probes
and quantitative amplification
procedures are described in U.S. Pat. No. 5,716,784 and U.S. Pat. No.
5,723,591. Instruments for carrying
out quantitative PCR in microtiter plates are available from PE Applied
Biosystems (Foster City, CA).
The steps of a representative protocol for quantitating gene expression level
using fixed, paraffin-
embedded tissues as the RNA source, including mRNA isolation, purification,
primer extension and
amplification are given in various published journal articles (see Godfrey et
al., J. Mol. Diag. 2:84-91, 2000;
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Specht et al., Am. J. Pathol. 158:419-29, 2001). Briefly, a representative
process starts with cutting about 10
[tin thick sections of paraffin-embedded neoplasm tissue samples or adjacent
non-cancerous tissue. The
RNA is then extracted, and protein and DNA are removed. Alternatively, RNA is
isolated directly from a
neoplasm sample or other tissue sample. After analysis of the RNA
concentration, RNA repair and/or
amplification steps can be included, if necessary, and RNA is reverse
transcribed using gene specific
promoters followed by RT-PCR.
In some embodiments, the primers used for the amplification are selected so as
to amplify a unique
segment of the gene of interest (such as mRNA encoding one of the genes listed
in Table 6 or Table 7, such
as ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2,
HJURP, HLA-
DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,
MYBL2,
NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48, TRIP13, UBE2C, and ZNF107). In some embodiments, expression levels of
other genes are also
detected (for example one or more control or housekeeping genes). Primers that
can be used to amplify one
or more of the genes listed in Table 6 or Table 7 (such as ATAD2, BLM,
C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577,
KIAA2013,
KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51,

RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107)
are
commercially available or can be designed and synthesized according to well
known methods.
An alternative quantitative nucleic acid amplification procedure is described
in U.S. Pat. No.
5,219,727. In this procedure, the amount of a target sequence in a sample is
determined by simultaneously
amplifying the target sequence and an internal standard nucleic acid segment.
The amount of amplified DNA
from each segment is determined and compared to a standard curve to determine
the amount of the target
nucleic acid segment that was present in the sample prior to amplification.
In some examples, gene expression level is identified or confirmed using
microarray techniques.
Thus, the gene expression signatures can be measured in either fresh or
paraffin-embedded neoplasm tissue,
using microarray technology. In this method, the nucleic acid sequences of
interest (including cDNAs and
oligonucleotides) are plated, or arrayed, on a microchip substrate. The
arrayed sequences are then hybridized
with isolated nucleic acids (such as cDNA or mRNA) from cells or tissues of
interest. Just as in the RT-PCR
method, the source of mRNA typically is total RNA isolated from neoplasms, and
optionally from
corresponding noncancerous tissue and normal tissues or cell lines.
In a specific embodiment of the microarray technique, PCR amplified inserts of
cDNA clones are
applied to a substrate in a dense array. In some examples, the array includes
at least one probe specific to
each of at least three of the disclosed genes (such as those in Table 6 or
Table 7). In some examples,
oligonucleotide probes specific for the nucleotide sequences of each of three
or more genes listed in Table 6
or Table 7 (such as ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3,
CDCA5, E2F2,
HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2,
MCM4, MCM5,
MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
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TACC3, TMEM48, TRIP13, UBE2C, and ZNF107) are arrayed on the substrate. The
arrayed sequences can
include, consist essentially of, or consist of these sequences. The
microarrayed nucleic acids are suitable for
hybridization under stringent conditions. Labeled cDNA probes may be
generated, for example through
incorporation of fluorescent nucleotides by reverse transcription of RNA
extracted from tissues of interest.
Labeled cDNA probes applied to the array hybridize with specificity to each
spot of DNA on the array. After
stringent washing to remove non-specifically bound probes, the array is
scanned by confocal laser
microscopy or by another detection method, such as a CCD camera. Quantitation
of hybridization of each
arrayed element allows for assessment of corresponding mRNA abundance. With
dual color fluorescence,
separately labeled cDNA probes generated from two sources of RNA are
hybridized pairwise to the array.
The relative abundance of the transcripts from the two sources corresponding
to each specified gene is thus
determined simultaneously. The miniaturized scale of the hybridization affords
a convenient and rapid
evaluation of the expression level and expression level patterns in the
neoplasm sample of the genes listed in
Table 6 or Table 7 (for example, ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C,
LDHA, MCM2,
MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107). Microarray analysis can be
performed by
commercially available equipment, following the manufacturer's protocols, such
as are supplied with
Affymetrix0 GeneChip() technology (Affymetrix, Santa Clara, CA), or Agilent's
microarray technology
(Agilent Technologies, Santa Clara, CA).
Serial analysis of gene expression (SAGE) is another method that allows the
simultaneous and
quantitative analysis of a large number of gene transcripts, without the need
of providing an individual
hybridization probe for each transcript. First, a short sequence tag (about 10-
14 base pairs) is generated that
contains sufficient information to uniquely identify a transcript, provided
that the tag is obtained from a
unique position within each transcript. Then, many transcripts are linked
together to form long serial
molecules, that can be sequenced, revealing the identity of the multiple tags
simultaneously. The expression
pattern of any population of transcripts can be quantitatively evaluated by
determining the abundance of
individual tags, and identifying the gene corresponding to each tag (see, for
example, Velculescu et al.,
Science 270:484-7, 1995; and Velculescu et al., Cell 88:243-51, 1997).
In situ hybridization (ISH) is another method for detecting and comparing
expression levels of genes
of interest. ISH applies and extrapolates the technology of nucleic acid
hybridization to the single cell level,
and, in combination with the art of cytochemistry, immunocytochemistry and
immunohistochemistry,
permits the maintenance of morphology and the identification of cellular
markers to be maintained and
identified, and allows the localization of sequences to specific cells within
populations, such as tissues and
blood samples. ISH is a type of hybridization that uses a complementary
nucleic acid to localize one or more
specific nucleic acid sequences in a portion or section of tissue (in situ),
or, if the tissue is small enough, in
the entire tissue (whole mount ISH). RNA ISH can be used to assay expression
patterns in a tissue, such as
the expression level of the disclosed genes.
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Sample cells or tissues are treated to increase their permeability to allow a
probe, such as a gene-
specific probe, to enter the cells. The probe is added to the treated cells,
allowed to hybridize at pertinent
temperature, and excess probe is washed away. A complementary probe is labeled
so that the probe's
location and quantity in the tissue can be determined, for example, using
autoradiography, fluorescence
microscopy or immunoassay. The sample may be any sample as herein described,
such as a non-neoplasm
sample or a neoplasm sample. Since the sequences of the genes of interest are
known, probes can be
designed accordingly such that the probes specifically bind the gene of
interest.
In situ PCR is the PCR-based amplification of the target nucleic acid
sequences prior to ISH. For
detection of RNA, an intracellular reverse transcription step is introduced to
generate complementary DNA
from RNA templates prior to in situ PCR. This enables detection of low copy
RNA sequences.
Prior to in situ PCR, cells or tissue samples are fixed and permeabilized to
preserve morphology and
permit access of the PCR reagents to the intracellular sequences to be
amplified. PCR amplification of target
sequences is next performed either in intact cells held in suspension or
directly in cytocentrifuge
preparations or tissue sections on glass slides. In the former approach, fixed
cells suspended in the PCR
reaction mixture are thermally cycled using conventional thermal cyclers.
After PCR, the cells are
cytocentrifuged onto glass slides with visualization of intracellular PCR
products by ISH or
immunohistochemistry. In situ PCR on glass slides is performed by overlaying
the samples with the PCR
mixture under a coverslip which is then sealed to prevent evaporation of the
reaction mixture. Thermal
cycling is achieved by placing the glass slides either directly on top of the
heating block of a conventional or
specially designed thermal cycler or by using thermal cycling ovens.
Detection of intracellular PCR products is generally achieved by one of two
different techniques,
indirect in situ PCR by ISH with PCR-product specific probes, or direct in
situ PCR without ISH through
direct detection of labeled nucleotides (such as digoxigenin-11-dUTP,
fluorescein-dUTP, 3H-CTP or biotin-
16-dUTP), which have been incorporated into the PCR products during thermal
cycling.
In some embodiments of the detection methods, the expression level of one or
more "housekeeping"
genes or "internal controls" can also be evaluated. These terms include any
constitutively or globally
expressed gene (or protein, as discussed below) whose presence enables an
assessment of gene (or protein)
levels of the disclosed gene expression signature. Such an assessment includes
a determination of the overall
constitutive level of gene transcription and a control for variations in RNA
(or protein) recovery.
For example, in some non-limiting embodiments, a high throughput method by
which to gain
information about gene expression is the nucleic acid microarray (e.g., a
gridded nucleic acid microarray), in
which a transparent support, such as a microscope slide, containing dozens to
hundreds to thousands or more
of immobilized nucleic acid samples is hybridized in a manner very similar to
the northern and Southern
blot. An ideal support allows effective immobilization of nucleic acid
sequences (i.e., probes) onto its
surface, and robust hybridization of target nucleic acid sequences with the
probe. Following hybridization
with dye-tagged nucleic acids, the array is "read" using a laser scanner to
stimulate (to fluorescence) the dye
attached to nucleic acid targets hybridized to the probes on the support. The
motorized stage executes a
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programmed comb scan pattern that sequentially traverses the array in the X
direction, and then steps a pixel
width in the Y direction, producing a bi-directional raster pattern. Part of
the dye fluorescence is captured by
the scanner objective, filtered into red and green signals that are routed to
each respective photomultiplier
tube (PMT) where they are converted to electrical signals that are amplified,
filtered and sampled by an
analog-to-digital (A/D) converter. The scanner software converts the A/D
converter output into a high-
resolution image. The pixel intensity of each spot on the image is
proportional to the number of dye
molecules and hence the number of probe nucleic acids on the array that are
hybridized with the target
nucleic acids.
B. Methods for Detecting Proteins
In some examples, the expression level in a sample of three or more proteins
encoded by the genes
disclosed in Table 6 or Table 7 is analyzed. In particular examples, the
expression level in a sample of three
or more (e.g., ten or more, 30 or more 37 or more, or all of the) proteins
encoded by ATAD2, BLM,
C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1,
Hs.193784,
Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH,
NSDHL,
PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 is analyzed. Suitable samples include biological samples
containing protein obtained
from a neoplasm (such as a breast neoplasm or multiple myeloma neoplasm) of a
subject, from non-
neoplasm tissue of the subject, and/or protein obtained from one or more
samples of cancer-free subjects.
Detecting a difference in the level of the three or more proteins encoded by
the genes in Table 6 or Table 7
(such as ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2,
HJURP,
HLA-DPB 1 , Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4,
MCM5,
MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107) in a neoplasm sample from the
subject relative to a
control, such as an increase or decrease in protein expression level,
indicates the prognosis or diagnosis of
the subject, as described above.
Antibodies specific for the proteins encoded by the genes listed in Table 6 or
Table 7 (such as
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
HLA-
DPB 1 , Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,
MYBL2,
NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48, TRIP13, UBE2C, and ZNF107 can be used for detection and quantitation
of proteins by one of a
number of immunoassay methods that are well known in the art, such as those
presented in Harlow and Lane
(Antibodies, A Laboratory Manual, CSHL, New York, 1988). Antibodies specific
for the proteins encoded
by the genes listed in Table 6 or Table 7 are commercially available or can be
generated using standard
methods known to the person of ordinary skill.
Any standard immunoassay format (such as ELISA, Western blot, or RIA assay)
can be used to
measure protein levels. Thus, in one example, the levels of three or more the
proteins encoded by the genes
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listed in Table 6 or Table 7 (such as ATAD2, BLM, C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3,
CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C,
LDHA, MCM2,
MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5,
STK6,
SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 in a sample (for example, a
multiple
myeloma or breast neoplasm sample) can readily be evaluated using these
methods. Immunohistochemical
techniques can also be utilized for gene detection and quantification, for
example using formalin-fixed,
paraffin embedded (FFPE) slides coupled with an automated slide stainer (for
example, available from
Ventana Medical Systems, Inc., Tuscon, AZ). General guidance regarding such
techniques can be found in
Bancroft and Stevens (Theory and Practice of Histological Techniques,
Churchill Livingstone, 1982) and
Ausubel et al. (Current Protocols in Molecular Biology, John Wiley & Sons, New
York, 1998).
For the purposes of quantitating the disclosed proteins, a sample that
includes cellular proteins (for
example a breast neoplasm sample or multiple myeloma neoplasm sample) can be
used. Quantitation of
proteins can be achieved by immunoassay. The level of proteins can be assessed
in the neoplasm sample and
optionally in adjacent non-neoplasm tissue sample or in a tissue sample from a
cancer-free subject. The level
of the disclosed proteins in the neoplasm sample can be compared to level of
the proteins from a sample
from a cancer-free subject or other control (such as a standard value or
reference value). A significant
increase or decrease in the amount can be evaluated using statistical methods
known in the art.
Quantitative spectroscopic methods, such as SELDI, can be used to analyze
protein expression in a
sample (such as neoplasm tissue, non-cancerous tissue, and tissue from a
cancer-free subject). In one
example, surface-enhanced laser desorption-ionization time-of-flight (SELDI-
TOF) mass spectrometry is
used to detect protein expression, for example by using the ProteinChipTM
(Ciphergen Biosystems, Palo
Alto, CA). Such methods are well known in the art (for example see U.S. Pat.
No. 5,719,060; U.S. Pat. No.
6,897,072; and U.S. Pat. No. 6,881,586). SELDI is a solid phase method for
desorption in which the analyte
is presented to the energy stream on a surface that enhances analyte capture
or desorption.
In another example, antibodies are immobilized onto the surface using a
bacterial Fc binding
support. The chromatographic surface is incubated with a sample, such as a
sample of a neoplasm. The
antibodies on the chromatographic surface can recognize the antigens present
in the sample. The unbound
proteins and mass spectrometric interfering compounds are washed away and the
proteins that are retained
on the chromatographic surface are analyzed and detected by SELDI-TOF. The
Mass Spectrometry profile
from the sample can be then compared using differential protein expression
mapping, whereby relative
expression levels of proteins at specific molecular weights are compared by a
variety of statistical techniques
and bioinformatic software systems.
C. Arrays for Profiling Gene Expression levels
In particular embodiments provided herein, arrays can be used to evaluate a
disclosed gene
expression signature, for example to determine a prognosis of a patient with
cancer (for example, multiple
myeloma or breast cancer) and/or determine whether a neoplasm is sensitive to
HDACi and mTORi
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combination therapy. When describing an array that consists of probes or
primers specific for three or more
of the genes listed in Table 6 or Table 7 or the proteins encoded by these
genes, such an array includes
oligonucleotide probes or primers specific for these genes or antibodies
specific for these proteins, and can
further include control probes or antibodies (for example to confirm the
incubation conditions are sufficient).
In some embodiments, the array consists of probes, primers, or antibodies
specific for 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, or 37 of the
genes listed in Table 7, and can further include one or more control probes,
primers, or antibodies. In some
embodiments, the array consists of probes, primers, or antibodies specific for
3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121,
122, 123 or 124 of the genes listed in Table 6 , and can further include one
or more control probes, primers,
or antibodies.
In one embodiment, the array includes, consists essentially of, or consists of
oligonucleotide probes
or primers or antibodies specific for each of three or more genes listed in
Table 6 or Table 7 (such as three
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
HLA-
DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,
MYBL2,
NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48, TRIP13, UBE2C, and ZNF107) or the proteins encoded by these genes. In
some embodiments,
the array further includes one or more control probes, primers, or antibodies.
Exemplary control probes
include GAPDH, 13-actin, and 18S RNA or antibodies that recognize proteins
encoded by these genes. In one
example, an array is a multi-well plate (e.g., 96 or 384 well plate). The
oligonucleotide probes or primers or
antibodies can include one or more detectable labels, to permit detection of
binding between the probe and
target (such as one of the genes listed in Table 6 or Table 7, or a protein
encoded by one of these genes.
In some embodiments, the array may further include probes, primers, or
antibodies specific for
additional genes, such as about 5, 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100
additional genes, or the proteins
encoded by these genes.
1. Array substrates
The solid support of the array can be formed from an organic polymer. Suitable
materials for the
solid support include, but are not limited to: polypropylene, polyethylene,
polybutylene, polyisobutylene,
polybutadiene, polyisoprene, polyvinylpyrrolidine, polytetrafluroethylene,
polyvinylidene difluoride,
polyfluoroethylene-propylene, polyethylenevinyl alcohol, polymethylpentene,
polycholorotrifluoroethylene,
polysulfornes, hydroxylated biaxially oriented polypropylene, aminated
biaxially oriented polypropylene,
thiolated biaxially oriented polypropylene, ethyleneacrylic acid, thylene
methacrylic acid, and blends of
copolymers thereof (see U.S. Patent No. 5,985,567).
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In general, suitable characteristics of the material that can be used to form
the solid support surface
include: being amenable to surface activation such that upon activation, the
surface of the support is capable
of covalently attaching a biomolecule such as an oligonucleotide or antibody
thereto; amenability to "in situ"
synthesis of biomolecules; being chemically inert such that at the areas on
the support not occupied by the
oligonucleotides or proteins (such as antibodies) are not amenable to non-
specific binding, or when non-
specific binding occurs, such materials can be readily removed from the
surface without removing the
oligonucleotides or proteins (such as antibodies).
In another example, a surface activated organic polymer is used as the solid
support surface. One
example of a surface activated organic polymer is a polypropylene material
aminated via radio frequency
plasma discharge. Other reactive groups can also be used, such as
carboxylated, hydroxylated, thiolated, or
active ester groups.
2. Array formats
A wide variety of array formats can be employed in accordance with the present
disclosure. One
example includes a linear array of oligonucleotide or antibody bands,
generally referred to in the art as a
dipstick. Another suitable format includes a two-dimensional pattern of
discrete cells (such as 4096 squares
in a 64 by 64 array). As is appreciated by those skilled in the art, other
array formats including, but not
limited to slot (rectangular) and circular arrays are equally suitable for use
(see U.S. Patent No. 5,981,185).
In some examples, the array is a multi-well plate. In one example, the array
is formed on a polymer medium,
which is a thread, membrane or film. An example of an organic polymer medium
is a polypropylene sheet
having a thickness on the order of about 1 mil. (0.001 inch) to about 20 mil.,
although the thickness of the
film is not critical and can be varied over a fairly broad range. The array
can include biaxially oriented
polypropylene (BOPP) films, which in addition to their durability, exhibit low
background fluorescence.
The array formats of the present disclosure can be included in a variety of
different types of formats.
A "format" includes any format to which the solid support can be affixed, such
as microtiter plates (e.g.,
multi-well plates), test tubes, inorganic sheets, dipsticks, and the like. For
example, when the solid support is
a polypropylene thread, one or more polypropylene threads can be affixed to a
plastic dipstick-type device;
polypropylene membranes can be affixed to glass slides. The particular format
is, in and of itself,
unimportant. All that is necessary is that the solid support can be affixed
thereto without affecting the
functional behavior of the solid support or any biopolymer absorbed thereon,
and that the format (such as the
dipstick or slide) is stable to any materials into which the device is
introduced (such as clinical samples and
reaction solutions).
The arrays of the present disclosure can be prepared by a variety of
approaches. In one example,
oligonucleotide or protein sequences are synthesized separately and then
attached to a solid support (see
U.S. Patent No. 6,013,789). In another example, sequences are synthesized
directly onto the support to
provide the desired array (see U.S. Patent No. 5,554,501). Suitable methods
for covalently coupling
oligonucleotides and proteins to a solid support and for directly synthesizing
the oligonucleotides or proteins
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onto the support are known to those working in the field; a summary of
suitable methods can be found in
Matson et al., Anal. Biochem. 217:306-10, 1994. In one example,
oligonucleotides are synthesized onto the
support using conventional chemical techniques for preparing oligonucleotides
on solid supports (such as
PCT applications WO 85/01051 and WO 89/10977, or U.S. Patent No. 5,554,501).
A suitable array can be produced using automated means to synthesize
oligonucleotides in the cells
of the array by laying down the precursors for the four bases in a
predetermined pattern. Briefly, a multiple-
channel automated chemical delivery system is employed to create
oligonucleotide probe populations in
parallel rows (corresponding in number to the number of channels in the
delivery system) across the
substrate. Following completion of oligonucleotide synthesis in a first
direction, the substrate can then be
rotated by 90 to permit synthesis to proceed within a second set of rows that
are now perpendicular to the
first set. This process creates a multiple-channel array whose intersection
generates a plurality of discrete
cells.
The oligonucleotides can be bound to the polypropylene support by either the
3' end of the
oligonucleotide or by the 5' end of the oligonucleotide. In one example, the
oligonucleotides are bound to the
solid support by the 3' end. However, one of skill in the art can determine
whether the use of the 3' end or the
5' end of the oligonucleotide is suitable for bonding to the solid support. In
general, the internal
complementarity of an oligonucleotide probe in the region of the 3' end and
the 5' end determines binding to
the support.
In particular examples, oligonucleotide probes or antibodies on the array
include one or more labels
that permit detection of oligonucleotide probe:target sequence hybridization
complexes or antibody:protein
complexes.
VI. Methods of Treatment
Several embodiments described herein include identification of a neoplasm in a
subject sensitive to
mTORi/HDACi combination therapy. In several embodiments, the methods include
selecting an
mTORi/HDACi combination therapy for the subject. In further examples, the
selected mTORi/HDACi
combination therapy is administered to the subject. Subjects that can benefit
from the disclosed methods
include human and veterinary subjects.
mTORi/HDACi combination therapy includes administration to a subject one or
more agents that
inhibit the activity of one or more HDAC molecules and one or more mTOR
molecules. The combination
therapy can be achieved with the use of a single agent (that inhibits both
mTOR and HDAC) or a
combination of one or more agents that inhibit mTOR and one or more agents
that inhibit HDAC. The
HDACi and mTORi can be administered simultaneously or sequentially.
In several embodiments, about 0.001 to about 5000 mg of the HDACi and/or mTORi
is
administered to the subject per day. For example, about 0.01, 0.05, 0.1, 0.5,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,
20, 30, 40, 50, 60, 70, 80, 90 or 100 mg/day of the agent can be administered
to the subject, such as from
about 0.01 to 0.1,0.1 to 1, 1 to10 or 10 to 100 mg/day of the agent can be
administered to the subject. In
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particular examples, the subject is administered one or more agents on a
multiple daily dosing schedule, such
as at least two consecutive days, 10 consecutive days, and so forth, for
example for a period of weeks,
months, or years. In one example, the subject is administered the conjugates,
antibodies, compositions or
additional agents for a period of at least 30 days, such as at least 2 months,
at least 4 months, at least 6
months, at least 12 months, at least 24 months, or at least 36 months. For
example, the subjects can be orally
administered 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100 mg/day LBH589
(Panobinostat), or more, in
combination with 0.5, mg/day RAD001 (everolimus). In some examples, the
subject is administered the
HDACi on days 1, 3, 5, 15, 17 and 19 of a 28 day cycle and the mTORi every day
of the 28 day cycle.
The person of ordinary skill is familiar with HDAC inhibitors, as well as
protocols for their
administration to a subject. For example, HDAC inhibitors include (1) small
molecular weight carboxylates
(e.g., 4-phenylbutyrate and valproic acid); (2) hydroxamic acids (e.g.,
Suberoylanilide Hydroxamic Acid
(SAHA; Vorinostat; Zolinza; Octanedioic acid hydroxyamide phenylamide), PXD101
(Belinostat),
LAQ824, LBH-589 (Panobinostat), Pyroxamide, trichostatin A (TSA), oxamflatin
and CHAPs, such as,
CHAP1 and CHAP 31); (3) benzamides (e.g., MS-275 (Entinostat; SNDX-275; MS-
275; MS-27-275), CI-
994 (Tacedinaline; PD-123654; GOE-5549; Acetyldinaline), mecetinostat
(MGCD0103)); and (4) cyclic
peptides (Trapoxin A, trapoxin B, despeptides and Apicidin (Drummond et al.,
Ann. Rev. Phartnacol.
Toxicol., 45:495-528, 2005; Marks el aL, J. Mzti. Cancer Inst., vol. 92, no,
15, pp. 1210-1216, 2000; Prince
et al., Clin. Cancer Res., 15:3958-3969, 2009). Additional HDAC inhibitors
include ML-210; M344
(D237); Tubastatin A; Scriptaid; NSC 3852; NCH 51 (PTACH); HNHA (Heptanomide);
BML-281; CBHA;
Salermide; Pimelic Diphenylamide; ITF2357 (Givinostat); PCI-24781 (CRA-02478);
APHA Compound 8;
Droxinostat; SB939, Resminostat (4SC-201), CUDC-101, AR-42, CHR-2845, CHR-
3996, 4SC-202,
sulphoraphane.
Pan-HD ACs inhibitors include, e.g., SAHA, LBH-589 (Panobinostat), PXD101
(Belinostat); and
isotype/class-specific 1-11)ACs inhibitors include, e.g., mmidepsin,
mecetinostat (MGC1)0103) and MS-275
(Prince et al., Clin. Cancer Res., 15:3958-3969, 2009). SAHA and romidepsin
(Istodax; FK228) are IIDACs
inhibitors approved by the U.S. Food and Drug Adminitration (FDA) for the
treatment of refractory
cutaneous T-cell lymphoma (CICL; Marks and Breslow, Nat. Biotechnol., 25:84-
90, 2007; Piekarz et al., J.
Clin. Oncol., 27:5410-5417, 2009). Additionally, examples of HDAC inhibitors
can be found in U.S. Pat.
Nos. 5,369,108, 5,700,811, 5,773,474, 5,055,608, 5,175,191, as well as,
Yoshida et al., Bioassays, 17:423-
430, 1995; Saito et al., Proc. Natl. Acad. Sci. U.S.A., 96:4592-4597, 1999;
Furamai et al., Proc. Natl. Acad.
Sci. U.S.A., 98: 87-92, 2001; Komatsu et al., Cancer Res., 61:4459-4466, 2001;
Su et al., Cancer Res.,
60:3137-3142, 2000; Lee et al., Cancer Res., 61:931-934, 2001; Suzuki et al.,
J. Med. Chem., 42:3001-3003,
1999.
The person of ordinary skill is also familiar with mTOR inhibitors, as well as
protocols for their
administration to a subject. For example, such inhibitors include Rapamycin
(sirolimus; Wyeth) and
Rapamycin derivatives (e.g., temsirolimus (CCI-779; Wyeth); everolimus
(RAD001; Novartis); and
ridaforolimus (deforolimus; AP23573; Ariad Pharmaceuticals)), and small-
molecule mTOR kinase
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inhibitors (e.g., AZD8055 (AstraZeneca); PKI-179 (Wyeth); PKI-587 (Wyeth);
XL765 (Exelixis); NvP-
BEZ235 (Novartis)). The person of ordinary skill is also familiar with
protocols for administration of mTOR
inhibitors; (See, e.g., the following references (which are incorporated by
reference herein in their entirety as
they relate to mTOR inhibitors and administration thereof): Dancey, Nat. Rev.
Clin. Oncol., 7:209-219, 200;
Chan et al., J. Clin. Oncol., 23:5314-5322, 2005; Witzig, et al., J. Clin.
Oncol., 23:5347-5356, 2005; Anse11
et al., J. Clin. Oncol., 24:a2732, 2006; Oza, et al., J. Clin. Oncol.,
24:a3003, 2006; Oza et al., J. Clin.
Oncol., 26:a5516, 2008; .Pandya et al., J. Thorac. Oncol., 2:1036-1041, 2007;
.Margolin et al., Cancer,
104:1045-1048, 2005; Chang et al., Invest. New Drugs, 23:357-361, 2005;
Galanis et al., J. Clin. Oncol.,
23:5294-5304, 2005; .Duran et al., Br. J. Cancer, 95:1148-1154, 2006; .Farag
et al., J. Clin. Oncol., 24:
a7616, 2006; .Yee et al., Blood (ASHAnnual Meeting Abstracts), 104:a4523,
2004; Okuno et al., J. Clin.
Oncol., 24:a9504, 2006; Soria et al., Ann. Oncol., 20:1674-1681, 2009; Wolpin
et al., J. Clin. Oncol.,
27:193-198, 2009; Yee et al., Clin. Cancer. Res., 12:5165-5173, 2006; Yao et
al., J. Clin. Oncol., 26:4311-
4318, 2008; Rao et al., J. Clin. Oncol., 25:a8530, 2007; Chawla et al., J.
Clin. Oncol., 24:a9505, 2006;
Rizzieri et al., Clin. Cancer Res., 14:2756-2762, 2008; Colombo et al., J.
Clin. Oncol., 25:a5516, 2007;
Bissler et al., N. Engl. J. Med., 358:140-151, 2008; Garrido-Laguna et al., J.
Clin. Oncol., 27:a4612, 2009).
In some examples, the mTORi includes an agent that inhibits activation of
mTOR, for example a PI3K
inhibitor such as GDC-0941, BKM 120, GS-1101, PX-886, or an AKT inhibitor such
as perifosine, MK-
2206, GSK2110183. In some examples, an agent is used that inhibits both HDAC
and mTOR (or an
upstream activator of mTOR, such as PI3K), for example, CURD-906 or CURD-907
(Curis, Inc., which
inhibit both PI3K and HDAC).
In some examples, the method further includes selecting a therapy other than
mTORi/HDACi
combination therapy for such a subject. In further examples, the selected
therapy is administered to the
subject. In some examples, the selected therapy includes radiation therapy
and/or one or more
chemotherapeutic agents. Chemotherapeutic agents include, but are not limited
to alkylating agents, such as
nitrogen mustards (for example, chlorambucil, chlormethine, cyclophosphamide,
ifosfamide, and
melphalan), nitrosoureas (for example, carmustine, fotemustine, lomustine, and
streptozocin), platinum
compounds (for example, carboplatin, cisplatin, oxaliplatin, and BBR3464),
busulfan, dacarbazine,
mechlorethamine, procarbazine, temozolomide, thiotepa, and uramustine;
antimetabolites, such as folic acid
(for example, methotrexate, pemetrexed, and raltitrexed), purine (for example,
cladribine, clofarabine,
fludarabine, mercaptopurine, and tioguanine), pyrimidine (for example,
capecitabine), cytarabine,
fluorouracil, and gemcitabine; plant alkaloids, such as podophyllum (for
example, etoposide, and
teniposide), taxane (for example, docetaxel and pacl*taxel), vinca (for
example, vinblastine, vincristine,
vindesine, and vinorelbine); cytotoxic/antineoplasm antibiotics, such as
anthracycline family members (for
example, daunorubicin, doxorubicin, epirubicin, idarubicin, mitoxantrone, and
valrubicin), bleomycin,
hydroxyurea, and mitomycin; topoisomerase inhibitors, such as topotecan and
irinotecan; monoclonal
antibodies, such as alemtuzumab, bevacizumab, cetuximab, gemtuzumab,
rituximab, panitumumab, and
trastuzumab; photosensitizers, such as aminolevulinic acid, methyl
aminolevulinate, porfimer sodium, and
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verteporfin; and other agents , such as alitretinoin, altretamine, amsacrine,
anagrelide, arsenic trioxide,
asparaginase, bexarotene, bortezomib, celecoxib, denileukin diftitox,
erlotinib, estramustine, gefitinib,
hydroxycarbamide, imatinib, pentostatin, masoprocol, mitotane, pegaspargase,
and tretinoin.
Chemotherapeutic agents can be administered individually, or in combination.
Selection and therapeutic
dosages of such agents are known to those skilled in the art, and can be
determined by a skilled clinician.
VII. Neoplasm Samples
The disclosed methods can be used to determine the responsiveness of a
neoplasm to a therapy (such
as mTORi/HDACi combination therapy) or to determine the prognosis of a subject
with a neoplasm. In
some examples, the neoplasm is a solid neoplasm, such as a sarcoma or
carcinoma, including fibrosarcoma,
myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, and other
sarcomas, synovioma,
mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon
carcinoma, lymphoid
malignancy, pancreatic cancer, breast cancer, lung cancers, ovarian cancer,
prostate cancer, hepatocellular
carcinoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma,
sweat gland carcinoma,
sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma,
medullary carcinoma,
bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma,
choriocarcinoma, Wilms'
tumor, cervical cancer, testicular tumor, bladder carcinoma, and CNS tumors
(such as a glioma, astrocytoma,
medulloblastoma, craniopharyogioma, ependymoma, pinealoma, hemangioblastoma,
acoustic neuroma,
oligodendroglioma, menangioma, melanoma, neuroblastoma and retinoblastoma).
In other examples, the neoplasm includes an abnormal cell growth occurring in
a hematological
cancer, including leukemias, including acute leukemias (such as acute
lymphocytic leukemia, acute
myelocytic leukemia, acute myelogenous leukemia and myeloblastic,
promyelocytic, myelomonocytic,
monocytic and erythroleukemia), chronic leukemias (such as chronic myelocytic
(granulocytic) leukemia,
chronic myelogenous leukemia, and chronic lymphocytic leukemia), polycythemia
vera, lymphoma,
Hodgkin's disease, non-Hodgkin's lymphoma (indolent and high grade forms;
including Burkitt's
lymphoma and mantle cell lymphoma), multiple myeloma, plasmacytoma,
Waldenstrom's
macroglobulinemia, heavy chain disease, myelodysplastic syndrome, and
myelodysplasia.
Appropriate samples include any conventional biological samples, including
clinical samples
obtained from a human or veterinary subject. Exemplary samples include,
without limitation, cells, cell
lysates, blood smears, cytocentrifuge preparations, cytology smears, bodily
fluids (e.g., blood, plasma,
serum, saliva, sputum, urine, bronchoalveolar lavage, sem*n, etc.), tissue
biopsies (e.g., neoplasm biopsies),
fine-needle aspirates, and/or tissue sections (e.g., cryostat tissue sections
and/or paraffin-embedded tissue
sections). In other examples, the sample includes circulating neoplasm cells.
In particular examples,
neoplasm samples are used directly (e.g., fresh or frozen), or can be
manipulated prior to use, for example,
by fixation (e.g., using formalin) and/or embedding in wax (such as formalin-
fixed paraffin-embedded tissue
samples).
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EXAMPLES
The following examples are provided to illustrate particular features of
certain embodiments, but the
scope of the claims should not be limited to those features exemplified.
Example 1
Identification of synergistic effects of HDAC/mTOR inhibition
This example describes the efficacy of combined HDAC and mTOR inhibition for
the treatment of
neoplasms. The utility of combining sirolimus and entinostat to control
proliferation and growth of
malignant B cell tumors was assessed.
Two central pathways frequently dysregulated in cancer are the
PI3K/Akt/mTOR/p53(mTOR) and
Cyclin/CDK/CDKI/Rb(CDK) pathways. mTOR and CDK pathway dysregulation is common
in B cell
neoplasias, including mantle cell lymphoma (MCL; Dal Col et al., Blood,
111:5142-5151, 2008 and Rizzatti
et al., Br J Haematol. 2005;130:516-526), multiple myeloma (MM; Dilworth et
al. Blood. 2000;95:1869-
1871, and Peterson et al. Cell. 2009;137:873-886), Burkitt's lymphoma (Klangby
et al. Blood.
1998;91:1680-1687 and Sanchez-Beato et al. Am J Pathol. 2001;159:205-213). and
mouse plasmacytoma
(PCT; Bliskovsky et al., Proc Natl Acad Sci US A. 2003;100:14982-14987; Zhang
et al., Proc Nati Acad Sci
USA. 1998;95:2429-2434; Zhang et al Mol Cell Biol. 2001;21:310-318; Mock et
al. Blood. 1997;90:4092-
4098; Mock et al. Proc Nati Acad Sci USA. 1993;90:9499-9503; Potter et al.
Cancer Res. 1994;54:969-
975; and Potter et al. Curr Top Microbiol Immunol. 1988;137:289-294), where
genetic predisposition is
determined in part by alleles of Mtor and Cdkn2a.
mTOR pathway dysregulation mechanistically involves mutations, activation by
growth factor
receptor pathways, PTEN loss, and amplification of AKT and DEPTOR. mTOR, a
serine-threonine kinase
forming two complexes, mTORC1 (mTOR, RAPTOR, PRAS40, mLST8, DEPTOR) and mTORC2
(mTOR, RICTOR, PROTOR, mLST8, SIN1, DEPTOR), phosphorylates a number of
downstream targets
(most notably pS6, 4EBP1, AKT) that regulate transcription/translation, cell
proliferation/survival, immune
responses, metabolism, and autophagy. Rapamycin (sirolimus), a relatively
specific inhibitor of mTORC1,
can also affect mTORC2 following prolonged exposure. Clinical investigations
using Rapamycin or its
analogs as single agents have shown modest long-term benefit despite initial
antitumor activity.
Similarly, dysregulation of the cyclin dependent kinase (CDK) pathway often
involves Cyclin/CDK
amplification or reduced activity of a tumor suppressor gene in the pathway
(Rb and cyclin-dependent
kinase inhibitors (CDKI), including p16 and p21), via genetic or epigenetic
mechanisms. HDAC inhibition
in MM cell lines negatively regulates the Rb pathway (decreased phospho-Rb,
decreased cyclin D1 and E2f1
expression), and positively regulates the p53 pathway (enhanced p53 activity,
increased p21 and p27
expression). The benzamide, entinostat (MS-275), is a selective Class I HDAC
inhibitor capable of
reactivating tumor suppressor gene pathways, which can in turn reduce CDK
activity. In contrast to pan-
HDAC inhibitors, entinostat has strong activity against HDAC1, weak activity
for HDACs2 and 3, some
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activity for HDAC9, and no activity against HDAC8 (Witt et al. Cancer Lett.
2009;277:8-21 and Bantscheff
et al., Nat Biotechnol. 2011;29:255-265). Combining HDAC inhibitors with other
therapies has shown
efficacy in clinical trials for MM (Badros et al., Clin Cancer Res.
2009;15:5250-5257) and breast
cancer(Huang et al., Cancer Lett. 2011;307:72-79), despite the relatively
modest benefit of these inhibitors
as single agents (Federico et al., J Biomed Biotechnol. 2011;2011:475641; Gojo
et al., Blood.
2007;109:2781-2790; Gore et al., Clin Cancer Res. 2008;14:4517-4525; Hess-
Stumpp et al., Int J Biochem
Cell Biol. 2007;39:1388-1405; and Kummar et al., Clin Cancer Res. 2007;13:5411-
5417).
Methods
Cell lines. Human MM cell lines L363, U266, EJM, KMS12, KMS18, 8226, FR-4, JK-
6L, ANBL-
6, FLAM-76, XG-6, OCI-MY1, OCI-MY5, LP-1, MM-M1, SKMM-1, and SACHI were
derived and
authenticated as previously described (Gabrea et al., Genes Chromosomes
Cancer., 47:573-590, 2008).
XRPC24 (X24:interleukin (IL)-6 independent, p16 positive), M0PC265 (IL-6
dependent,p16 positive), and
MOPC460 (IL-6 dependent, p16 negative, p53 partial deletion) cells were
derived from pristane-induced
PCTs from BALB/c mice. 107403 cells (p16 deleted) were cloned from a myc-ras
retroviral-induced PCT
from DBA/2 mice. MM cell lines were cultured in RPMI-1640 (2 mM L-glutamine,
10% fetal bovine serum
(FBS), 100U/m1 penicillin, 1001J g/ml streptomycin). Mouse cell lines were
cultured in RPMI-1640 with
50[LM P-mercaptoethanol and 10 ng/ml IL-6, except X24 which is IL-6
independent.
Drugs. For in vitro studies: MS-275 (Sigma-Aldrich), sirolimus (Developmental
Therapeutics
Program (DTP), NCI) and triapine (Nanotherapeutics) were dissolved in DMSO at
10 mM (stored at -20 C).
For in vivo studies: A 50 mg/ml stock of Rapamycin (DTP, NCI) was prepared in
ethanol (stored at -20 C),
and diluted at the time of injection to final concentration in 5% Tween-80, 5%
polyethylene glycol-400
(Sigma, St Louis, MO). Entinostat (MS-275) (Syndax) was used in suspension
made with 20%
hydroxypropyl P-cylodextrin (Sigma). For in vivo studies, entinostat was
generously provided by Syndax
Pharmaceuticals Inc. Triapine and sirolims were generously provided by
Nanotherapeutics Inc. and DTP,
NCI, respectively.
Cell proliferation assay. 50,000 cells were seeded in 96-well (200 1.11/well)
plates and incubated
with sirolimus and/or entinostat for 24-72 hours. WST-1 reagent (Roche) was
used per manufacturer's
protocol.
In Vivo Studies. Athymic, NCr-nu/nu mice (Frederick, MD) were used under
institutionally
approved (ACUC, NCI) protocols. For visualization, MM cells were infected with
pSicoLV-luciferase-green
fluorescent protein fusion gene. Growth of luc/GFP positive cells was measured
weekly by bioluminescence
using a XenogenIVIS 100 system. Sirolimus and entinostat (200 1 of each) were
administered daily five
days a week for four (L363) or twelve (U266) weeks by i.p. injection and oral
gavage, respectively.
Combination index calculations. CompuSyn (ComboSyn, Inc.) was used to assess
synergy/additivity/antagonism of the drug combination by the Chou-Talalay
method(Chou, Cancer Res.
70:440-446. 2010).
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Flow Cytometry. Cell cycle (stained with propidium iodide/RNAse buffer) and
apoptosis assays
(stained with Annexin-V-PE/7AAD) were done by FACScan flow cytometry and
quantified using
ModfitLT3.1(Verity Software House) and BD CellQuestPro.
Westerns. Antibodies were obtained from Cell Signaling and used at 1:1000
dilutions.
Results
mTORWIDACi combination inhibits tumor growth. The effects of sirolimus or
entinostat alone on
MM, MCL, and PCT cell line viability were concentration and time dependent
(FIG. 2A-L). Low doses of
sirolimus (10 nM) and entinostat (0.5[LM) were tested in a panel of seventeen
human MM cell lines, two
MCL cell lines, and two mouse PCT cell lines (FIG. 1A; Table 1). This dose
combination decreased p-S6
and increased acetylation of histones H3/H4 (FIG. 4), indicating effective
target inhibition for sirolimus and
entinostat, respectively. Consistent with previous reports of same-class
drugs, the addition of entinostat to
sirolimus prevents AKT activation often seen with rapalog treatment (Zhang et
al., Blood, 117:1228-1238,
2011) (FIG. 6A). Engagement of the pro-survival MAPK pathway is frequently
observed in MM
(Annunziata et al., Blood, 117:2396-2404, 2011 and Giuliani et al., Leukemia,
18:628-635, 2004); MAPK
activation was reduced by the combination as evidenced by decreased pERK1/2
(FIG. 6B).
Compared with single drug treatment, the combination inhibited cell growth
(p<0.01) in most cell
lines. This dose combination was active (c.>EC50) in 19/21 lines; KMS18 and
RPMI8226 were not as
sensitive at these doses. Drug synergy, as defined by the Chou-Talalay method
(Combination Index <1)
(Chou, Cancer Res. 70:440-446. 2010), was also observed in 19/21 lines (FIGs.
1A, 2M-0; Table 1);
sirolimus alone was as effective as the drug combination for the two MM cell
lines OCI-MY5 and FR4. The
combination treatment was relatively nontoxic to human PBMCs from healthy
donors (FIG. 2P).
In vivo combination activity was tested in xenograft experiments. L363 MM
cells were xenografted
on flanks of nude mice and grown for eleven days before randomization to
treatment groups (control,
combination, and two dose points for each single agent). Tumors were imaged
weekly in vivo for 28 days of
treatment (FIG. 1B), after which mice were euthanized and tumors weighed. The
control and single agent
arms had palpable tumors, while no dissectible tumors were found in the
combination group (FIG. 1C).
Subsequently, a less sensitive line, U266, was grown for three weeks to a
tumor volume of 50 mm3 prior to
treatment group randomization. Tumor burden in the control arm necessitated
euthanasia by treatment week
4. In the single agent groups, tumor progression was delayed, but outgrowth
eventually occurred. By
contrast, the combination treatment prevented tumor growth for three months,
with no or small tumors
present at necropsy (FIG. 1D). No treatment-related illnesses or significant
weight were observed (FIG. 3).
Combining entinostat with sirolimus enhances cell cycle arrest and apoptosis.
Sirolimus caused
arrest/slowing of many tumor cells in G1 phase (FIGs. 5A,B; 6C). Cells in S
phase were greatly reduced
(FIGs. 5A,B; 6C), and G1 arrest was enhanced by the sirolimus/entinostat
combination treatment in most
cell lines, except L363, which underwent G2/M arrest. Annexin V-7AAD staining
showed increased
apoptosis in combination compared to single agent treatments (FIG. 5C,D).
Consistent with enhanced
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apoptosis, PARP cleavage was observed in cells treated with entinostat or the
combination, but not with
sirolimus (FIG. 5E); the combination reduced expression of anti-apoptotic
proteins BCL-xL and Survivin
(FIG. 6D,E).
Example 2
Identification of molecular synergy of combination by transcriptional co-
expression analysis
This example describes gene expression signatures that can be used to predict
whether a neoplasm is
sensitive to combined HDAC and mTOR inhibition and/or to predict prognosis of
a subject with a neoplasm.
Systems-level weighted gene co-expression network analyses were used to
determine the transcriptional
underpinnings of the mTORi/HDACi drug combination. This approach revealed a
gene signature highly
enriched with genes cooperatively affected by the drugs and significantly
dysregulated in MM patients
(GEO database), and identified a set of markers with clinical potential to
predict which patients, based on
their gene expression patterns, may benefit most from this combination
treatment.
Methods
Microarray and Bioinformatics. L363 cells were treated with either 1 nM or 10
nM sirolimus, 0.5
1J M entinostat or the combination for 48 hours. Total RNA was extracted with
TRIzol (Invitrogen) from
three separate experiments. Labeled aRNA prepared from 1 jig RNA
(MessageAmpTmII aRNA
Amplification kit; Ambion) was hybridized to Affymetrix (Santa Clara, CA, USA)
HG-U133 Plus 2 array
chips, processed on Workstation 450, and analyzed with Gene Chip Operating
Software (Affymetrix).
Microarray data pre-processing. Affymetrix (Santa Clara, CA, USA) HG-U133 Plus
2 CEL files
were imported to the R Bioconductor affy package and processed with the RMA
algorithm (Irizarry et al.,
Biostatistics, 4:249-264, 2003). A schematic of the workflow for pre-
processing is provided online (FIG. 7).
Probe sets with low signal across all arrays were removed. Multiple probe sets
corresponding to the same
gene were replaced by the one with the maximal median intensity. Around 14K
genes were available for the
statistical analyses.
Analysis of Variance. Univariate two-way ANOVA models were applied to examine
the combined
expression effects of entinostat and sirolimus (Slinker, J. Mol. Cell.
Cardiol., 30:723-731, 1998) (see
workflow: FIG. 7, 8). Specifically, a significant interaction term in the two-
by-two factorial ANOVA was
used as an indication of transcriptional synergy for the drug combination
(P<0.05). Otherwise, when the
interaction was not significant, the additive two-way ANOVA model was fitted
and the main effects for each
individual drug treatment tested. When the interaction was significant, the
individual simple effects for the
entinostat and sirolimus treatments were estimated with one-way ANOVA
contrasts. The simple effect for
the drug combination treatment was also estimated for each gene. Using the
method of Storey and Tibshirani
(Storey et al., Proc Nati Acad Sci US A., 100:9440-9445, 2003) the P-values
were converted to the false
discovery rate Q-values. The analyses were done using R programming language
(R: A Language and
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Environment for Statistical Computing. R: A Language and Environment for
Statistical Computing. 2011)
and the gregmisc and qvalue libraries.
WGCNA. Network modeling was performed using Weighted Gene Co-expression
Analysis as
proposed by Langfelder and Horvath (Zhang et al., Stat Appl Genet Mol Biol.,
4:e17, 2005) and
implemented in the R WGCNA library (Langfelder, BMC Bioinformatics., 9:559,
2008). In the network,
nodes represented gene expression profiles across the experiments and the
undirected edges represented the
correlation-based strength of connection among genes. In the first step, the
unsigned Pearson's correlation
coefficients were determined for all pair-wise comparisons of gene-expression
profiles, which were then
transformed into the adjacency matrix using a power function: a13= Icor(xi,
)0113. The power adjacency
function converted the co-expression similarity measure into a continuous
strength of connection (weight),
while allowing retention of all co-expression relationships among genes and
scale-free network properties by
emphasizing large correlations at the expense of small ones. Furthermore, the
connectivity, kõ of the i-th
node was defined as the sum of its adjacencies with all other nodes in the
network (k,= Ea,j). The power
coefficient p = 8 was applied when building the network, which resulted in the
connectivity distribution
satisfying the exponentially truncated power-law. In such networks the degree
of connectivity of the most
connected nodes (hubs) is smaller than expected in a pure scale-free network,
due to the scale-free properties
preserved within a narrower range of the node connectivities(Langfelder et
al., Bioinformatics, 24:719-720,
2008).
In forming network modules (sets of genes whose expression profiles were
highly correlated across
experiments), the adjacency was further transformed using the topological
overlap measure
(interconnectedness). The topological overlap matrix (TOMO defined commonality
of network neighbors
for each pair of nodes and its symmetrical distance matrix (d13 = 1-TOM,j) was
used to identify highly
interconnected groups of nodes with a clustering algorithm. The network
modules were detected using the
agglomerative average linkage hierarchical clustering and automated dynamic
cut tree algorithm (Langfelder
et al., Bioinformatics, 24:719-720, 2008), with a minimum module size of 20
genes. Each module
represented a group of genes with similar expression pattern summarized by the
module eigengene (ME,),
computed as the first principal component of a module's expression matrix.
Module eigengenes were utilized
to define a measure of module membership (MM,) for a node as the signed
correlation of a node profile with
the corresponding module eigengene.
Assessing which modules captured genes relevant to particular drug treatments,
the two-way
ANOVA gene significance (GS, = -log10 P-value,) was integrated with the
network concepts of module
significance (M51) and intramodular connectivity (UN,). The module
significance measure was calculated as
the average gene significance for all nodes in a particular module.
Intramodular connectivity for the i-th
node quantified its co-expression with all the other nodes in a given module
by the sum of a node's
adjacencies within the module. The relation between the intramodular
connectivity and gene significance
was estimated with Pearson's correlation coefficient and Fisher's asymptotic
test implemented in the
WGCNA package. A combination of module significance equal or greater than 2.0
(negative 10g10 of 0.01)
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with a significant correlation of gene significance and intramodular
connectivity (Bonferroni corrected P-
value<0.05) was used to associate a network module with a drug response.
In the final step a top connectivity network was selected. Spurious or
isolated connections with the
topological overlap less than 0.25 were removed. In addition, the nodes were
selected based upon the
measure of module membership (absolute value of MM > 0.8) and the gene
significance of the module-
specific drug effects (GS > 2). Extremely highly connected nodes (hub genes)
were defined within each
module, setting the cutoff threshold for scaled intramodular connectivity
(kINse = kIN/kIN..) to 0.6 and
pairwise adjacency to 0.66 (corresponding to the pairwise Pearson's
correlation coefficient of 0.95).
Functional over-representation. The NIH Database for Annotation,
Visualization, and Integrated
Discovery (DAVID) Bioinformatics Resource was used to determine over-
representation of Gene Ontology
(GO) (Huang et al., Nat Protoc. 4:44-57, 2009 and Huang et al, Curr Protoc
Bioinformatics, Chapter 13:Unit
13-11, 2009) terms. DAVID's GO FAT functional categories (GO subsets with
broadest terms filtered out)
were tested. The significance of the functional enrichment was identified with
a modified Fisher's exact test
(EASE score) followed by the Benjamini correction for multiple comparisons and
using 0.05 as a p-value
cutoff. Lists of enriched GO terms were summarized with semantically non-
redundant terms using the
REVIGO algorithm (Supek et al., PLoS One., 6:e21800, 2011) with SimRel and
medium similarity options.
Results
Gene co-expression network analysis identifies an mTORi/HDACi cooperative
Drug response. To define, at a systems-level, the cellular responses
underlying the synergistic
effects of mTOR and HDAC inhibition, whole genome expression profiles of MM
cells treated with each
inhibitor individually, and in combination, were generated. Weighted gene co-
expression network analysis
(WGCNA) was used to identify sets of highly correlated genes (gene modules),
by constructing a network
based on pairwise Pearson's correlations between expression profiles, followed
by unsupervised hierarchical
clustering on topological dissimilarity (Zhang et al., Stat Appl Genet Mol
Biol., 4:17, 2005; FIG. 9:
WGCNA cluster dendrogram/scale free topology). Using this approach, five
modules, color-coded blue,
orange, red, darkgreen, and springgreen, of co-expressed genes (FIGs. 9-11),
were analyzed. As the gene
expression effects within a module were likely to arise from a common
perturbation (Horvath et al., Proc
Nati Acad Sci U S A., 103:17402-17407, 2006) (i.e. a single drug or drug
combination), gene expression
effects were assigned in the modules to drug treatments (Pearson's correlation
measures of intramodular
connectivity and mean significance of genes; FIGs.10B, 11). From these
comparisons, both drugs affected
expression of the genes in the blue and orange modules, sirolimus those in the
red module, and entinostat,
the genes in both green modules (FIG. 10).
Using network and intramodular connectivity values (FIG. 11), a drug response
network of 901
highly connected genes (FIG. 10C-E: color-coded by module and sized by degree
of connectivity) was
defined from the set of 1647 genes whose expression levels were altered by the
drug treatments (FIG. 7).
The eigengene graphs and heatmaps (FIG. 10E), demonstrate the relationship of
each drug's effects to the
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overall expression pattern of up- and down-regulated genes. The HDAC inhibitor
alone induces up-
regulation of some genes (springgreen module), and down-regulation of others,
mostly in the darkgreen
module. In general, Rapamycin alone (red) down-regulates gene expression. Two
gene modules were
affected by both drugs. In one (orange), each drug induces an opposing
transcriptional response, leading to
no net expression change (i.e., neutral) when combined. Notably, in the other
(blue), genes are altered
cooperatively by both drugs so that the expression change of the combination
is greater than that of either
individual treatment.
Functional relationships of genes affected in each drug response module (FIG.
10C) were assessed
for over-representation of gene ontology (GO) terms (DAVID database (Huang et
al., Nat Protoc., 4:44-57,
2009); FIG. 12, Table 2). Down-regulated genes from the cooperative module
showed significant functional
enrichment (p<0.001) for genes involved in cell cycle (especially mitotic
functions), as well as DNA
replication/repair (FIG. 12). The up-regulated genes included a number of HLA
genes, and were enriched for
involvement in the MHC complex and class II receptor activity (p<0.0001).
RRM2 inhibition enhances DNA damage response and decreases MM cell viability
WGCNA analysis identifies the genes/hubs most connected to all other genes
within an expression
module. As the cooperative module was enriched with genes functionally
involved in DNA
replication/repair, the hub gene, ribonucleotide reductase M2 (RRM2), was
focused on for additional follow
up and validation. Many of the genes highly connected (by WGCNA) to the RRM2
hub are involved in
DNA replication and DNA metabolic processes (DAVID GO terms); five are hub
genes in the cooperative
module (FIGs. 13A, 10E). RRM2 had one of the largest expression decreases with
the drug combination
(FIG. 13B), was a leading edge gene enriched in both new and refractory
patient datasets (FIG. 13C, Table
5), and was one of the 37 genes in the prognostic classifier (FIG. 18).
Western blot analysis of L363 cells
treated with single drugs and the combination confirmed the decrease in RRM2
protein expression predicted
by GEP (FIG. 13D). RRM2 is essential for DNA synthesis/repair, and its
inhibition by RNAi increases the
DNA damage marker 7H2AX (Zhang et al. J Biol Chem., 284:18085-18095, 2009).
The mTORi/HDACi
combination treatment also increased 7H2AX in L363 cells (FIG. 13D). Treatment
of L363 cells with
triapine, an inhibitor that specifically blocks RRM2 enzymatic activity, also
increased 7H2AX (FIG. 13D).
Previously reported effective concentrations of triapine for other tumor cell
lines (Barker et al., Clin Cancer
Res., 12:2912-2918, 2006) also inhibited MM cell viability, and combining it
with sirolimus led to greater
inhibition than with individual drugs (FIG. 13E). Thus, RRM2 is a validated
target contributing to the
combination drug effect.
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Example 3
Identification of clinically-linked markers of combination activity and
synergy
Methods:
A schematic of the bioinformatic workflow used to identify the 37-gene
classifier based on
differential expression between normal and neoplastic cells and expression
correlation with prognosis is
shown in FIG. 14.
Publicly available microarray data sets. Raw data (Affymetrix HG-U133_2 CEL
files) from
primary bone marrow samples of multiple myeloma patients and healthy donors
were obtained from the
GEO database (GSE6477) (Carrasco et al., Cancer Cell, 9:313-325, 2009; Chng et
al., Cancer Res. 67:2982-
2989, 2007)and processed with the RMA algorithm(Irizarry et al.,
Biostatistics, 4:249-264, 2003). One-way
ANOVA contrasts were used to estimate the differences in gene expression
between the healthy donors
(N=15) and the different classes of multiple myeloma, i.e., newly diagnosed
(N=75), relapsed (N=28), SMM
(N=23, smoldering multiple myeloma), and MGUS (N=21, monoclonal gammopathy of
uncertain
significance). The ANOVA t-statistic was used as the ranking metric in the
Gene Set Enrichment Analysis
(GSEA). MASS normalized data (Affymetrix HG-U133 Plus2) from 414 newly
diagnosed multiple
myeloma patients (CD-138+-selected plasma cells from bone marrow samples) were
downloaded from GEO
(GSE 4581(Zhan et al., Blood, 108:2020-2028, 2006)) and utilized in the
survival risk prediction analysis.
GSEA. Gene Set Enrichment Analysis (GSEA) was applied as described previously
(Subramanian
et al., Proc Natl Acad Sci US A, 102:15545-15550, 2005) to test the enrichment
of the WGCNA network
modules in the human microarray data with respect to multiple myeloma patients
and healthy
donors(Carrasco et al., Cancer Cell, 9:313-325, 2009; Chng et al., Cancer Res.
67:2982-2989, 2007). The
pre-ranked GSEA version (Subramanian et al., Proc Nati Acad Sci USA, 102:15545-
15550, 2005) was
performed with 5000 permutations of the module gene sets. The data were ranked
based on the t-statistic
from one-way ANOVA planned comparisons. A FDR q-value less than 0.1 was
considered significant.
Survival Analysis. Whether the cooperative gene signature of entinostat and
sirolimus was
predictive of overall survival in patients with MM disease (Than et al.,
Blood, 108:2020-2028, 2006) was
tested. A multivariate survival risk predictor was built using the principal
components method of Bair and
Tibshirani (Bair et al., PLoS Biol., 2:E108, 2004)as implemented in the BRB-
Array Tools developed by Dr.
Richard Simon and BRB-Array Tools Development Team (linus.nci.nih.gov/BRB-
ArrayTools.html). The
applied model is based on 'supergenes' that were defined here with the first
three principal component linear
combinations from genes whose expression was univariately correlated with
survival (Cox regression p-
value <0.05). The 'supergene' expression is related to survival time using Cox
proportional hazards
modeling to derive a regression coefficient (weight) for each 'supergene',
which is then used for computing
the risk score as the weighted combination of the 'supergenes'. This
multivariate model was tested in two
complementary validation schemes (10-fold cross-validation and single
training/test split) to assign risk-
group membership for clinical samples. Kaplan-Meier survival curves were
plotted for the low- and high-
risk groups (a risk score lower or higher than the 50th percentile in the
training set). To assess the
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significance of prediction in the cross-validated model a permutation log-rank
test was used. The survival
data was randomly permuted among the patients, repeating the whole risk
prediction procedure 5000 times.
The p-value was calculated as the proportion of permuted test statistics that
were as large as or larger than
the observed value. The survival difference between the two risk groups in the
single split validation
procedure was assessed by the asymptotic log-rank test. A p-value of 0.05 was
chosen as the significance
threshold for both the log-rank tests.
In vitro drug testing MM, breast, melanoma and prostate cancer cell lines were
treated with lOnM
rapamycin, 500nM MS-275, 2.5nM panobinostat, individually or in combination
for 48 hours unless
otherwise indicated in the text.
Quantification of Signature Gene Expression. Total RNA was isolated from cells
using Qiagen
RNeasy Mini Kit. 10Ong of total RNA was used for gene expression analysis
using a Nanostring custom
Gene Expression probe set. The Nanostring procedure was performed per
manufactures instructions, and
raw data was analyzed using nSolver Analysis Software (Geiss, Nature
Biotechnology 2008,PMID
18278033).
Genes targeted by the drug combination are frequently dysregulated in MM
Disease-related Differential Expression. To determine if genes altered by
mTORi/HDACi were
dysregulated in MM cells or precursor lesions, gene set enrichment analysis
(GSEA) was_used to test
whether the gene set defined by the drug responsive co-expression network
(FIG.10C; Table 4) was over-
represented/enriched in MM (newly diagnosed or treatment refractory), SMM
(smoldering myeloma), or
MGUS (monoclonal gammopathy of undetermined significance) patients relative to
CD138+ cells from
healthy donors (G5E6477; Carrasco et al., Cancer Cell, 9:313-325, 2006). The
up- and down- regulated
genes of each drug responsive module were tested separately and a high
proportion of these were
significantly enriched in the four disease gene sets (FIG. 15B; Tables 4,5).
The MM patient-specific GEP of the 901 genes in the drug-response network was
largely the inverse
of the in vitro drug combination-specific GEP (FIG. 15A). This trend was most
significant among genes in
the cooperative (blue) module, where 94 genes responded this way. In this
module, genes down-regulated by
the drug combination were found to be over-expressed in new and relapsed MM
patients versus healthy
donors, while the genes up-regulated were typically under-expressed in both MM
patients and premalignant
patient groups (SMM and MGUS) (FIG. 15B). Enrichment scores for all genes
within modules are shown in
Table 5.
Expression of genes affected by mTORillIDACi treatment in vitro is correlated
with better patient
survival. The expression of genes comprising the 37 gene combination (blue
module) response signature
was tested to determine if they would correlate with patient survival in order
for the drug combination to
have potential clinical utility. As a proxy test for the potential clinical
value of the drug response, a gene
expression prognostic classifier was developed from the cooperative drug
response signature using
supervised principal components analysis (Bair et al., PLoS Biol., 2:E108,
2004) employing two validation
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schemes (FIG. 17). A classifier for the cooperative drug signature was built
from the 37 genes most strongly
associated with overall survival in MM patients (GSE4581:training set
univariate Cox regression p-value <
0.05) (Table 7). The validated Kaplan-Meier survival curves for the predicted
low- and high-risk groups
(FIG. 18A) show statistically significant separation of the groups (log-rank
test permutation p=0.009 and
asymptotic p= 0.017 in the training and test sets, respectively).
GEPs of the 37 genes in 207 patients from the test set (FIG. 18B) shows
overexpression of many of
these genes in patients with worse prognosis; predicted risk classifications
for each patient are shown in
Table 10. The drug-induced expression pattern of the survival genes is the
opposite of the gene expression
pattern seen in high-risk patients, with one exception (FIG. 18C). All genes,
except KIAA2013 (function
unknown), were affected by the drug combination in the direction expected for
increased patient survival.
Thus, the 37 genes of this classifier may identify a subset of patients likely
to benefit from combined
mTORi/HDACi (FIG. 18C). For stratification of patients likely versus unlikely
to benefit from combined
mTORi/HDACi, the expression of the 37 genes of this classifier could be
evaluated by an algorithm to
compute a stratifying prognostic index score. As an example of this, the
stratifying prognostic index would
be computed by the following formula: Liwi xi - 4.552161, where wi and xi are
the weight (as defined in
Table 7), and logged gene expression of the ith gene as detected in a sample
of the neoplasm prior to
treatment. In this example, a patient with a neoplasm scoring greater than, or
equal to, -0.061194 would be
classified as likely to benefit from combination treatment with an mTOR
pathway inhibitor and a HDAC
inhibitor.
Differences with other prognostic classifiers
There have been several GEP-based prognostic classifiers reported in MM (Zhan
et al., Blood,
108:2020-2028, 2006; Shaughnessy et al., Blood, 109:2276-2284, 2007; Hose et
al., Haematologica, 96:87-
95, 2011; and Decaux et al., J. Clin. Oncol., 26:4798-4805, 2008), which were
evaluated to determine if any
could be substituted for stratifying patients' likely sensitive to
mTORi/HDACi. In Than et al. (Blood,
108:2020-2028, 2006), a GEP classifier was reported defining seven molecular
subtypes in MM, influenced
largely by chromosomal translocations and hyperdiploidy. When comparing the
subgroup classification of
the 414 patients in the Than study with the high/low risk classification using
the 37-gene mTORi/HDACi
classifier (FIG. 29; Table 10), where it would define patients classified as
high-risk by the 37-gene signature
as likely to benefit from mTORi/HDACi therapy, it was found that the Than
subgroup classifier was unable
to sufficiently define which patient segment would likely benefit. While all
patients classified in the Than
"proliferation" (PR) subtype would be predicted to benefit from mTORi/HDACi,
all other subtypes contain
both patients predicted to benefit and not benefit from mTORi/HDACi (FIGs. 29,
31; Table 10). Also
reported in Than et al. (Blood, 108:2020-2028, 2006), is a proliferation index
of 11 genes, of which only two
overlap with the 37-gene mTORi/HDACi classifier, suggesting the proliferation
index score would be
inadequate for predicting sensitivity to mTORi/HDACi. FIG. 30 and Table 10
show the comparison of
patients classified by the mTORi/HDACi classifiers and whether the patient has
a proliferation index score
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above or below the median for this 414 patient cohort. These findings
demonstrate the proliferation index
alone is likely insufficient for predicting mTORi/HDACi benefit. Table 11
summarizes the distribution of
the high/low risk classification using the 37-gene mTORi/HDACi classifier
among the molecular subgroups
from Zhan et al, and between the high/low proliferation index. In five poor
prognosis or proliferation
classifiers reported in MM (Zhan et al., Blood, 108:2020-2028, 2006;
Shaughnessy et al., Blood, 109:2276-
2284, 2007; Hose et al., Haematologica, 96:87-95, 2011; and Decaux et al., J.
Clin. Oncol., 26:4798-4805,
2008), none contain more than five (13.5%) overlapping genes with the
mTORi/HDACi classifier reported
here, suggesting this classifier as biologically and functionally distinct
from other classifiers (see Tables 13A
and 13B; the reference for Tables 13A and 13B are: (1) Shaughnessy et al.,
Blood. 2007;109:2276-2284; (2)
Decaux et al., J Clin Oncol. 2008;26:4798-4805; (3) Than et al., Blood.
2006;108:2020-2028; (4) Hose et
al., Haematologica. 2011;96:87-95; (5) Shaughnessy et al., Blood.
2011;118:3512-3524; (6) Whitfield et al.,
Nat Rev Cancer. 2006;6:99-106; (7) Rosenwald et al., Cancer Cell. 2003;3:185-
197; (8) Dai et al., Cancer
Res. 2005;65:4059-40661; and (9) Paik et al., N Engl J Med. 2004;351:2817-
2826). Of particular note,
Shaughnessy et al. (Blood, 109:2276-2284, 2007), specifically built an 80-gene
prognostic classifier related
to the gene expression change measured in patients treated with the proteasome
inhibitor bortezomib, and
there are no overlapping genes with the mTORi/HDACi classifier provided
herein, which supports a
mechanism of action of this combination distinct from proteasome inhibition or
generalized drug-induced
cell death.
Prognostic-linked Pharmacodynamic (PD) biomarker. As the development of the
classifier
reported here began by identifying genes which synergistically respond at the
expression level in human
MM cells treated with the mTORi/HDACi combination, the expression change of
the 37 genes included in
this classifier could be used to identify if a patient treated with the drug
combination is having a favorable
molecular response. Additionally, as this classifier is made up of genes which
expression is predictive of
overall survival, use of this classifier as a PD biomarker may prove more
clinically informative than other
PD biomarkers which only indicate target inhibition (i.e., histone acetylation
changes in response to HDACi
therapy) with no relationship to favorable clinical drug response. Use of this
classifier as a prognostically-
linked PD biomarker may beneficially inform several clinical decisions. For
example, early discontinuation
of mTORi/HDACi therapy if insufficient molecular response is measured by
analyzing gene expression
changes in the neoplasm sample with the classifier, as opposed to continuing
mTORi/HDACi therapy until
clinical or symptomatic evidence of disease progression. In another example,
the 37-gene classifier could be
used as a PD biomarker for adjusting to the optimal dose necessary to achieve
a prognostically-favorable
gene expression change. In GEP of the same MM cell line treated with the same
dose of entinostat and a
lower dose (1 nM) of sirolimus, a highly linear dose response change in gene
expression for the 37-gene
classifier was found (Pearson's correlation r = 0.98, p < 2.216, FIG. 20). As
might be expected, the lower
sirolimus dose resulted in smaller transcriptional effects (FIG. 20). As an
example, regression analyses
predicted that the gene expression value (y) may determine the sirolimus dose
(x) in the following manner:
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y = -0.563046 + 1.025323x, so that the optimal dose of sirolimus to achieve
expression of gene y could be
selected based on this regression equation. It is highly likely a similar
regression equation could be derived
for optimal HDACi dosing as well. As an example, the genes within the
mTORi/HDACi classifier are
differentially expressed when comparing healthy CD138+ plasma cells to MM
cells in a large patient and
healthy volunteer cohort (FIG. 15; Table 4). Thus, it is likely the optimal
absolute gene expression level for
all genes within the classifier could be defined by an algorithm considering
the median expression of each
gene within the classifier as measured in a sufficiently-sized cohort of
samples from the tissue type of origin
for the neoplasm being considered in healthy volunteers. These analyses
suggest that adjusting drug dosages
for individual patients, as determined by molecular profiling utilizing the
mTORi/HDACi classifier, could
be beneficial for tailored clinical management.
In support of this, additional experimental testing to further validate the
pharmacodynamic nature of
the mTORi/HDACi classifier was performed. A subset of sixteen cell lines was
selected from a large panel
of human MM cell lines for further experimental analyses. Hierarchical
clustering by median-centered
baseline expression of the 37 genes in the entire panel of human MM cell lines
is depicted as a heatmap in
FIG. 21 indicating the diverse baseline expression of this signature is also
represented among in vitro
cultured human MM cell lines. Additionally, for comparison, the differential
expression (log2 fold change)
between normal healthy donor CD138+ cells and cells from newly diagnosed or
treatment refractory MM
patients (GSE6477; Carrasco et al., Cancer Cell, 9:313-325, 2009; Chng et al.,
Cancer Res. 67:2982-2989,
2007) is also shown in FIG.21. To demonstrate that the classifier is agnostic
of the platform of gene
expression measurement, FIG. 22 shows the highly linear correlation (r=0.95; R-
squared =0.89) between the
treatment-induced gene expression fold change in L363 cells as detected by the
Affymetrix U133 plus 2.0
chip-based microarray gene expression platform versus the Nanostring0
multiplexed, barcode probe-based
mRNA detection platform which requires no amplification of mRNA. The heatmap
shown in FIG. 23 shows
the log2 fold change in expression of 19 of the 37 classifier genes in the
human MM cell line L363 treated
with 10 nM Rapamycin, 500 nm MS-275, and the combination as detected by
microarray and Nanostringa
Additionally, FIG. 23 indicates substitution of the Class I-specific HDAC
inhibitor MS-275 with the pan-
HDAC inhibitor panobinostat results in a similar pattern of gene expression
change for the classifier genes.
These findings are separately confirmed in an additional MM cell line shown in
FIG. 24. FIG. 25 shows the
log2 gene expression fold change of each gene in the classifier in response to
combination treatment in
fifteen human MM cell lines. The shaded bars indicate the expression change as
measured in the MM cell
line L363, which is highly sensitive to mTORi/HDACi treatment, and the r value
is calculated comparing the
individual cell line response to the response observed in the L363 line. The
compilation of this data (log2
fold change gene expression) in a single heatmap for all fifteen MM lines is
shown in FIG. 26. The intensity
of gene expression for each of these lines before and after mTORi/HDACi
combination treatment as
detected by Nanostring0 is shown in FIG. 27A and FIG. 27B, respectively. The
pharmocodynamic nature
of this gene expression classifier is further illustrated in FIG. 27C, where
the log2 fold change of gene
expression is shown as measured at 8, 24, and 48 hour time points after in
vitro combination treatment.
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Eleven of the classifier genes with available antibodies were tested for
change in protein expression after 48
hours of combination treatment in a panel of human MM cell lines (FIG. 28).
As a simple example, a sensitivity index algorithm based on the 37-gene
classifier to detect response
1 37
to combination treatment such as S/ = ¨37 Ei=111 92XRMI 1092XUNTI could be
used to define whether a
patient has a favorable molecular response. The sensitive and insensitive
parameters for each individual
tumor type would need to be defined within the context of a prospective
clinical trial. As an example of
applying this equation to the in vitro data collected on the Nanostring
platform, a rule for classifying future
sample was developed using 14 multiple myeloma cell lines treated with the
combination of 10 nM
rapamycin and 500 nM MS-275 for 48 hours. Cell lines were considered sensitive
to the combination
treatment if at least 50% decrease in viability was observed. The midpoint
between the means of the
sensitivity index (SI) of the two classes was determined as the threshold
value (SI=1.91) for classification of
a new sample based on expression changes in the 37 genes due to the
combination treatment. To estimate the
prediction error we used the leave-one-out cross-validation procedure Simon et
al., Journal of the National
Cancer Institute 95:14-18, 2003) and we found that 86% of the cell lines were
classified correctly (FIG. 37).
Numerous models and strategies have been developed for predictive modeling
using gene
expression data. To present more advanced examples of developing predictors of
sensitivity to the
combination treatment we also generated models based on the Compound Covariate
Predictor (CCP),
Diagonal Linear Discriminant Analysis (DLDA), Nearest Neighbor Classifications
(NNC), Nearest Centroid
Classification (NCC), and Support Vector Machines (SVM) as implemented in the
BRB-ArrayTools
(linus.nci.nih.gov/BRB-ArrayTools.html by Dr. Richard Simon and BRB-ArrayTools
Development Team).
The prediction error was estimated by 0.632+ bootstrap method of re-sampling
with default parameter of
generating 100 random training sub-sets. Using permutation test (N=1000) we
also evaluated the
significance of the cross-validated misclassification rate (significance level
alpha=0.05). Table 14 shows the
percentage of the correct classification level and the permutation p-values
for each method. Table 15 and 16
contains the algorithms and weighs or reference expression for the methods
with the correct classification
rate reaching at least 80% (linear predictors: CCP, DLDA, SVM and NCC
classification). For Table 15, the
prediction rule is defined by the inner sum of the weights (w1) and expression
(x1) of the 37 genes in the
classifier. The expression is the log ratios of combination treated vs.
untreated samples. A sample is
classified to the class Non-Sensitive if the sum is greater than the
threshold; that is, Liwi xi > threshold; The
threshold for the Compound Covariate Predictor (CCP) is -129.615. The
threshold for the Diagonal Linear
Discriminant (DLDA) predictor is -86.875; and the threshold for the Support
Vector Machine (SVN)
predictor is -3.557. For Table 16, the centroid for the Non-
sensitive/Sensitive class is a vector containing the
means of expression in the 37 genes. The expression is the log ratios of
combination treated vs. untreated
sample. The distance (d) of the expression profile for the new sample (k) to
each of the centroid (C) is
measured by Euclidean distance:
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d(k, C) = 1(xik ¨ xic.)2
where (xik)(squared distance) and (xi) are the log ratios of the 37 genes in a
sample and centroid,
respectively. The sample is predicted to belong to the class corresponding to
the nearest centroid.
Prognostically-linked PD biomarker for detection of synergistic activity.
While the 37-gene
mTORi/HDACi classifier is comprised of genes which synergistically respond to
combined treatment with
mTORi/HDACi, as a PD biomarker, it may not differentiate between patients who
are having a synergistic
favorable molecular response to both drugs in the combination and those
patients who are having an
exceedingly favorable response to only one drug with little to no benefit from
the other. To address the
clinical question of whether an individual patient treated simultaneously with
the mTORi/HDACi
combination is receiving benefit from one or both drugs, the same multivariate
predictor modeling used to
define the 37-gene signature as a prognostically-linked subset of the genes
synergistically affected by both
drugs (blue module; 126 genes input) was applied. For this analysis, the 901
genes identified in the
transcriptional co-expression network analysis (FIG. 10) as the overall drug
response network consisting of
genes affected by the both drugs in a cooperative fashion, and those
contributed by the affects of one drug
alone were used as input for the multivariate predictor modeling. Of the 901
genes, 124 genes were
identified to have expression linked to prognosis, and included in these 124
genes are all 37 genes identified
as the cooperative classifier (FIG. 19; Table 6). In one example, a neoplasm
highly sensitive to the mTOR
inhibitor, yet insensitive to the HDAC inhibitor, may be detected as having a
favorable molecular response
with the 37-gene classifier. Yet by analyzing the expression change after
initial combination treatment with
the 124 gene classifier, one could detect a lack of favorable change in the
seventy-two prognostically-
associated genes identified as contributed solely by the HDACi. With the
additional information provided by
the 124-gene mTORi/HDACi classifier in this example, a clinician may continue
treatment only with the
mTORi, thus avoiding exposing the patient who is unlikely to receive any
benefit from the HDACi to the
side-effects and associated risk of continued use of the HDACi therapy.
Example 4
Validation of Gene Signature in Multiple Cancer Types
This example illustrates the utility of determining the gene expression
signature including
expression of certain genes listed as Blue module genes in Table 6 and Table 7
above for use in the
prognosis of a broad range of cancer types. Gene expression datasets were
analyzed using the Oncomine
platform to ascertain the expression of this gene expression signature in
numerous neoplasm types, including
squamous cell lung carcinoma, cutaneous melanoma, pleomorphic liposarcoma,
colon adenoma, multiple
myeloma, papillary renal cell carcinoma, melanoma, glioblastoma, chronic
lymphocytic leukemia, invasive
breast carcinoma stroma, ovarian serous cystadenocarcinoma, invasive breast
carcinoma, glioblastoma,
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mantle cell lymphoma. Unexpectedly, the results indicate that a gene
expression signature composed of
genes within the Blue module is dysregulated in nearly all neoplasm types
analyzed.
Using the Oncomine gene expression analysis tool, a concept is an aspect of
biology represented by
a molecular signature. As shown in Table 8, 32 out of 33 genes down-regulated
in the 37-Blue module gene
expression signature were entered into Oncomine as a concept signature and
associated concepts were
identified using the default parameters for significant overlap with other
signatures (odds ratio>=2, p-
value<=le-4). The particular analysis performed, and gene signature identified
is listed on FIGs. 32A-BB.
The analyses show that there are many cancer types and specific histological
subtypes showing activation of
the predictor signature and also involvement in poor outcome (over-expression)
when a survival association
is observed.
In addition to human MM cell lines, other cell lines from other tumor types
were found to be
sensitive to combined mTORi/HDACi treatment including human mantle cell
lymphoma, human metastatic
melanoma, human Burkitt's lymphoma, and a mouse model of prostate cancer
representing aggressive,
castration- resistant disease (FIGs. 1, 33). The change of the 37-gene
classifier was also validated by
Nanostring0 assay in mTORi/HDACi treated human cell lines from breast cancer
(MCF7), Burkitt's
lymphoma (CB32), and melanoma (A375) tumor types (FIG. 34). A heatmap showing
the mean centered
gene expression of the 37-gene classifier in a large panel of human breast
cancer cell lines is shown in FIG.
35. Cell lines representing luminal and basal subtypes of breast cancer are
shown. Based on the clustering of
cell lines related to expression of the mTORi/HDACi classifier genes, it
appears unlikely that known
molecular subtype classifiers could substitute in predicting likely benefit
from treatment with
mTORi/HDACi. The synergistic activity of the combination on the classifier
genes in three different human
breast cancer cell lines is shown in FIGs. 36A-36C.
Example 5
Evaluation of Gene Expression Signature to Predict Sensitivity to
mTORi/HDACi combination Therapy
This example describes methods for evaluating a gene expression signature
including expression of
at least 6 of the 37 genes listed as Blue module genes in Table 6 and Table 7
for predicting sensitivity of a
multiple myeloma neoplasm to mTORi/HDACi combination therapy. A panel of
biological samples from
subjects having a multiple myeloma neoplasm is assembled prior to treatment of
the subjects with
mTORi/HDACi combination therapy.
The multiple myeloma neoplasm samples, and in some instances adjacent non-
neoplasm samples,
are obtained from the subjects. Approximately 1-100 1.1g of tissue is obtained
for each sample type, for
example, a bone marrow biopsy or aspirate. RNA and/or protein is isolated from
the neoplasm and non-
neoplasm tissues using routine methods (for example using a commercial kit).
The expression level of at least six (such as all 37) of ATAD2, BLM, C9orf140,
CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577,
KIAA2013,
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KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51,

RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107
is
determined by microarray analysis, Nanostring analysis or real-time
quantitative PCR (or another equivalent
method). The relative expression level of the at least six (such as all 37) of
ATAD2, BLM, C9orf140,
CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784,
Hs.202577,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3,
PHF19,
RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and
ZNF107
in the neoplasm sample is compared to a control (e.g., RNA isolated from
adjacent non-neoplasm tissue
from the subject and/or a reference value obtained from gene expression levels
in a set of neoplasms of the
same type with known outcome). Based on the increase or decrease in expression
level of each of the at least
six (such as all 37) genes, an aggregate increase or decrease of the gene
expression signature (encompassing
the at least 6 genes, such as all 37 genes) compared to the control is
calculated.
After obtaining the neoplasm sample, the subjects are administered mTORi/HDACi
combination
therapy. For example the HDACi can be LBH589 or MS-275 and the mTORi can be
RAD001 (everolimus)
or Rapamycin. For example, the subjects can be orally administered 1, 5, 10,
20, 30, 40, 50, 60, 70, 80, 90 or
100 mg/day LBH589 (Panobinostat), or more, in combination with 0.5, 1, 2, 3,
4, 5, 6, 7, 8, 9 or 10 mg/day
RAD001 (everolimus). In some example, the subject is administered the HDACi
(e.g., LBH589) on days 1,
3, 5, 15, 17 and 19 of a 28 day cycle and the mTORi (e.g., RAD001) every day
of the 28 day cycle. The
treatment outcome for each subject treated with the mTORi/HDACi combination
therapy is scored
according to known methods (e.g., survival time or progression-free survival
time) and the outcome of each
subject is correlated with the expression level of the 37 genes and/or the
aggregate increase or decrease of
the gene expression signature. A positive correlation between the expression
level of the 37 genes or
expression of the gene expression signature prior to mTORi/HDACi treatment and
improved outcome of the
subject (e.g., increased survival or increased progression free survival)
indicates that the subject is sensitive
to mTORi/HDACi combination therapy.
Example 6
Determining Sensitivity of a Neoplasm to mTORYHDACi Combination Therapy
This example describes particular methods that can be used to determine
whether a neoplasm is or is
likely to be sensitive to mTORi/HDACi combination therapy. One skilled in the
art will appreciate that
methods that deviate from these specific methods can also be used to
successfully determine sensitivity of a
neoplasm to mTORi/HDACi combination therapy.
A neoplasm sample, and in some instances adjacent non-neoplasm sample, is
obtained from the
subject. Approximately 1-100 1.1g of tissue is obtained for each sample type,
for example using a fine needle
aspirate. RNA and/or protein is isolated from the neoplasm and non-neoplasm
tissues using routine methods
(for example using a commercial kit).
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The sensitivity of a neoplasm (for example, a multiple myeloma neoplasm) to
mTORi/HDACi
combination therapy is determined by detecting in a neoplasm sample obtained
from a subject expression
levels of at least six (such as all 37) of ATAD2, BLM, C9orf140, CCNB2, CDC20,
CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22,
KIF2C, LDHA,
MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1,
SPAG5,
STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 by microarray
analysis, Nanostring
analysis or real-time quantitative PCR (or equivalent method). The relative
expression level of the at least
six (such as all 37) of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6,
CDCA3, CDCA5,
E2F2, HJURP, HLA-DPB1, Hs.193784, Hs.202577, KIAA2013, KIF22, KIF2C, LDHA,
MCM2, MCM4,
MCM5, MYBL2, NCAPH, NSDHL, PHC3, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6,
SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 in the neoplasm sample is compared to
a control (e.g.,
RNA isolated from adjacent non-neoplasm tissue from the subject and/or a
reference value obtained from
gene expression levels in a set of neoplasms of the same type with known
outcome). An increase in the
expression level of one or more of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 and/or a decrease in the expression level of one or more of
Hs.193784, Hs.202577,
HLA-DPB1, and PHC3 in the neoplasm sample relative to the control (such as an
increase or decrease of at
least about 1-fold, for example, at least about 1.5-fold, about 2-fold, about
2.5-fold, about 3-fold, about 4-
fold, about 5-fold, about 7-fold or about 10-fold) or an increase of the
overall gene expression signature as
compared to the reference value indicates that the neoplasm is sensitive to
mTORi/HDACi combination
therapy. The subject is selected for mTORi/HDACi combination therapy and can
be administered one or
more appropriate mTORi/HDACi combination therapy. Methods and therapeutic
dosages of such therapies
are known to those skilled in the art, and can be determined by a skilled
clinician.
In another example, the relative expression of proteins of the gene signature
is determined at the
protein level by methods known to those of ordinary skill in the art, such as
protein microarray, Western
blot, immunohistochemistry or immunoassay techniques. Total protein is
isolated from the neoplasm sample
and control (non-neoplasm) sample and compared using any suitable technique.
An increase in protein
expression level of one or more of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, and ZNF107 and/or a decrease in protein expression level of one or more
of Hs.193784,
Hs.202577, HLA-DPB1, and PHC3 in the neoplasm sample relative to the control
(such as an increase or
decrease of at least about 1-fold, for example, at least about 1.5-fold, about
2-fold, about 2.5-fold, about 3-
fold, about 4-fold, about 5-fold, about 7-fold or about 10-fold) or an
increase of the overall protein
expression signature as compared to the reference value indicates that the
neoplasm is sensitive to
mTORi/HDACi combination therapy. The subject is selected for mTORi/HDACi
combination therapy and
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can be administered one or more appropriate mTORi/HDACi combination therapy.
Methods and therapeutic
dosages of such therapies are known to those skilled in the art, and can be
determined by a skilled clinician.
Example 7
Determination of clinically-beneficial response to treatment with mTORi/HDACi
This example describes particular methods that can be used to determine if a
neoplasm in a subject
is likely to respond to HDACi/mTORi therapy after therapy has been initiated,
but before a physical
indication of response (for example, reduction of tumor burden) could be
detected. One skilled in the art will
appreciate that methods that deviate from these specific methods can also be
used to successfully determine
the responsiveness of the neoplasm to the HDACi/mTORi therapy.
A neoplasm sample, and in some instances adjacent non-neoplasm sample, is
obtained from the
subject before and after initiation of HDACi/mTORi therapy treatment (for
example, 8 hours, 12 hours, 1
day, 2 days, 3 days, 4 days, 5 days, six days, 1 week, 2 weeks, 3 weeks or 4
weeks following initiation of
treatment). Approximately 1-1001.1g of tissue is obtained for each sample
type, for example using a fine
needle aspirate. RNA and/or protein is isolated from the neoplasm and non-
neoplasm tissues using routine
methods (for example using a commercial kit).
The sensitivity of the neoplasm to HDACi/mTORi therapy (for example, a
multiple myeloma
neoplasm) is determined by detecting expression levels of ATAD2, BLM,
C9orf140, CCNB2, CDC20,
CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2,
MCM4,
MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, ZNF107, Hs.193784, Hs.202577, HLA-DPB1, and PHC3
in the both
the sample obtained from the subject before and after initiation of
HDACi/mTORi therapy by microarray
analysis. The normalized expression level of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A, CDC6,
CDCA3, CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5,
MYBL2,
NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48,
TRIP13, UBE2C, ZNF107, Hs.193784, Hs.202577, HLA-DPB1, and PHC3 in the
neoplasm sample taken
after initiation of HDACi/mTORi therapy is compared to a control (e.g., the
normalized expression level of
these genes in the neoplasm sample taken prior to HDACi/mTORi therapy).
An increase in expression of one or more of (such as all of) ATAD2, BLM,
C9orf140, CCNB2,
CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP, KIF22, KIF2C, LDHA, MCM2,
MCM4,
MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1,
TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or a decrease in protein
expression level of one or
more of (such as all of) Hs.193784, Hs.202577, KIAA2013, HLA-DPB1, and PHC3 in
the neoplasm sample
relative to the control (such as an increase or decrease of at least about 1-
fold, for example, at least about
1.5-fold, about 2-fold, about 2.5-fold, about 3-fold, about 4-fold, about 5-
fold, about 7-fold or about 10-fold)
or an increase of the overall gene expression signature as compared to the
control indicates that the
neoplasm is responsive to the HDACi/mTORi therapy.
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In another example, the relative expression of proteins of the gene signature
is determined at the
protein level by methods known to those of ordinary skill in the art, such as
protein microarray, Western
blot, or immunoassay techniques. Total protein is isolated from the neoplasm
sample and control (non-
neoplasm) sample and compared using any suitable technique. An increase in
protein expression of one or
more of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2,
HJURP,
KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2,

SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or
a decrease
in protein expression level of one or more of Hs.193784, Hs.202577, KIAA2013,
HLA-DPB1, and PHC3 in
the neoplasm sample relative to the control (such as an increase or decrease
of at least about 1-fold, for
example, at least about 1.5-fold, about 2-fold, about 2.5-fold, about 3-fold,
about 4-fold, about 5-fold, about
7-fold or about 10-fold) or an increase of the overall protein expression
signature as compared to the
reference value indicates a poor prognosis, such as a decrease in the
likelihood of survival, progression free
survival and/or metastasis-free survival, for the subject.
Example 8
Determination of synergistic response to treatment with mTORi/HDACi
Since patients will be treated with the combination simultaneously, the 124-
gene signature including
gene expression upregulation and downregulation as listed in column (2) of
Table 6 will allow for detection
of synergy of mTORi/HDACi therapy. This will allow sparing patients who are
only responding to one arm
of the therapy from unbeneficial treatment with the other drug (thus avoiding
side effects of that drug). In
one example, a neoplasm highly sensitive to the mTOR inhibitor, yet
insensitive to the HDAC inhibitor may
be detected as having a favorable molecular response with the 37-gene
classifier. Yet by analyzing the
expression change after initial combination treatment with the 124 gene
classifier, one could detect a lack of
favorable change in the seventy-two prognostically- associated genes
identified as contributed solely by the
HDACi. With the additional information provided by the 124-gene mTORi/HDACi
classifier in this
example, a clinician may continue treatment only with the mTORi, thus avoiding
exposing the patient who is
unlikely to receive any benefit from the HDACi to the side-effects and
associated risk of continued use of
the HDACi therapy.
Example 9
Determining Prognosis of a Subject with a Neoplasm
This example describes particular methods that can be used to determine a
prognosis for a subject
diagnosed with a neoplasm. One skilled in the art will appreciate that methods
that deviate from these
specific methods can also be used to successfully determine the prognosis of a
subject with a neoplasm.
A neoplasm sample, and in some instances adjacent non-neoplasm sample, is
obtained from the
subject. Approximately 1-100 1.1g of tissue is obtained for each sample type,
for example using a fine needle
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aspirate. RNA and/or protein is isolated from the neoplasm and non-neoplasm
tissues using routine methods
(for example using a commercial kit).
The prognosis of a neoplasm (for example, a multiple myeloma neoplasm) is
determined by
detecting expression levels of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A,
CDC6, CDCA3,
CDCA5, E2F2, HJURP, KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH,
NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48,
TRIP13,
UBE2C, ZNF107, Hs.193784, Hs.202577, HLA-DPB1, and PHC3 in a neoplasm sample
obtained from a
subject by microarray analysis, Nanostring or real-time quantitative PCR. The
relative expression level of
ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2, HJURP,
KIAA2013, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19,
RAD51,
RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, ZNF107,
Hs.193784,
Hs.202577, HLA-DPB1, and PHC3 in the neoplasm sample is compared to a control
(e.g., RNA isolated
from adjacent non-neoplasm tissue from the subject and/or a reference value
obtained from gene expression
levels in a set of neoplasms of the same type with known outcome).
An increase in expression of one or more of ATAD2, BLM, C9orf140, CCNB2,
CDC20, CDC25A,
CDC6, CDCA3, CDCA5, E2F2, HJURP, KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2,
NCAPH, NSDHL, PHF19, RAD51, RRM2, SLC19A1, SPAG5, STK6, SUV39H1, TACC3,
TMEM48,
TRIP13, UBE2C, and ZNF107 and/or a decrease in protein expression level of one
or more of Hs.193784,
Hs.202577, KIAA2013, HLA-DPB1, and PHC3 in the neoplasm sample relative to the
control (such as an
increase or decrease of at least about 1-fold, for example, at least about 1.5-
fold, about 2-fold, about 2.5-
fold, about 3-fold, about 4-fold, about 5-fold, about 7-fold or about 10-fold)
or an increase of the overall
gene expression signature as compared to the reference value indicates a poor
prognosis, such as a decrease
in the likelihood of survival, progression free survival and/or metastasis-
free survival, for the subject.
In another example, the relative expression of proteins of the gene signature
is determined at the
protein level by methods known to those of ordinary skill in the art, such as
protein microarray, Western
blot, or immunoassay techniques. Total protein is isolated from the neoplasm
sample and control (non-
neoplasm) sample and compared using any suitable technique. An increase in
protein expression of one or
more of ATAD2, BLM, C9orf140, CCNB2, CDC20, CDC25A, CDC6, CDCA3, CDCA5, E2F2,
HJURP,
KIF22, KIF2C, LDHA, MCM2, MCM4, MCM5, MYBL2, NCAPH, NSDHL, PHF19, RAD51, RRM2,
SLC19A1, SPAG5, STK6, SUV39H1, TACC3, TMEM48, TRIP13, UBE2C, and ZNF107 and/or
a decrease
in protein expression level of one or more of Hs.193784, Hs.202577, KIAA2013,
HLA-DPB1, and PHC3 in
the neoplasm sample relative to the control (such as an increase or decrease
of at least about 1-fold, for
example, at least about 1.5-fold, about 2-fold, about 2.5-fold, about 3-fold,
about 4-fold, about 5-fold, about
7-fold or about 10-fold) or an increase of the overall protein expression
signature as compared to the
reference value indicates a poor prognosis, such as a decrease in the
likelihood of survival, progression free
survival and/or metastasis-free survival, for the subject.
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In view of the many possible embodiments to which the principles of the
disclosed embodiments
may be applied, it should be recognized that the illustrated embodiments are
only preferred examples of the
embodiments and should not be taken as limiting. Rather, the scope of the
embodiments is defined by the
following claims. We therefore claim all that comes within the scope and
spirit of these claims.
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Table 1. The Combination Index (CI) values indicate rapamycin and MS-275 drug
synergy in 88% of
MM cell lines tested.
Cell line Rapamycin (nM) MS-275 ( M)
Dose Effect* CI**
1 0.5 0.406 0.299
1 KMS-12BM
0.5 0.447 0.248
1 0.5 0.219 0.083
2 KMS18
10 0.5 0.273 0.155
1 0.5 0.936 0.271
3 L363
10 0.5 0.949 0.280
1 0.5 0.146 0.710
4 8226
10 0.5 0.293 0.5
1 0.5 0.505 1.646
5 FR-4
10 0.5 0.579 1.598
1 0.5 0.776 0.495
6 JK-6L
10 0.5 0.836 0.479
1 0.5 0.868 1.025
7 ANBL-6
10 0.5 0.922 0.260
1 0.5 0.831 0.660
8 FLAM-76
10 0.5 0.864 0.570
1 0.5 0.929 0.208
9 XG-6
10 0.5 0.954 0.154
1 0.5 0.618 0.139
10 U266
10 0.5 0.661 0.240
1 0.5 0.694 1.4
11 OCI-MY5
10 0.5 0.719 5.09
1 0.5 0.695 0.411
12 LP-1
10 0.5 0.751 0.344
1 0.5 0.743 0.928
13 MM-Ml
10 0.5 0.766 0.846
1 0.5 0.664 0.632
14 OCI-MY1
10 0.5 0.681 0.605
1 0.5 0.358 0.628
SKMM-1
10 0.5 0.473 0.543
1 0.5 0.609 0.548
16 SACHI
10 0.5 0.635 0.557
1 0.5 0.415 0.352
17 EJM
10 0.5 0.461 0.387
1.0 0.5 0.78 0.095
107403 (PCT) 10.0 0.5 0.80 0.077
100.0 0.5 0.82 0.101
1.0 0.5 0.683 0.579
M0PC265 (PCT) 10.0 0.5 0.755 0.563
100.0 0.5 0.805 0.799
1.0 0.5 0.596 0.865
MOPC460 (PCT) 10.0 0.5 0.634 0.812
100.0 0.5 0.742 0.699
*The dose effect is the proportion of viable cells. **CI <0.1 (very strong
synergism); CI = 0.1 -0.3
(strong synergism); CI = 0.3 - 0.85(synergism); CI = 0.85 - 0.9 (slightly
synergism); CI = 1 (additive).
-84-

Table 2 (5 pages). Functionally-related genes determined using gene ontology
(GO) terms.
# of Mean.kIN Mean Fold
Hub Genes # 0
GO Rep. GO Term P-Value FDR (%)
tµ.)
Terms sc Change
DOWN-regulated UP-regulated of Hubs z;
Blue Module 'a
--4
_______________________________________________________________________________
________________________________________________ 1-
CDC25A, CDC25C, KIF22, MCM2,
tµ.)
.6.
3.7e-10 - (-4.5)
- --4
BP DNA replication 10 2.8e-06 - 3.8 0.58 - 0.66
MCM4, RAD51, RBM14, RFC2, 10121
0.0016 (-2.93)
RRM2, TIMELESS
CCNB2, CDC25A, CDC25C, CDCA3,
CDCA5, CIT, DBF4B, E2F2, ESPL1,
BP cell cycle 1 1.70E-18 6.60E-14 0.67 (-
3.66) FOXMl, HJURP, KIF22, KIF2C,
21134
MCM2, MKI67, NCAPH, PLK1,
0
RAD51, SPAG5, 5PC24, TIMELESS
_______________________________________________________________________________
_____________________ 0
I.)
chromosome
co
BP 1 0.00023 0.89 0.68 (-
3.55) CDCA5, ESPL1, HJURP, NCAPH
416 in
a,
segregation
c7,
.
c7,
coin
microtubule-based
ESPL1, KIF22, KIF2C, SPAG5,
BP 1 0.00031 1.0 0.68 (-
3.25) 619 I.)
process
TUBA1B, TUBA1C 0
_______________________________________________________________________________
__________________________________________________ H
FP
CCNB2, CDC25A, CDC25C, CDCA3,
1
0
BP cell division 1 1.30E-11 1.10E-07 0.69 (-
3.77) CDCA5, CIT, ESPL1, NCAPH, PLK1,
12118 in
1
0
SPAG5, 5PC24, TIMELESS
in
antigen processing
HLA-DMA, HLA-
and presentation
DPB1, HLA-
BP of peptide or poly- 1 0.00017 0.69 0.71
(+3.89) DQB1, (HLA- 414
saccharide antigen
DRB1, HLA-
via MHC class II
DRB4) Iv
_______________________________________________________________________________
________________________________________________ n
,-i
CCNB2, CDC25A, CDC25C, CDCA3,
CDCA5, CIT, E2F2, ESPL1, HJURP,
cp
tµ.)
=
sister chromatid 1.4e-18 - (-
3.72) - KIF22, KIF2C, MCM2, MKI67,
tµ.)
BP 25 1.1e-13 - 5 0.56 - 0.71
HLA-DMA 25141 ,--E-,
segregation 0.0025 (-
2.74) NCAPH, PLK1, RAD51,
RBM14, c:
.6.
RRM2, SCARB1, SPAG5, 5PC24,
c:
TIMELESS, TUBA1B, TUBA1C
r

# of Mean.kIN Mean Fold
Hub Genes # I
GO Rep. GO Term P-Value FDR (%)
Terms sc Change
DOWN-regulated UP-regulated of Hubs g
CDC25A, CDC25C, E2F2, HJURP,
t..)
o
1-,
1.5e-05 - (-3.92) -
LMNB1, MCM2, MCM4, MKI67, c,.)
CC nuclear lumen 3 0.034 - 1.6
0.58 - 0.64 13125 --='-'
0.0015 (-2.28) PLK1, RAD51, RBM14, RFC2,
--4
1-,
t..)
TIMELESS
.6.
--4
CCNB2, CDCA5, CENPM, ESPL1,
HJURP, KIF22, KIF2C, LMNB1,
non-membrane-
MCM2, MCM4, MKI67, NCAPH,
CC 1 0.00012 0.19 0.66 (-3.12)
21133
bounded organelle
PLK1, RAD51, RBM14, RFC2,
SPAG5, 5PC24, TIMELESS,
TUBA1B, TUBA1C
0
CCNB2, CDCA5, CENPM, ESPL1,
0
I.)
HJURP, KIF22, KIF2C, LMNB1,
co
ul
a,
microtubule 1.7e-12 - (-3.92) -
MCM2, MCM4, MKI67, NCAPH, (5)
,
co CC 4 1.5e-08 - 3.5
0.62 - 0.67 21133
(5)
in
T cytoskeleton 0.0035 (-3.11)
PLK1, RAD51, RBM14, RFC2, I.)
0
SPAG5, 5PC24, TIMELESS,
H
FP
1
TUBA1B, TUBA1C
0
ul
1
CDCA5, CENPM, HJURP, KIF22,
0
ul
5.8e-13 - (-3.97) -
KIF2C, MCM2, MKI67, NCAPH,
CC kinetochore 10 1.1e-08 - 4.2
0.53 - 0.7 13122
0.0045 (-2.21) RAD51, RFC2, SPAG5, 5PC24,
TIMELESS
HLA-DMA, HLA-
DPB1, HLA-
MHC protein 5.1e-05 -
Iv
CC 2 0.1 - 1.6 0.71 - 0.71 (+3.89) -
(+3.89) DQB1, (HLA- 414 n
complex 0.0015
DRB1, HLA-
cp
DRB4)
t..)
o
_______________________________________________________________________________
________________________________________________ 1-,
t..)
c,
.6.
c,
vD
c,.)

# of Mean.kIN Mean Fold
Hub Genes # I
GO Rep. GO Term P-Value FDR (%)
Terms sc Change
DOWN-regulated UP-regulated of Hubs g
HLA-DMA, HLA-
t..)
o
1-,
DPB1, HLA-
c,.)
MHC class II
MF 1 4.60E-05 1.0 0.71 (+3.89)
DQB1, (HLA- 414 1 2
receptor activity
t..)
DRB1, HLA-
.6.
--4
DRB4)
Orange Module
C3AR1, ELOVL3,
ENPP1, ESAM,
integral to 0.00055 - (+0.467) -
CC 2 4.1 - 4.7 0.8 - 0.8
ADAM23 GALNT10, 9120
membrane 0.00097 (+0.467)
LAMP3,
n
SEMA4F, STOM
0
I.)
co
Darkgreen Module
a,
DNA metabolic
(5)
(5)
co BP 1 1.80E-05 1.3 0.56 (-1.67)
DNMT3A, LIG3, PARP1, SSRP1 NFIA 5119 ul
process
I.)
_______________________________________________________________________________
__________________________________________________ 0
C20orf7, CENPV, DNMT3A, FKBP4,
H
macro-molecular (-1.9) -
a,
1
BP 4 le-05 - 0.00018 1.4- 5
0.56 -0.6 GEMIN4, HMGN2, IP011, PARP1,
APC2 11133 0
complex assembly (-1.62)
ul
,
TSR1, TUBB
0
ul
ADA, AHSA1, BID, CIDEB, CTPS,
CC cytosol 1 0.0005 1.7 0.56 (-1.66)
GEMIN4, NTRK2, ODC1, PSMD8, 10133
TUBB
ACO2, DDX54, DNMT3A, FKBP4,
GEMIN4, KEAP1, LAS1L, LIG3,
membrane-
Iv
CC 1 3.30E-06 0.03 0.59 (-1.75)
MIPEP, NOL12, NVL, PARP1, PDIA5, TBX19 20148 r)
enclosed lumen
1-i
POLR3H, PSMD8, SSRP1, THOC4,
cp
TSR1, WDR4
t..)
o
_______________________________________________________________________________
________________________________________________ 1-,
t..,
-a
c.,
.6.
c.,
,,,

# of
Mean.kIN Mean Fold Hub Genes # I
GO Rep. GO Term P-Value FDR (%)
Terms sc Change
DOWN-regulated UP-regulated of Hubs g
ALDOA, CENPV, DDX54, DNMT3A,
t..)
o
1-,
FKBP4, GEMIN4, HMGN2, KEAP1,
-a-,
non-membrane-
LAS1L, NOL12, NTRK2, NVL, --4
1-,
CC 1 0.00021 0.84 0.59 (-1.54)
APC2, TBX19 23153 t=-)
bounded organelle
PARP1, PSMD8, RCC1, SSRP1, .6.
--4
STAG3L4, STOML2, TRIB2, TSR1,
TUBB
ALDOA, CENPV, DDX54, DNMT3A,
FKBP4, GEMIN4, HMGN2, KEAP1,
intracellular non-
LA51L, NOL12, NTRK2, NVL,
CC membrane- 1 0.00021 0.84 0.59 (-1.54)
APC2, TBX19 23153
PARP1, PSMD8, RCC1, SSRP1,
0
bounded organelle
STAG3L4, STOML2, TRIB2, TSR1,
.0
I.)
TUBB
co
u-,
.1,
BID, C20orf7, IP011, PARP1,
(5)
(5)
co CC envelope 1 0.0012 2.9 0.60
(-1.72) 7119
co
SLC25A33, STOML2, TOMM4OL ____________________________ I.)
.0
ACO2, ACP6, BID, C20orf7, IP011,
H
8.5e-05 - (-1.72) -
.1,
,
CC mitochon-drion 5 0.47 - 3.8 0.57
- 0.6 MIPEP, PARP1, SLC25A33,
10131 .0
0.0017 (-1.38)
,
STOML2, TOMM4OL
.0
u-,
ACO2, DDX54, DNMT3A, FKBP4,
GEMIN4, KEAP1, LAS1L, LIG3,
(-1.8) -
CC nuclear lumen 4 9.9e-07 - le-04
0.027 - 0.47 0.59 - 0.61 MIPEP, NOL12, NVL, PARP1, PDIA5, TBX19 20148
(-1.75)
POLR3H, PSMD8, SSRP1, THOC4,
TSR1, WDR4
_______________________________________________________________________________
______________________________________________ Iv
Springgreen Module
n
_______________________________________________________________________________
______________________________________________ ,-i
ASAP3, CHN1,
cp
F1110357,
t..)
o
GTPase regulator 0.0028 -
(+0.948) - 1-,
MF 3 6e-08 - 2.8e-07 0.55 - 0.56
TIAM2 RABGAP1L, 9126
activity 0.0044 (+1.14)
c:
RASA2, RASAL2,
.6.
c:
SYTL3, TIAM1
vD
w
_______________________________________________________________________________
______________________________________________ ,

# of Mean.kIN Mean Fold
Hub Genes # I
GO Rep. GO Term P-Value FDR (%)
Terms sc Change
DOWN-regulated UP-regulated of Hubs g
EPB41L5, GSN, JUP,
t..)
o
cytoskeletal
1-,
MF 1 0.00025 2.9 0.56 (+1.48)
MAPT, MYH11, 7123 '"
protein binding
-a-,
OBSL1, TNNT1
--4
1-,
_______________________________________________________________________________
________________________________________________ t..)
Red Module .6.
--4
ABHD12, C19orf63, CD320,
CD79B, CLN6, DHCR7, IL21R,
integral to 1.1e-05 - 2.9e- (-1.9) -
CC 2 0.16 - 0.21 0.71 - 0.71
PIGU, SCAMP3, SCNN1B, RASGRP3 15135
membrane 05 (-1.9)
SLC37A4, SLC7A11, SSR2,
TMEM109
0
endomem-
DHCR7, PIGU, SCAMP3, 0
CC 1 0.00021 1.0 0.72 (-1.96)
6112 "
co
brane system
SLC37A4, SSR2, TMEM109 ul
a,
(5)
.
(5)
co
ul
I.)
0
H
FP
I
0
Ui
I
0
Ui
IV
n
,-i
cp
t..,
=
t..,
-a-,
c.,
.6.
c.,
,,,

Table 3 (18 pages). Detailed functional enrichment findings for the five drug-
related gene expression modules.
REVIGO results DAVID results
GE in Combination vs. Control _______ 0
tµ.)
P-value Mean o
Mean GO FDR Fold
Genes UP- 1-,
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated
score
Change --4
1-,
t=.)
Blue Module
.6.
--4
GO: sister chromatid
BP 0 0.7127 819 0 2.1E-03 4.4E-02
15.36 4 -3.72 CDCA5, ESPL1, NCAPD3, NCAPH
0000819 segrt'n
CCNB2, CDC20, CDC25A,
CDC25C, CDC6, CDCA3, CDCA5,
CIT, ESPL1, H2AFX, KIF22, KIF2C,
GO:
BP M phase 0.777 0.6804 819 1 1.4E-18 1.1E-15 10.96 25 -3.63
MKI67, NCAPD3, NCAPH, PLK1, 0
0000279
RAD51, RAD54L, SKA3, SPAG5,
0
I.)
co
5PC24, TACC3, TIMELESS,
ul
a,
TRIP13, UBE2C
c7,
c7,
ul
F
CCNB2, CDC20, CDC25A, I.)
0
CDC25C, CDC6, CDCA3, CDCA5,
H
GO:
a,
1
BP nuclear division 0.99 0.7015 819 1 7.5E-15 1.1E-12
12.38 19 -3.69 CIT, ESPL1, KIF22, KIF2C,
0
0000280
ul
1
NCAPD3, NCAPH, PLK1, SKA3,
0
ul
SPAG5, 5PC24, TIMELESS, UBE2C
mitotic sister
GO:
BP chromatid 0.818 0.7127 819 1 2.0E-03 4.3E-02 15.79 4 -3.72
CDCA5, ESPL1, NCAPD3, NCAPH
0000070
segregation
GO: M phase of meiotic
ESPL1, H2AFX, MKI67, PLK1,
BP 0.997 0.6393 819 1 5.1E-05 2.4E-
03 10.47 7 -3.67 Iv
0051327 cell cycle
RAD51, RAD54L, TRIP13 n
CCNB2, CDC20, CDC25A,
cp
CDC25C, CDC6, CDCA3, CDCA5,
tµ.)
o
GO:
BP mitosis 0.942 0.7015 819 1 7.5E-15 1.1E-
12 12.38 19 -3.69 CIT, ESPL1,
KIF22, KIF2C, tµ.)
'a
0007067
c:
NCAPD3, NCAPH, PLK1, SKA3,
.6.
c:
SPAG5, 5PC24, TIMELESS, UBE2C
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value
Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name
Dispensability kINõ select filter EASE Benjamini Enrichment n Fold
Genes DOWN-regulated
64
score
Change regulated1-,
GO:
ESPL1, H2AFX, MKI67, PLK1, 'a
BP meiosis 0.899 0.6393 819 1 5.1E-
05 2.4E-03 10.47 7 -3.67 --4
1-,
0007126
RAD51, RAD54L, TRIP13 t.)
_______________________________________________________________________________
_______________________________________________ .6.
BLM, CCNB2, CDC20, CDC25A, --4
CDC25C, CDC6, CDCA3, CDCA5,
CIT, ESPL1, H2AFX, KIF22, KIF2C,
GO:
BP cell cycle phase 0.935 0.6713
819 1 2.3E-17 5.9E-15 9.03 26 -3.61 MKI67, NCAPD3, NCAPH,
PLK1,
0022403
RAD51, RAD54L, SKA3, SPAG5,
5PC24, TACC3, TIMELESS,
0
TRIP13, UBE2C
_______________________________________________________________________________
_________________________________________________ 0
CCNB2, CDC20, CDC25A,
"
co
ul
CDC25C, CDC6, CDCA3, CDCA5, a,
GO: M phase of mitotic
0,
BP 0.948 0.7015 819 1 1.0E-14 1.3E-12
12.16 19 -3.69 CIT, ESPL1, KIF22, KIF2C,
0,
ul
0000087 cell cycle
.
NCAPD3, NCAPH, PLK1, SKA3, I.)
0
H
SPAG5, 5PC24, TIMELESS, UBE2C
a,
1
0
antigen processing
ul
HLA-DMA, (1)
and pres-entation
ul
HLA-DPB1,
GO: of peptide or
BP 0 0.7123 2504 0 1.7E-04 6.9E-03 35.52 4 3.89 HLA-DQB1,
0002504 polysaccharide
(HLA-DRB1,
antigen via MHC
HLA-DRB4)
class II
CCNB2, CDC20, CDC25A,
Iv
n
CDC25C, CDC6, CDCA3, CDCA5,
GO:
BP cell division 0.018 0.693651301 0 1.3E-11 1.1E-09
8.79 18 -3.77 CIT, ESPL1, MCM5,
NCAPD3, cp
0051301
t.)
NCAPH, PLK1, SKA3, SPAG5,
o
1-,
t.)
5PC24, TIMELESS, UBE2C
'a
_______________________________________________________________________________
_______________________________________________ c:
.6.
GO: chromo-some
CDCA5, ESPL1, HJURP, NCAPD3, c:
BP 0.023 0.6808 7059 0 2.3E-04 8.9E-03
10.66 6 -3.55
0007059 segregation
NCAPH, SKA3

REVIGO results DAVID results
GE in Combination vs. Control
P-value
Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name
Dispensability kINõ select filter EASE Benjamini Enrichment n Fold
Genes DOWN-regulated
64
score
Change regulated1-,
CENPA, ESPL1, KIF22, KIF2C,
'a
GO: micro-tubule-based
--4
1-,
BP 0.024 0.682 7017 0 3.1E-04 1.0E-02
5.20 9 -3.25 SPAG5, TACC3, TUBA1B,
t.)
0007017 process
.6.
TUBA1C, UBE2C
--4
BLM, CCNB2, CDC20, CDC25A,
CDC25C, CDC6, CDCA3, CDCA5,
CENPA, CHAF1B, CIT, DBF4B,
E2F2, ESPL1, FOXMl, H2AFX,
GO:
BP cell cycle 0.027 0.6677 7049 0 1.7E-18 6.6E-16
6.35 34 -3.66 HJURP, KIF22, KIF2C, MCM2,
0007049
n
MKI67, NCAPD3, NCAPH, PLK1,
0
RAD51, RAD54L, SKA3, SPAG5,
K)
co
ul
5PC24, SUV39H1, TACC3,
a,
c7,
TIMELESS, TRIP13, UBE2C
c7,
ul
i
I.)
BLM, CDC25A, CDC25C, CDC6,
0
H
GO:
CHAF1B, MCM10, MCM2, MCM4, a,
BP DNA replication 0.031 0.636 6260 0 3.7E-10 2.8E-08
10.87 14 -4.50 1
0
0006260
MCM5, POLA2, RAD51, RBM14, ul
1
RFC2, RRM2
0
ul
double-strand
GO: break repair via
BP 0.409 0.577 724 0 2.9E-04 1.0E-02
29.91 4 -2.93 BLM, H2AFX, RAD51, RAD54L
0000724 hom*olo-gous
recombina-tion
BLM, CDC25A, CDC25C, CDC6,
Iv
n
CHAF1B, FANCG, H2AFX, KIF22,
GO: DNA metabolic
BP 0.422 0.6165 6259 0 9.1E-10 6.3E-08
5.75 20 -4.10 MCM10, MCM2,
MCM4, MCM5, cp
0006259 process
tµ.)
POLA2, RAD18, RAD51, RAD54L,
o
1-,
tµ.)
RBM14, RFC2, RRM2, TRIP13
'a
_______________________________________________________________________________
________________________________________________ c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1¨,
BLM, CENPA, CHAF1B, E2F2,
'a
--4
GO: macromolecular
H2AFX, HJURP, MCM2, RAD51,
t.)
BP 0.457 0.6483 65003 0 1.6E-03 3.9E-02
2.87 13 -3.37 HLA-DMA 4'
0065003 complex assembly
RRM2, TMEM48, TUBA1B, --4
TUBA1C
cellular macro-
CENPA, CHAF1B, H2AFX, HJURP,
GO: molecular complex
BP 0.944 0.6397 65003 1 2.5E-03 5.0E-02
3.77 9 -3.16 KIF2C, MCM2, TMEM48,
0034621 subunit organiza-
TUBA1B, TUBA1C
tion
0
GO: recombi-national
BP 0.467 0.577 725 0 2.9E-04 1.0E-02
29.91 4 -2.93 BLM, H2AFX, RAD51, RAD54L o
0000725 repair
"
co
ul
GO: DNA-dependent
BLM, MCM2, MCM4, MCM5, a,
BP 0.538 0.6607 6261 0 7.0E-04 2.0E-02
12.25 5 -3.99 c7,
0006261 DNA replication
RAD51 c7,
ul
c..,)
BLM, CCNB2, CDC20, CDC25A,
I.)
o
H
CDC25C, CDC6, CDCA3, CDCA5,
a,
1
0
GO:
CENPA, CIT, ESPL1, KIF22, ul
BP mitotic cell cycle 0.551
0.6792 278 0 5.2E-13 5.0E-11 8.15 21 -3.66
1
0
0000278
KIF2C, NCAPD3, NCAPH, PLK1, ul
SKA3, SPAG5, 5PC24, TIMELESS,
UBE2C
CCNB2, CDC20, CDC25A,
CDC25C, CDC6, CDCA3, CDCA5,
GO:
BP organelle fission 0.573
0.7015 48285 0 1.5E-14 1.7E-12 11.89 19 -3.69 CIT,
ESPL1, KIF22, KIF2C, Iv
0048285
n
NCAPD3, NCAPH, PLK1, SKA3,
SPAG5, 5PC24, TIMELESS, UBE2C
cp
_______________________________________________________________________________
________________________________________________ t.)
o
BLM, CDCA5, CENPA, CHAF1B,
t.)
GO: chromo-some
ESPL1, H2AFX, HJURP, MCM2, 'a
BP 0.755 0.6027 48285 1 1.9E-05 1.1E-03
4.27 14 -2.74 SATB1 .12
0051276 organiza-tion
NCAPD3, NCAPH, RAD54L, c:
RBM14, SUV39H1

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change
GO: nucleo-some
CENPA, CHAF1B, H2AFX, HJURP, 'a
BP 0.926 0.5585 48285 1 2.1E-03 4.5E-02
9.11 5 -3.39 --4
1¨,
0034728 organiza-tion
MCM2 t.)
_______________________________________________________________________________
________________________________________________ .6.
--4
chromatin
GO:
CENPA, CHAF1B, H2AFX, HJURP,
BP assembly or dis- 0.925 0.5751 48285 1 1.1E-03 2.9E-02
7.61 6 -3.20
0006333 MCM2, SUV39H1
assembly
GO: nucleo-some
CENPA, CHAF1B, H2AFX, HJURP,
BP 0.82 0.5585 48285 1 1.3E-03 3.5E-02
10.29 5 -3.39
0006334 assembly
MCM2
GO: chromatin
CENPA, CHAF1B, H2AFX, HJURP,
BP 0.96 0.5585 48285 1 1.6E-03 3.9E-02
9.87 5 -3.39 0
0031497 assembly
MCM2
_______________________________________________________________________________
__________________________________________________ 0
GO: protein-DNA
CENPA, CHAF1B, H2AFX, HJURP, "
co
BP 0.997 0.5585 48285 1 1.9E-03 4.4E-02
9.35 5 -3.39 ul
0065004 complex assembly
MCM2 a,
c7,
GO:
ESPL1, H2AFX, MKI67, PLK1, c7,
ul
-1. BP meiotic cell cycle 0.605
0.6393 51321 0 5.7E-05 2.6E-03 10.25 7 -3.67
I.)
0051321
RAD51, RAD54L, TRIP13 0
_______________________________________________________________________________
__________________________________________________ H
CDCA5, CENPA, CHAF1B,
a,
I
GO:
0
BP DNA packaging 0.61 0.6197 6323 0 6.3E-06 4.0E-04
11.25 8 -3.53 H2AFX, HJURP, MCM2, NCAPD3, ul
,
0006323
0
NCAPH
ul
micro-tubule
GO:
CENPA, ESPL1, KIF2C, SPAG5,
BP cytoskele-ton 0.649 0.6747 226 0 4.8E-04 1.5E-02
6.95 7 -3.42
0000226 TACC3, TUBA1B, UBE2C
organiza-tion
BLM, CDC25A, CDC25C, CDC6,
GO: regulation of cell
BP 0.652 0.6467 51726 0 8.6E-05 3.6E-03
4.79 11 -3.58 E2F2, ESPL1, FANCG, H2AFX,
Iv
0051726 cycle
n
TACC3, TIMELESS, UBE2C
GO: DNA recombina-
BLM, H2AFX, RAD51, RAD54L, cp
t.)
BP 0.653 0.593 6310 0 6.5E-04 2.0E-02
8.52 6 -3.45 o
0006310 tion
RBM14, TRIP13
t.)
'a
BLM, CHAF1B, FANCG, H2AFX,
c:
GO:
.6.
BP DNA repair 0.663 0.5778 6281 0 2.2E-05 1.2E-03
5.62 11 -3.03 KIF22, RAD18,
RAD51, RAD54L, c:
0006281
RBM14, RFC2, TRIP13

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
BLM, CHAF1B, FANCG, H2AFX, -c-:--,
GO: cellular response to
--4
1-,
BP 0.764 0.581 6281 1 1.6E-03 3.8E-02
3.06 12 -2.96 KIF22, RAD18, RAD51, RAD54L,
t.)
0033554 stress
.6.
RBM14, RFC2, TIMELESS, TRIP13
--4
BLM, CHAF1B, FANCG, H2AFX,
GO: response to DNA
BP 0.957 0.581 6281 1 4.6E-05 2.3E-03 4.65 12 -2.96 KIF22,
RAD18, RAD51, RAD54L,
0006974 damage stimulus
RBM14, RFC2, TIMELESS, TRIP13
GO: double-strand
BLM, H2AFX, RAD51, RAD54L,
BP 0.669 0.5762 6302 0 9.0E-04 2.5E-02
11.46 5 -3.51
0006302 break repair
TRIP13
0
BLM, CCNB2, CDC20, CDC25A,
0
CDC25C, CDC6, CDCA3, CDCA5, "
co
ul
CENPA, CIT, ESPL1, H2AFX,
a,
GO:
c7,
BP cell cycle process 0.68
0.664622402 0 3.6E-15 7.0E-13 6.87 27 -3.61 KIF22, KIF2C,
MKI67, NCAPD3, c7,
ul
ul 0022402
.
NCAPH, PLK1, RAD51, RAD54L, I.)
0
H
SKA3, SPAG5, 5PC24, TACC3,
a,
1
0
TIMELESS, TRIP13, UBE2C
ul
1
0
BLM, CENPA, CHAF1B, E2F2,
ul
macromolecular
GO:
H2AFX, HJURP, KIF2C, MCM2,
BP complex subunit 0.695 0.659943933 0 2.7E-04 9.8E-03
3.09 15 -3.55 HLA-DMA
0043933 RAD51, RRM2, SCARB1,
organiza-tion
TMEM48, TUBA1B, TUBA1C
HLA-DMA,
HLA-DPB1, *0
GO: MHC protein
n
CC 0.000 0.712 42611 0 1.5E-03 1.6E-02
17.24 4 3.89 HLA-DQB1,
0042611 complex
(HLA-DRB1, c6
HLA-DRB4) :4:
-c-:--,
c,
.6.
c,
,,,

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability kINõ
select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
HLA-DMA, t
1-,
HLA-DPB1 k.)
GO: MHC class II
' .6.
CC 0.773 0.712 42611 1 5.1E-05 1.0E-03 51.72 4 3.89 HLA-
DQB1,
0042613 protein complex
(HLA-DRB1,
HLA-DRB4)
CCNB2, CDC20, CDC6, ESPL1,
GO: micro-tubule
CC 0.002 0.670 15630 0 1.2E-03 1.4E-02
3.17 12 -3.20 KIF22, KIF2C, PLK1, SKA3,
CAMSAP1L1
0015630 cytoskel-eton
SPAG5, TUBA1B, TUBA1C
_______________________________________________________________________________
_____________________________________________ 0
GO:
CDC20, CDC6, KIF22, PLK1,
CC spindle 0.727 0.616 15630 1 3.5E-03 3.5E-02
5.80 6 -3.92 0
0005819
SKA3, SPAG5 "
co
ul
BLM, CDC20, CDC25A, CDC25C,
a,
c7,
CDC6, CHAF1B, E2F2, H2AFX,
c7,
in
GO:
HJURP, LMNB1, MCM10, MCM2, 0
CC nuclear lumen 0.173 0.641 31981 0 1.5E-03 1.6E-02
2.09 21 -3.86 H
0031981
MCM4, MCM5, MKI67, PLK1, a,
1
0
POLA2, RAD51, RBM14, RFC2,
ul
1
0
UBE2C
ul
BLM, H2AFX, MCM2, NCAPD3,
GO: nuclear chromo-
CC 0.841 0.581 31981 1 1.5E-05 3.4E-04
7.95 9 -2.28 POLA2, RAD51, SUV39H1,
CALC0001
0000228 some
TIMELESS
BLM, CDC20, CDC25A, CDC25C,
CDC6, CHAF1B, E2F2, H2AFX,
Iv
GO:
n
CC nucleo-plasm 0.902 0.636 31981 1 8.3E-05 1.4E-03 2.94 18 -
3.92 MCM10, MCM2, MCM4, MCM5,
0005654
PLK1, POLA2, RAD51, RBM14,
cp
tµ.)
RFC2, UBE2C
o
1-,
_______________________________________________________________________________
__________________________________________ tµ.)
'a
c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability kINõ
select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
BLM, CCNB2, CDC20, CDC6,
'a
--4
CDCA5, CENPA, CENPM, ESPL1,
t.)
.6.
H2AFX, HJURP, KIF22, KIF2C,
--4
GO: non-membrane-
LMNB1, MCM2, MCM4, MKI67, CALC0001,
CC 0.235 0.658 43228 0 1.2E-04 1.9E-03
1.91 33 -3.12
0043228 bounded organelle
NCAPD3, NCAPH, PLK1, POLA2, CAMSAP1L1
RAD18, RAD51, RBM14, RFC2,
SKA3, SPAG5, 5PC24, SUV39H1,
TIMELESS, TUBA1B, TUBA1C
_______________________________________________________________________________
___________________________________________ 0
GO:
CENPA, CENPM, HJURP, KIF22,
CC kineto-chore 0.350 0.690 776 0 9.8E-07 3.5E-05 14.78
8 -3.86 o
0000776
KIF2C, SKA3, SPAG5, 5PC24 N)
co
ul
GO: condensed nuclear
a,
CC 0.912 0.608 776 1 4.5E-03 4.2E-02 11.85 4 -
2.71 BLM, NCAPD3, RAD51, SUV39H1 c7,
0000794 chromo-some
c7,
ul
GO: condensed chromo-
CENPA, CENPM, HJURP, KIF2C, 0
CC 0.880 0.697 776 1 2.8E-06 8.5E-05 17.16 7 -
3.97 H
0000777 some kineto-chore
SKA3, SPAG5, 5PC24 a,
1
0
condensed chromo-
ul
GO:
CENPA, CENPM, HJURP, KIF2C, 1
0
CC some, centro-meric 0.918 0.697 776 1
6.1E-06 1.6E-04 15.08 7 -3.97 ul
0000779 SKA3, SPAG5, 5PC24
region
BLM, CDCA5, CENPA, CENPM,
H2AFX, HJURP, KIF22, KIF2C,
GO:
MCM2, MKI67, NCAPD3, NCAPH,
CC chromo-some 0.481 0.645 5694 0 1.7E-12 1.5E-10 7.05
22 -3.11 CALC0001
0005694
POLA2, RAD18, RAD51, RFC2, Iv
n
SKA3, SPAG5, 5PC24, SUV39H1,
TIMELESS
cp
_______________________________________________________________________________
__________________________________________ t.)
o
BLM, CENPA, CENPM, HJURP,
1-,
t.)
GO: condensed chromo-
KIF2C, MKI67, NCAPD3, NCAPH, 'a
CC 0.505 0.680 793 0 6.8E-11 4.1E-09 14.44 13
-3.60 c:
.6.
0000793 some
RAD51, SKA3, SPAG5, 5PC24, c:
SUV39H1

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
BLM, CDCA5, CENPA, CENPM,
'a
--4
H2AFX, HJURP, KIF22, KIF2C,
t.)
.6.
GO:
MCM2, MKI67, NCAPD3, NCAPH, --4
CC chromo-somal part 0.634 0.640 44427 0 5.8E-13 1.1E-10
8.03 21 -3.12 CALC0001
0044427
POLA2, RAD18, RFC2, SKA3,
SPAG5, 5PC24, SUV39H1,
TIMELESS
GO: nuclear chromo-
BLM, H2AFX, MCM2, NCAPD3,
CC 0.831 0.544 44427 1 1.9E-04 2.6E-03
8.23 7 -2.21 CALC0001
0044454 some part
POLA2, TIMELESS
n
GO:
CENPA, H2AFX, KIF22, MCM2,
CC chromatin 0.790 0.580 44427 1 3.2E-04 4.2E-03
6.05 8 -2.21 CALC0001 0
0000785
RAD18, SUV39H1, TIMELESS "
co
ul
GO:
BLM, H2AFX, POLA2, RAD18, a,
CC replication fork 0.815 0.534 44427 1 6.7E-05 1.2E-03
22.22 5 -2.74 c7,
0005657
RFC2 c7,
ul
co
GO: chromo-some,
CENPA, CENPM, HJURP, KIF22,
I.)
0
H
CC 0.657 0.686 775 0 1.7E-07 7.5E-06
11.56 10 -3.68 KIF2C, MKI67, SKA3, SPAG5,
a,
1
0000775 centro-meric region
0
5PC24, SUV39H1
ul
1
0
BLM, CCNB2, CDC20, CDC6,
ul
CDCA5, CENPA, CENPM, ESPL1,
intra-cellular non-
H2AFX, HJURP, KIF22, KIF2C,
GO:
LMNB1, MCM2, MCM4, MKI67, CALC0001,
CC membrane- 0.676 0.658 43232 0 1.2E-04 1.9E-03
1.91 33 -3.12
0043232
bounded organelle
NCAPD3, NCAPH, PLK1, POLA2, CAMSAP1L1
RAD18, RAD51, RBM14, RFC2,
Iv
n
SKA3, SPAG5, 5PC24, SUV39H1,
TIMELESS, TUBA1B, TUBA1C
cp
_______________________________________________________________________________
________________________________________________ tµ.)
HLA-DMA, :4:
GO: MHC class II
HLA-DPB1, .6.
MF 0.000 0.712 32395 0 4.6E-05 1.0E-02
53.65 4 3.89 HLA-DQB1, S
0032395 receptor activity
c,.)
(HLA-DRB1,
HLA-DRB4)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability kINõ
select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
Orange Module
-c-:--,
_______________________________________________________________________________
__________________________________________ -4
C3AR1, CNNM4, 4r2
ELOVL3, ENPP1,
ADAM23, FGFR4, HVCN1, ESAM, GALNT10,
GO: integral to
CC 0.000 0.804 16021 0 5.5E-04 4.7E-02
1.87 20 0.47 IL13RA1, SLC30A3, IL2RB, KIAA1467,
0016021 membrane
SORT1, TMEM107
KREMEN1, LAMP3,
SEMA4F, STOM,
TMEM180
_______________________________________________________________________________
_____________________________________________ 0
C3AR1, CNNM4,
0
ELOVL3, ENPP1, "
co
ul
ADAM23, FGFR4, HVCN1, ESAM, GALNT10, a,
GO: intrinsic to
0,
CC 0.607 0.804 31224 0 9.7E-04 4.1E-02
1.80 20 0.47 IL13RA1, SLC30A3, IL2RB, KIAA1467,
0,
ul
0031224 membrane
SORT1, TMEM107
KREMEN1, LAMP3, cr`D)
H
SEMA4F, STOM, a,
1
0
TMEM180
ul
1
0
Darkgreen Module
in
ABL1, AIFM1, CHD1L, DFFB,
DNASE1L1, DNMT3A, GTF2H3,
GO: DNA metabolic
BP 0.000 0.563 6259 0 1.8E-05 1.3E-02 3.26
19 -1.67 HAUS7, HMGA1, HSPD1, LIG3, NFIA
0006259 process
NASP, OBFC2B, PARP1, SET,
SMARCB1, SSRP1, SUPT16H
Iv
_______________________________________________________________________________
__________________________________________ n
C20orf7, CENPV, FKBP4, GEMIN4,
GTF2H3, H2AFY, H3F3A, HMGA1,
cp
GO: macro-molecular
t.)
o
BP 0.000 0.603 65003 0 1.8E-04 5.0E-02
2.64 20 -1.65 HSPD1, IP011, MED12, PAK2, APC2 1-,
0065003 complex assembly
t.)
SET, 5F3B3, SHMT1, TSR1, TUBB,
-c-:--,
c,
.6.
TUBGCP4, WDR77
c:
_______________________________________________________________________________
__________________________________________ c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
cellular macro-
C20orf7, CENPV, FKBP4, GEMIN4, 'a
--4
GO: molecular complex
H2AFY, H3F3A, HMGA1, HSPD1,
t.)
BP 0.944 0.586 65003 1 1.0E-05 1.4E-02
4.01 16 -1.90 .6.
0034621 subunit organiz-
IP011, NASP, PAK2, SET, --4
ation
SUPT16H, TSR1, TUBB, WDR77
AIFM1, AIFM2, CENPV, CHD1L,
DFFB, DNMT3A, H2AFY, H3F3A,
GO: chromo-some
BP 0.638 0.558 51276 0 3.2E-05 1.4E-02
3.28 18 -1.62 HMGA1, HMGN2, IN080, NASP,
PRDM6
0051276 organiz-ation
PARP1, SET, SMARCA4,
0
SMARCB1, SUPT16H
_______________________________________________________________________________
__________________________________________________ 0
C20orf7, CENPV, FKBP4, GEMIN4,
"
co
ul
GTF2H3, H2AFY, H3F3A, HMGA1,
a,
. macro-molecular
c7,
C-) GO:
HSPD1, IP011, MED12, NASP, c7,
ul
F BP
0043933 complex subunit 0.695 0.587 43933 0 5.1E-05 1.8E-02
2.71 22 -1.67
PAK2, SET, 5F3B3, SHMT1,
APC2 I.)
0
organiz-ation
H
SUPT16H, TSR1, TUBB,
a,
1
0
TUBGCP4, WDR77
ul
1
0
ABL1, ACO2, AIFM1, COIL,
ul
DDX54, DFFB, DNMT3A, DUSP7,
EXOSC2, FKBP4, GEMIN4,
GTF2H3, HCFC1, HMGA1,
HNRNPL, HSPD1, IVD, KEAP1,
LARS2, LAS1L, LIG3, LMNB2, Iv
GO: membrane-
PDIA5, n
CC 0.000 0.590 31974 0 3.3E-06 3.1E-04
1.96 48 -1.75 MED12, MIPEP, MPHOSPH6,
0031974 enclosed lumen
TBX19
MRPS15, NF2, NOL12, NOLC1,
cp
t.)
NVL, OXCT1, PA2G4, PARP1, o
1-,
t.)
POLR3H, PSMD8, SET, SMARCB1,
'a
c:
SSRP1, SUPT16H, TH1L, THOC4,
.6.
c:
TOE1, TRIM25, TSR1, UTP20, c,.)
WDR4

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
ABL1, COIL, DDX54, DFFB,
'a
--4
DNMT3A, DUSP7, EXOSC2,
k.)
.6.
FKBP4, GEMIN4, GTF2H3, HCFC1,
--4
HMGA1, HNRNPL, KEAP1,
GO:
LAS1L, LIG3, LMNB2, MED12,
CC nuclear lumen 0.000 0.601 31981 0 1.7E-05 1.2E-03
2.04 39 -1.80 TBX19
0031981
MPHOSPH6, NF2, NOL12, NOLC1,
NVL, PA2G4, PARP1, POLR3H,
PSMD8, SET, SMARCB1, SSRP1,
0
SUPT16H, TH1L, THOC4, TOE1,
0
TRIM25, TSR1, UTP20, WDR4
I.)
co
ul
ABL1, ACO2, AIFM1, COIL,
a,
.
c7,
C-)
DDX54, DFFB, DNMT3A, DUSP7, c7,
ul
.
EXOSC2, FKBP4, GEMIN4, "
0
H
GTF2H3, HCFC1, HMGA1,
a,
1
0
HNRNPL, HSPD1, IVD, KEAP1,
ul
1
LARS2, LAS1L, LIG3, LMNB2,
0
ul
GO:
PDIA5,
0043233
TBX19
MRPS15, NF2, NOL12, NOLC1,
NVL, OXCT1, PA2G4, PARP1,
POLR3H, PSMD8, SET, SMARCB1,
SSRP1, SUPT16H, TH1L, THOC4,
Iv
n
TOE1, TRIM25, TSR1, UTP20,
WDR4
cp
_______________________________________________________________________________
________________________________________________ t.)
o
1-,
t.)
'a
o
.6.
o
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability õ filter EASE
n Fold Genes DOWN regulated
kIN select
Benjamini Enrichment
regulated 64
score
Change 1-,
ABL1, COIL, DDX54, EXOSC2,
'a
--4
FKBP4, GEMIN4, KEAP1, LAS1L,
t.)
.6.
GO:
MED12, MPHOSPH6, NF2, NOL12, --4
CC nucleolus 0.858 0.612 31981 1 1.0E-04 4.7E-03
2.51 23 -1.76 TBX19
0005730
NOLC1, NVL, PA2G4, PARP1,
PSMD8, SMARCB1, TOE1,
TRIM25, TSR1, UTP20
ABL1, ACO2, AIFM1, COIL,
DDX54, DFFB, DNMT3A, DUSP7,
0
EXOSC2, FKBP4, GEMIN4,
0
GTF2H3, HCFC1, HMGA1,
K)
co
ul
HNRNPL, HSPD1, IVD, KEAP1,
a,
.
c7,
C-) GO: intra-cellular
LARS2, LAS1L, LIG3, LMNB2,
PDIA5,
Y
c7,
ul
. CC 0.975 0.590 31981 1 9.9E-07 2.8E-04
2.04 48 -1.75 MED12, MIPEP, MPHOSPH6,
"
0
0070013 organelle lumen
TBX19 H
MRPS15, NF2, NOL12, NOLC1,
a,
1
0
NVL, OXCT1, PA2G4, PARP1,
ul
1
POLR3H, PSMD8, SET, SMARCB1,
0
ul
SSRP1, SUPT16H, TH1L, THOC4,
TOE1, TRIM25, TSR1, UTP20,
WDR4
AIFM1, AIFM2, ALDH18A1, BID,
C20orf7, DHODH, EXOG, GCAT,
Iv
GO:
n
CC envelope 0.002 0.597 31975 0 1.2E-03 2.9E-02 2.34 19 -
1.72 HK2, HSPD1, IP011, LMNB2, BCL2L11
0031975
NDUFS3, PARP1, 5LC25A33,
cp
t.)
STOML2, TMPO, TOMM4OL
o
1-,
_______________________________________________________________________________
________________________________________________ t.)
'a
c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
ABL1, ADA, AHSA1, AIFM2, BID,
'a
--4
CABLES1, CASP2, CEP192,
k.)
.6.
CIDEB, CTPS, DFFB, DOCK2,
--4
GO:
DUSP7, FARSA, GEMIN4, GYS1, BCL2L11,
CC cytosol 0.080 0.563 5829 0 5.0E-04 1.7E-02
1.87 33 -1.66
0005829
HK2, HMGA1, HSPD1, JARS, RABGAP1
LDLRAP1, NTRK2, ODC1, PAK2,
PSMD8, SET, SHMT1, SPHK2,
TUBB, TUBGCP4, UROD
_______________________________________________________________________________
__________________________________________________ 0
ABL1, ALDOA, CENPV, CEP192,
0
COIL, CORO1B, DDX54,
K)
co
ul
DNMT3A, DOCK2, EXOSC2,
a,
.
0,
C-)
FKBP4, GEMIN4, H2AFY, H3F3A, 0,
ul
c..,)
.
HAUS7, HMGA1, HMGN2, KEAP1' APC2, KIF5A, 1'3"
LAS1L, LMNB2, MED12,
a,
intra-cellular non-
MY05C, 1
0
GO:
MPHOSPH6, MRPS15, NF2, ul
CC membrane- 0.235 0.591 43232 0 2.1E-04 8.4E-03
1.61 53 -1.54 PRDM6, '
0043232
NOL12, NOLC1, NTRK2, NVL, 0
ul
bounded organelle
RABGAP1,
PA2G4, PAK2, PARP1, PSMD8,
TBX19
RCC1, SMARCA4, SMARCB1,
SSRP1, STAG3L4, STOML2,
SUPT16H, TMPO, TOE1, TRIB2,
TRIM25, TSR1, TUBB, TUBGCP4,
Iv
n
UTP20
cp
t.)
o
1-,
t.)
'a
c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability õ filter EASE
n Fold Genes DOWN-regulated
kIN select
Benjamini Enrichment
regulated 64
score
Change
ABL1, ALDOA, CENPV, CEP192,
'a
--4
COIL, CORO1B, DDX54,
t.)
.6.
DNMT3A, DOCK2, EXOSC2,
--4
FKBP4, GEMIN4, H2AFY, H3F3A,
HAUS7, HMGA1, HMGN2, KEAP1' APC2, KIF5A,
LAS1L, LMNB2, MED12,
MY05C,
GO: non-membrane-
MPHOSPH6, MRPS15, NF2,
CC 0043228 bounded organelle NOL12, NOLC1, NTRK2, NVL, 0.362
0.591 43228 0 2.1E-04 8.4E-03 1.61 53 -1.54 PRDM6,
RABGAP1,
n
PA2G4, PAK2, PARP1, PSMD8,
TBX19
0
RCC1, SMARCA4, SMARCB1,
I.)
co
SSRP1, STAG3L4, STOML2,
ul
a,
.
c7,
C-)
SUPT16H, TMPO, TOE1, TRIB2, c7,
ul
TRIM25, TSR1, TUBB, TUBGCP4,
"
0
H
UTP20
a,
1
0
ACO2, ACP6, AIFM1, AIFM2,
ul
1
ALDH18A1, BID, C20orf7,
0
ul
DHODH, ECH1, EXOG, GCAT,
GO:
BCL2L11,
CC 0005739 mitochon-drion 0.385 0.575
5739 0 1.6E-03 3.8E-02 1.90 27 -1.38 HK2, HSPD1, IVD, LARS2, MIPEP' IF16,
MAPK10
MRPS15, NDUFS3, OXCT1,
SHMT1, 5LC25A33, STOML2,
TOMM4OL, TXNRD2
Iv
_______________________________________________________________________________
________________________________________________ n
AIFM2, ALDH18A1, BID, C20orf7,
GO: mitochon-drial
DHODH, EXOG, GCAT, HK2,
cp
CC 0.511 0.599
31966 0 1.7E-03 3.6E-02 2.75 14 -1.61 BCL2L11 t--
)
0031966 membrane
HSPD1, NDUFS3, 5LC25A33, o
1-,
t.)
STOML2, TOMM4OL
'a
_______________________________________________________________________________
________________________________________________ c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability õ filter EASE
n Fold Genes DOWN regulated
kIN select
Benjamini Enrichment
regulated 64
score
Change 1-,
ACO2, AIFM1, AIFM2,
'a
--4
ALDH18A1, BID, C20orf7,
k.)
.6.
GO:
DHODH, EXOG, GCAT, HK2, --4
CC mitocho-ndrial part 0.727 0.570
31966 1 8.5E-05 4.7E-03 2.70 21 -1.72 BCL2L11
0044429
HSPD1, IVD, LARS2, MIPEP,
MRPS15, NDUFS3, OXCT1,
5LC25A33, STOML2, TOMM4OL
AIFM1, AIFM2, ALDH18A1, BID,
C20orf7, DHODH, EXOG, GCAT,
GO:
n
CC organelle envelope 0.785 0.597
31966 1 1.1E-03 3.1E-02 2.35 19 -1.72 HK2, HSPD1,
IP011, LMNB2, BCL2L11
0031967
o
NDUFS3, PARP1, 5LC25A33,
N)
co
ul
STOML2, TMPO, TOMM4OL
a,
.
c7,
C-)
AIFM1, AIFM2, ALDH18A1, BID, c7,
ul
ul
. GO: mitochon-drial
C20orf7, DHODH, EXOG, GCAT, I.)
0
CC 0.923 0.600 31966 1 1.0E-03 3.1E-02
2.76 15 -1.65 BCL2L11 H
0005740 envelope
HK2, HSPD1, NDUFS3, 5LC25A33, a,
1
0
STOML2, TOMM4OL
ul
1
0
Springgreen Module
ul
CAPG, CLIP2, EPB41L5,
FMNL2, GSN, HIP1, JUP,
GO: cyto-skeletal
CCR5, KLHL3, KIF1B, KPTN, LIMA1,
MF 0.000 0.561 8092 0 2.5E-04 2.9E-02
2.39 23 1.48
0008092 protein binding
PARVB, RANBP10 MAPT, MYH11, MYH15,
MY015A, OBSL1, SPIRE1, *0
n
SYNE2, TNNT1, VCL
cp
tµ.)
o
1-,
tµ.)
'a
c:
.6.
c:
c,.)

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability õ filter EASE
n Fold Genes DOWN-regulated
kIN select
Benjamini Enrichment
regulated 64
score
Change 1-,
ARHGAP17, ARHGAP26, t-
ARHGEF9, ASAP1, ASAP3, 4r2
CHN1, CYTH1, CYTH3,
ARHGAP4, DOCK10,
GO: GTPase regulator
DNMBP, ERC1, F1110357,
MF 0.000 0.553 30695 0 1.8E-07 4.3E-05
3.39 26 1.14 MAP4K1, RANBP10,
0030695 activity
JUN, RABGAP1L,
TBC1D9B, TIAM2
RALGPS1, RASA2,
RASAL2, RPH3A,
SRGAP2, SYTL3, TIAM1
_______________________________________________________________________________
________________________________________________ 0
ARHGAP26, ARHGEF9,
ASAP1, ASAP3, CYTH1,
ARHGAP4, DOCK10, CYTH3, DNMBP, ERC1,
. GO: small GTPase
c7,
'-' MF 0.947 0.557 30695 1 6.0E-08 2.8E-05
4.19 22 0.95 MAP4K1, RANBP10, F1110357, JUN, c7,
ul
c) 0005083 T regulator activity
TBC1D9B, TIAM2 RABGAP1L, RASA2, "
0
H
RASAL2, RPH3A, SYTL3,
t=
0
TIAM1
ul
1
ARHGAP17, ARHGAP26,
ccri)
ARHGEF9, ASAP1, ASAP3,
CHN1, CYTH1, CYTH3,
nucleoside-triphos- ARHGAP4, DOCK10,
GO:
DNMBP, ERC1, F1110357,
MF phatase regulator 0.841
0.553 30695 1 2.8E-07 4.4E-05 3.31 26 1.14 MAP4K1, RANBP10,
0060589
JUN, RABGAP1L,
activity
TBC1D9B, TIAM2
RALGPS1, RASA2,
1-d
n
RASAL2, RPH3A,
SRGAP2, SYTL3, TIAM1 2
Red Module
o
1-,
tµ.)
B3GAT3, B3GNT1, CORO1A,
'a
c:
.6.
GO: endo-membrane
DHCR7, NRM, PIGU, SCAMP2, c:
CC 0.000 0.718 12505 0 2.1E-04 1.0E-02
3.74 12 -1.96
0012505 system
SCAMP3, 5LC37A4, SREBF1,
55R2, TMEM109

REVIGO results DAVID results
GE in Combination vs. Control
P-value Mean
Mean GO FDR Fold
Genes UP- 0
GOGO TermID GO Name Dispensability
kINõ select filter EASE n Fold Genes DOWN-regulated
Benjamini Enrichment
regulated 64
score
Change 1-,
ABHD12, ATP6V0B, ATP6V0C,
'a
--4
B3GAT3, B3GNT1, C19orf63,
t.)
.6.
C20orf3, CD276, CD320, CD79B,
--4
CLN6, DHCR7, DHRS7B, IL21R,
INSIG1, NINJ1, NRM, P2RX4,
GO: integral to
CC 0.003 0.713 16021 0 1.1E-05 1.6E-03
1.80 35 -1.90 PAQR4, PIGU, SCAMP2, RASGRP3
0016021 membrane
SCAMP3, SCNN1B, SLC25A25,
5LC37A4, 5LC39A3, SLC7A11,
0
SREBF1, 55R2, TMED1,
0
TMED3, TMEM109,
I.)
co
ul
TNFRSF13B, ZDHHC12
a,
.
c7,
C-)
ABHD12, ATP6V0B, ATP6VOC, c7,
ul
B3GAT3, B3GNT1, C19orf63,
I.)
0
H
C20orf3, CD276, CD320, CD79B,
a,
1
0
CLN6, DHCR7, DHRS7B, IL21R,
ul
1
INSIG1, NINJ1, NRM, P2RX4,
0
ul
GO: intrinsic to
CC 0.607 0.713 31224 0 2.9E-05 2.1E-03
1.74 35 -1.90 PAQR4, PIGU, SCAMP2, RASGRP3
0031224 membrane
SCAMP3, SCNN1B, SLC25A25,
5LC37A4, 5LC39A3, SLC7A11,
SREBF1, 55R2, TMED1,
TMED3, TMEM109,
Iv
n
TNFRSF13B, ZDHHC12
cp
t.)
o
1-,
t.)
'a
c:
.6.
c:
c,.)

CA 02854665 2014-05-05
WO 2013/071247
PCT/US2012/064693
Table 4. The GSEA scores for each drug-related gene expression module in newly
diagnosed MM,
treatment-refractory MM, MGUS, and SMM patients compared to healthy
volunteers.
Negative Enrichment Score (ES)
G5E6477 NAME SIZE ES NES NOM.p.val FDR.q.val RANK.AT.MAX
NEW Blue UP 13 -0.848 -2.29 <le-4 <le-4
871
RELAPSED Blue UP 13 -0.817 -2.22 <le-4 <le-4
1015
SMM Blue UP 13 -0.817 -2.28 <le-4 <le-4
1529
MGUS Blue UP 13 -0.699 -1.94 0.0017
0.0037 3003
RELAPSED Springgreen_UP 198 -0.377 -1.80 <le-4 0.0043 2932
NEW Springgreen_UP
198 -0.361 -1.71 <le-4 0.0088 2205
SMM Springgreen_UP
198 -0.268 -1.33 0.0243 0.1648 2472
NEW Darkggreen_UP
24 -0.357 -1.14 0.2727 0.2318 2755
RELAPSED Darkggreen_UP 24 -0.335 -1.08 0.3304 0.3042 1852
SMM Darkggreen_UP
24 -0.332 -1.10 0.3124 0.3756 2142
SMM Blue_DOWN 81 -
0.196 -0.84 0.7850 0.7598 1610
MGUS Springgreen_DOWN 70 -0.147 -0.61 0.9954 0.9796 3621
MGUS Darkggreen_DOWN 144 -0.140 -0.66 0.9972 1 2913
MGUS Darkggreen_UP 24 -0.273 -0.90 0.6005 1 3908
MGUS Red_DOWN 40 -0.192 -0.71
0.9192 1 1854
MGUS Blue_DOWN 81 -0.217 -
0.93 0.6136 1 1312
MGUS Springgreen_UP 198 -
0.197 -0.97 0.5363 1 2335
Positive Enrichment Score (ES)
G5E6477 NAME SIZE ES NES NOM.p.val FDR.q.val RANK.AT.MAX
RELAPSED Darkggreen_DOWN 144 0.497 2.20 <le-4 <le-4 2683
NEW Darkggreen_DOWN 144 0.456 2.07 <le-4 0.0007
1947
RELAPSED Blue_DOWN 81 0.483 1.94 <le-4 0.0009 2234
NEW Red_DOWN 40
0.485 1.72 0.0060 0.0101 2485
RELAPSED Red_DOWN 40 0.444 1.55 0.0196 0.0293 2288
NEW
Blue_DOWN 81 0.349 1.43 0.0272 0.0682 3892
MGUS Orange_UPms275 21 0.400 1.23 0.1807 0.1437 4769
SMM Orange_UPms275 21 0.433 1.36 0.1025 0.2624 2906
SMM Red_DOWN 40
0.298 1.09 0.3104 0.4002 3821
SMM Darkggreen_DOWN 144 0.236 1.11 0.2340 0.5449
2633
NEW Orange_UPms275 21 0.270 0.82 0.7255 0.7931 4074
RELAPSED Springgreen_DOWN 70 0.166 0.65 0.9847 0.9661 3820
SMM Springgreen_DOWN 70 0.137 0.57 0.9981 0.9896 4632
NEW Springgreen_DOWN 70 0.207 0.83 0.7988 0.9905 3646
RELAPSED Orange_UPms275 21 0.272 0.82 0.7378 1 3118
-108-