BACKGROUND
the american cancer society estimates that in  <dig>  approximately  <dig>  women will die of epithelial ovarian cancer  in the united states, with  <dig>  estimated new cases  <cit> .
since early ovarian cancer shows few symptoms, the vast majority of patients continue to be diagnosed with advanced stage disease, where the prognosis is poor with 5-year survival rate around 27 %.
the standard treatment protocol for patients with advanced eoc is an initial debulking surgery, followed by carboplatin-paclitaxel combination chemotherapy.
platinating agents, such as cisplatin, carboplatin, and oxaliplatin, are able to interact with dna to form monoadducts, intra- and interstrand crosslinks, and dna-protein crosslinks, ultimately resulting in cell death.
taxane agents are widely used chemotherapeutic drugs often in combination with platinating agents.
taxanes, such as paclitaxel and docetaxel, block cell division by binding to β-tubulin, stabilizing the microtubules, leading to cell death  <cit> .
although over 70 % of ovarian cancer patients are initially sensitive to the combination therapy consisting of a platinum agent and a taxane, the majority will experience relapse and subsequent resistance to the therapy  <cit> .
thus, finding new therapeutic options for treating patients with eoc is essential.
one approach that has been used widely in cancer drug discovery is connectivity mapping  <cit> .
the connectivity map  is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules analyzed using pattern-matching algorithms that discover relationships between the drugs, gene expression changes, and the phenotypes.
this computational approach has greatly facilitated drug screening studies, as cmap contains more than  <dig> gene expression profiles for approximately  <dig> compounds .
in particular, it has been employed in many studies for discovering repurposing drugs against common diseases, including diabetes and alzheimer’s disease  <cit> , and for treating solid tumors, including those associated with colon cancer  <cit> , breast cancer  <cit> , and lung adenocarcinoma  <cit> .
the basic approach for cmap-based drug discovery studies is the identification of disease-  associated genomic signatures that inversely correlate with perturbation in the genomic signature associated with the administration of molecules or drugs  <cit> .
in these studies, the essence of the protocol – the individual-gene cmap approach – for identifying drugs for treating a specific disease is straightforward: find a set of differentially expressed genes  obtained by comparing two sets – e.g., control and patient tissues – of gene expression microarrays, score the match between the deg set and genomic profiles of drugs given by cmap, and rank the drugs by score  <cit> .
the candidate drugs are those with the highest absolute scores.
however, these studies have some limitations.
first, the list of degs used in the cmap analysis is usually based on a relatively small number of biological replicates .
additionally, the number of cell lines in cmap exposed to the compounds is limited to only a handful of cancer cell lines from breast, leukemia, prostate and melanoma, with each compound usually tested on only a few cell lines.
furthermore, recent studies have shown potential issues with use of cancer cell lines in terms of the lack of rigor in the estimation of drug response phenotypes in cell lines  <cit>  and the lack of concordance between cell lines and human genomic profiles  <cit> .
among eoc cell lines, a recent study has found that igrov <dig> is most probably not of the high grade serous subtype as it is often quoted  <cit> .
to address the limitations of previous deg selection based on cancer cell lines, we determined our degs based on two large collections of tumor gene expression data collected on high grade serous eoc patients for whom clinical endpoints were available .
to determine the most relevant degs, we characterized the associations between gene expression and time to recurrence.
we hypothesized that the potential therapeutic drugs for eoc are those that have a gene expression profile that are related to the gene expression signature related to clinical outcome of time to recurrence .
following cmap analyses, we then tested key genes on ten eoc cell lines to assess the ability of the candidate drugs to effectively kill eoc cells.
methods
tcga ovarian cancer study
as part of tcga, research collected and assessed genome-wide gene expression data on  <dig> samples using the agilent expression 244 k microarray.
gene expression and clinical data were downloaded from the tcga data portal  on september  <dig>   <dig>  gene expression data were lowess normalized with replicate probes for a gene collapsed by averaging across the probes.
of  <dig> serous cyst adenocarcinoma with agilent gene expression data,  <dig> of the tumors were classified as high-grade.
thus, restricting to high grade serous tumors and removing the samples in the tcga that were part of the mayo clinic study, we included  <dig> tumors in our analysis.
summary of the tcga participants are presented in table  <dig> table  <dig> summary of tcga and mayo clinic studies
mayo clinic ovarian cancer study
briefly, eligible cases were ascertained between  <dig> and  <dig> at the mayo clinic within 1 year of diagnosis with pathologically confirmed primary invasive high-grade serous eoc.
progression and vital status were obtained from the mayo clinic tumor registry, electronic medical records, and active patient contact.
all cases provided written informed consent for use of their tissues and medical records in research; all protocols were approved by the mayo clinic institutional review board.
rna from fresh frozen tumors of each patient was extracted and assessed using agilent whole human genome 4 × 44 k expression arrays as previously described  <cit> .
the program “combat” was used to correct for batch-effects due to cy <dig> and cy <dig> labeling differences observed among experimental batches  <cit> .
summary of  <dig> mayo clinic participants are presented in table  <dig>  data used in this study can be found at the gene expression omibus .
statistical and cmap analyses
cox proportional hazard models were used to assess the association of gene expression  with time to recurrence , by study with and without adjustment for age at diagnosis, stage, and debulking status.
deg probes were selected for inclusion in the cmap analysis if p <  <dig>  for the unadjusted analyses and p <  <dig>  for the covariate adjusted analyses; different thresholds were used as cmap  has a limit on the number of features included in any signature.
these set of probes were then mapped to genes and then to the affymetrix id, as cmap is based on affymetrix probes/features.
we conducted cmap analysis on individual genes with hazard ratio  >  <dig> coded as “positively” associated and genes with hr <  <dig> coded as “negatively” associated with ttr to determine a set of drugs for which our gene signatures matches the “reference” signature .
clustering of samples based on gene expression levels was completed using recursive partition mixture models using the r package rpmm  <cit> , restricting the number of levels to a maximum of  <dig>
in vitro drug screens
in vitro drug cytotoxicity assays were conducted to determine which of the drugs highlighted by cmap analysis affected viability of eoc cells.
the drugs were purchased from the following vendors: cotinine, 3-nitropropionic acid, adiphenine hydrochloride, ethosuximide, and podophyllotoxin ; cephalexin and mitoxantrone ; clemizole ; wortmannin, doxorubicin, and 17-aag .
upon receipt, dimethyl sulfoxide  was used to prepare 10 mm stock solutions for all of the drugs except for cephalexin, which was prepared at a 5 mm concentration due to reduced solubility.
single-use aliquots of the stock drug solutions were made and stored at −80 °c.
all cell lines used in this study were obtained or derived at the fox chase cancer center .
details of the origin of the eoc cell lines  have been previously reported .
each cell line was grown in rpmi  <dig>  containing 2 mm l-glutamine and supplemented with 10 % fbs ,  <dig> i.u./ml penicillin , 100 μg/ml streptomycin , and  <dig>  μg/ml insulin  and maintained at 37 °c in a humidified atmosphere with 5 % co <dig>  cell lines were grown to 80 % confluency, harvested and seeded into 96-well plates at concentrations of  <dig> to  <dig> cells per well in a total volume of 95 μl.
twenty-four hours after seeding, drug compounds were prepared using cell growth media and 5 μl of each were added to the seeded cells in the 96-well plates.
a microlab nimbus  <dig> pipetting robot  was used to prepare the serial dilutions and for addition to the cell lines.
the final drug solutions consisted of eight concentrations ranging from  <dig> to  <dig>  μm .
vehicle-only wells were included on each plate to serve as interplate normalization controls.
seventy-two hours following drug addition, a 1/5th volume of celltiter blue reagent  was added directly to each well using a matrix wellmate .
the plates were incubated at 37 °c for 150 min and the fluorescent signal was measured using an infinite® m <dig> pro microplate reader .
the ratio of the fluorescent signal in a drug treated well to that of the average fluorescent signal from the vehicle treated wells on each plate multiplied by  <dig> was used to calculate cell viability for each drug treated well for each cell line.
a minimum of two biological replicates were performed for each cell line.
the viability data were subjected to non-linear regression analysis and ic <dig> values calculated using prism  <dig> software .
all data in the viability curves are reported as mean ± standard error of the mean .
RESULTS
genes associated with clinical outcome
to determine the gene signatures to use in cmap analyses, we determined the set of probes  associated with ttr by fitting separate cox proportional hazards models within each study  with and without adjustment for age at diagnosis, stage, and debulking status.
comparing the genes associated with ttr with no adjustment for covariates at the  <dig>  significance level and the same direction of effect resulted in  <dig> genes in common between tcga and mayo studies;  <dig> genes had a hazard ratio  <  <dig>  and  <dig> genes had a hr >  <dig> .
in contrast, the adjusted analyses resulted in  <dig> genes in common , with  <dig> genes having a hr <  <dig> and  <dig> having a hr >  <dig>  table  <dig> presents the genes with p <  <dig>  in both studies  for both the adjusted and unadjusted analyses.table  <dig> genes associated with epithelial ovarian cancer time to recurrence in analysis of tumors from the tcga and mayo clinic  for both adjusted and unadjusted analyses
ptprcap
ubash3a
ppbp
pvrig
igkv3-20
fcrl5
itk
ighv3or16-12
vangl1
slc16a8
igkv3-20
acap1
vangl1
cd38
ela1
psme2
ubd
note, three agilent probes did not have a current hugo gene id and are not presented in the table
to further evaluate that these sets of genes were predictive of outcome, we clustered the samples based on the expression levels of the genes in common between the two studies using recursive partitioning mixture models .
tcga and mayo clinic samples were clustered separately on the set of genes in common between the two studies .
the resulting cluster assignments were then assessed for association with survival using log-rank tests.
the log-rank p-values for testing cluster assignment with ttr were  <dig>  and  <dig>  for tcga and mayo clinic studies   and  <dig>  and  <dig>  based on clustering of genes from model adjusting for covariates .
connectivity mapping
cmap analysis was completed for each of the  <dig> deg sets  using the cmap software developed at the broad institute .
this resulted in :  <dig>   <dig>   <dig> and  <dig> drugs signatures were found to be either negatively or positively related with gene signatures based on analysis of tcga , tcga , mayo clinic  and mayo clinic , respectively.
we then looked at the overlap of the compounds found in both studies and found that  <dig>  compounds were in common between cmap analyses based on the covariate adjusted  signature, as illustrated in fig.
<dig>  this set of compounds included the following: tanespimycin , ethosuxiumide, cotinine, clemizole, 0175029– <dig> ; wortmannin, 3-nitropropionic acid, adiphenine, cephaeline, doxorubicin, podophyllotoxin, mitoxantrone, cephalexin,  <dig> .
in vitro drug screens were completed using these drugs with the exception of 0175029– <dig> and  <dig> for which commercial sources were not found and cephaeline which was cost prohibitive.fig.
<dig> venn diagram depicting the overlap of compounds/drugs found from the cmap analyses
in vitro drug screens
in order to experimentally determine the effect of the eleven cmap-highlighted drugs on eoc cell viability, we conducted in vitro dose response studies on a set of  <dig> eoc cell lines.
a significant reduction in cell viability following 72 h of drug treatment was observed for five of these compounds: mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-aag .
these  <dig> drugs were known to be cytotoxic and therefore were expected to affect eoc cell growth and viability based on their mechanisms of action .
the six remaining drugs failed to show in vitro anti-cancer activity, consistent with prior reports of minimal cytotoxic capacity .
the dose response data for all  <dig> drugs across the  <dig> eoc cell lines can be found in additional file 3: figure s <dig>  additional file 4: figure s <dig>  additional file 5: figure s <dig>  additional file 6: figure s <dig>  additional file 7: figure s <dig>  additional file 8: figure s <dig>  additional file 9: figure s <dig>  additional file 10: figure s <dig>  additional file 11: figure s <dig>  additional file 12: figure s <dig> and additional file 13: figure s <dig> fig.
<dig> drug dose response curves for five compounds for ovarian cancer cell line a2780: mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-aag
discussion
in this manuscript we have presented a bioinformatics approach for connectivity mapping based on clinical outcomes collected on a large patient population followed by functional validation of the identified drugs.
the strengths of this approach are: non-reliance on a signature determined based on a small number of cancer cell lines; use clinically relevant outcomes that are directly tied to response ; large clinical studies that provide the ability to look at overlap between cmap determined drugs; functional validation to confirm the ability of the discovered drugs to kill ovarian cancer cells.
the choice of applying this approach to ovarian cancer was by design, as eoc treatment is somewhat uniform where the majority of patients undergo tumor debulking or cytoreduction surgery to remove as much of the tumor as possible, followed by platinum-taxane combination chemotherapy.
application of this approach to other cancers with less standard treatments will introduce heterogeneity into clinical outcome and would need to be accounted for in the statistical analyses .
nevertheless, there are still limitations to cmap analysis, which include: small sample size contributing to the cmap database, with none of the cell lines having been derived from eoc; “batch effects” in cmap cell line cultures  <cit> ; and the signatures are based on only gene expression measured using microarrays.
this study is a proof of principle that clinical outcomes from large studies  have the ability to be leveraged for drug discovery.
of the  <dig> drugs carried forward, we a priori hypothesized that  <dig> of these drugs would affect eoc cell viability , for which all  <dig> showed an ability to kill eoc cells in vitro  for 72 h followed by measuring cell viability using the celltiter-blue assay).
their anti-cancer activities have been studied extensively and mitoxantrone and doxorubicin have been used in the treatment of a variety of cancers .
however, the cmap analyses of the tcga and mayo clinic studies did not identify the two most commonly used therapies for eoc, with none of the  <dig> drugs identified having similar structure or mechanism of action to carboplatin or paclitaxel.
however, three of the  <dig> compounds are topoisomerase ii inhibitors , a class of drugs used often in the treatment of breast cancer, lung cancer, testicular cancer, lymphomas and sarcomas  <cit> .
the natural products podophyllotoxin, wortmannin, and 17-aag have proven highly toxic to human subjects but subsequent analogues such as etoposide, px- <dig>  and ganetespib, respectively, have decreased side effects and are currently being investigated in the treatment of eoc  <cit> .
the six compounds that failed to exhibit significant in vitro cytotoxicity across the eoc cell line panel belong to classes of drugs related to central nervous system pathways, exhibit antimicrobial activity or are required in high micromolar concentrations which may not be physiologically achievable.
interestingly, adiphenine was also identified using cmap analysis as an adjuvant therapy to treat the psychological distress associated with eoc diagnosis  <cit> .
while novel drugs were not identified, a filtered list of six was obtained for directed in vitro testing.
CONCLUSIONS
future research is needed to investigate the use of these cmap–like analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies.
last of all, cmap analyses only determines candidate drugs that can be tested in future studies with no information provided on the optimal dose in humans; research into the optimal therapeutic dosage needs to be considered in the planning of future drug studies  <cit> .
additional files
additional file 1: figure s <dig>  results from clustering tumors from the tcga and mayo clinic studies.
analyses were completed with genes found to be associated with clinical outcome in both studies .
additional file 2: figure s <dig>  results from clustering tumors from the tcga and mayo clinic studies.
analyses were completed with genes found to be associated with clinical outcome in both studies .
additional file 3: figure s <dig>  the dose response data for 3-nitropropionic acid across the  <dig> eoc cell lines.
additional file 4: figure s <dig> .
the dose response data for 17-aag across the  <dig> eoc cell lines.
additional file 5: figure s <dig>  the dose response data for adiphenine hydrochloride across the  <dig> eoc cell lines.
additional file 6: figure s <dig>  the dose response data for cephalexin across the  <dig> eoc cell lines.
additional file 7: figure s <dig>  the dose response data for clemizole across the  <dig> eoc cell lines.
additional file 8: figure s <dig>  the dose response data for cotinine across the  <dig> eoc cell lines.
additional file 9: figure s <dig>  the dose response data for doxorubicin across the  <dig> eoc cell lines.
additional file 10: figure s <dig>  the dose response data for ethosuximide across the  <dig> eoc cell lines.
additional file 11: figure s <dig>  the dose response data for mitoxantrone across the  <dig> eoc cell lines.
additional file 12: figure s <dig>  the dose response data for podophyllotoxin across the  <dig> eoc cell lines.
additional file 13: figure s <dig>  the dose response data for wortmannin across the  <dig> eoc cell lines.
additional file 14: table s <dig>  dge and cmap results.
abbreviations
cmapconnectivity mapping
degsdifferentially expressed genes
dmsodimethyl sulfoxide
eocepithelial ovarian cancer
hrhazard ratio
rpmmrecursive partition mixture model
semstandard error of the mean
tcgathe cancer genome atlas
ttrtime to recurrence

 the american cancer society estimates that in approximately women will die of epithelial ovarian cancer in the united states, with estimated new cases. since early ovarian cancer shows few symptoms, the vast majority of patients continue to be diagnosed with advanced stage disease, where the prognosis is poor with 5-year survival rate around 27 %. the standard treatment protocol for patients with advanced eoc is an initial debulking surgery, followed by carboplatin-paclitaxel combination chemotherapy. platinating agents, such as cisplatin, carboplatin, and oxaliplatin, are able to interact with dna to form monoadducts, intra- and interstrand crosslinks, and dna-protein crosslinks, ultimately resulting in cell death. taxane agents are widely used chemotherapeutic drugs often in combination with platinating agents. taxanes, such as paclitaxel and docetaxel, block cell division by binding to β-tubulin, stabilizing the microtubules, leading to cell death. although over 70 % of ovarian cancer patients are initially sensitive to the combination therapy consisting of a platinum agent and a taxane, the majority will experience relapse and subsequent resistance to the therapy. thus, finding new therapeutic options for treating patients with eoc is essential. one approach that has been used widely in cancer drug discovery is connectivity mapping. the connectivity map is a collection of genome-wide transcriptional expression data from cultured human cells treated with bioactive small molecules analyzed using pattern-matching algorithms that discover relationships between the drugs, gene expression changes, and the phenotypes. this computational approach has greatly facilitated drug screening studies, as cmap contains more than gene expression profiles for approximately compounds. in particular, it has been employed in many studies for discovering repurposing drugs against common diseases, including diabetes and alzheimer’s disease, and for treating solid tumors, including those associated with colon cancer, breast cancer, and lung adenocarcinoma. the basic approach for cmap-based drug discovery studies is the identification of disease- associated genomic signatures that inversely correlate with perturbation in the genomic signature associated with the administration of molecules or drugs. in these studies, the essence of the protocol – the individual-gene cmap approach – for identifying drugs for treating a specific disease is straightforward: find a set of differentially expressed genes obtained by comparing two sets – e.g., control and patient tissues – of gene expression microarrays, score the match between the deg set and genomic profiles of drugs given by cmap, and rank the drugs by score. the candidate drugs are those with the highest absolute scores. however, these studies have some limitations. first, the list of degs used in the cmap analysis is usually based on a relatively small number of biological replicates. additionally, the number of cell lines in cmap exposed to the compounds is limited to only a handful of cancer cell lines from breast, leukemia, prostate and melanoma, with each compound usually tested on only a few cell lines. furthermore, recent studies have shown potential issues with use of cancer cell lines in terms of the lack of rigor in the estimation of drug response phenotypes in cell lines and the lack of concordance between cell lines and human genomic profiles. among eoc cell lines, a recent study has found that igrov is most probably not of the high grade serous subtype as it is often quoted. to address the limitations of previous deg selection based on cancer cell lines, we determined our degs based on two large collections of tumor gene expression data collected on high grade serous eoc patients for whom clinical endpoints were available. to determine the most relevant degs, we characterized the associations between gene expression and time to recurrence. we hypothesized that the potential therapeutic drugs for eoc are those that have a gene expression profile that are related to the gene expression signature related to clinical outcome of time to recurrence. following cmap analyses, we then tested key genes on ten eoc cell lines to assess the ability of the candidate drugs to effectively kill eoc cells. methods tcga ovarian cancer study as part of tcga, research collected and assessed genome-wide gene expression data on samples using the agilent expression 244 k microarray. gene expression and clinical data were downloaded from the tcga data portal on september gene expression data were lowess normalized with replicate probes for a gene collapsed by averaging across the probes. of serous cyst adenocarcinoma with agilent gene expression data, of the tumors were classified as high-grade. thus, restricting to high grade serous tumors and removing the samples in the tcga that were part of the mayo clinic study, we included tumors in our analysis. summary of the tcga participants are presented in table  table summary of tcga and mayo clinic studies mayo clinic ovarian cancer study briefly, eligible cases were ascertained between and at the mayo clinic within 1 year of diagnosis with pathologically confirmed primary invasive high-grade serous eoc. progression and vital status were obtained from the mayo clinic tumor registry, electronic medical records, and active patient contact. all cases provided written informed consent for use of their tissues and medical records in research; all protocols were approved by the mayo clinic institutional review board. rna from fresh frozen tumors of each patient was extracted and assessed using agilent whole human genome 4 × 44 k expression arrays as previously described. the program “combat” was used to correct for batch-effects due to cy and cy labeling differences observed among experimental batches. summary of mayo clinic participants are presented in table  data used in this study can be found at the gene expression omibus. statistical and cmap analyses cox proportional hazard models were used to assess the association of gene expression with time to recurrence, by study with and without adjustment for age at diagnosis, stage, and debulking status. deg probes were selected for inclusion in the cmap analysis if p <  for the unadjusted analyses and p <  for the covariate adjusted analyses; different thresholds were used as cmap has a limit on the number of features included in any signature. these set of probes were then mapped to genes and then to the affymetrix id, as cmap is based on affymetrix probes/features. we conducted cmap analysis on individual genes with hazard ratio  >  coded as “positively” associated and genes with hr <  coded as “negatively” associated with ttr to determine a set of drugs for which our gene signatures matches the “reference” signature. clustering of samples based on gene expression levels was completed using recursive partition mixture models using the r package rpmm, restricting the number of levels to a maximum of in vitro drug screens in vitro drug cytotoxicity assays were conducted to determine which of the drugs highlighted by cmap analysis affected viability of eoc cells. the drugs were purchased from the following vendors: cotinine, 3-nitropropionic acid, adiphenine hydrochloride, ethosuximide, and podophyllotoxin; cephalexin and mitoxantrone; clemizole; wortmannin, doxorubicin, and 17-aag. upon receipt, dimethyl sulfoxide was used to prepare 10 mm stock solutions for all of the drugs except for cephalexin, which was prepared at a 5 mm concentration due to reduced solubility. single-use aliquots of the stock drug solutions were made and stored at −80 °c. all cell lines used in this study were obtained or derived at the fox chase cancer center. details of the origin of the eoc cell lines have been previously reported. each cell line was grown in rpmi containing 2 mm l-glutamine and supplemented with 10 % fbs, i.u./ml penicillin, 100 μg/ml streptomycin, and  μg/ml insulin and maintained at 37 °c in a humidified atmosphere with 5 % co cell lines were grown to 80 % confluency, harvested and seeded into 96-well plates at concentrations of to cells per well in a total volume of 95 μl. twenty-four hours after seeding, drug compounds were prepared using cell growth media and 5 μl of each were added to the seeded cells in the 96-well plates. a microlab nimbus pipetting robot was used to prepare the serial dilutions and for addition to the cell lines. the final drug solutions consisted of eight concentrations ranging from to  μm. vehicle-only wells were included on each plate to serve as interplate normalization controls. seventy-two hours following drug addition, a 1/5th volume of celltiter blue reagent was added directly to each well using a matrix wellmate. the plates were incubated at 37 °c for 150 min and the fluorescent signal was measured using an infinite® m pro microplate reader. the ratio of the fluorescent signal in a drug treated well to that of the average fluorescent signal from the vehicle treated wells on each plate multiplied by was used to calculate cell viability for each drug treated well for each cell line. a minimum of two biological replicates were performed for each cell line. the viability data were subjected to non-linear regression analysis and ic values calculated using prism software. all data in the viability curves are reported as mean ± standard error of the mean.  genes associated with clinical outcome to determine the gene signatures to use in cmap analyses, we determined the set of probes associated with ttr by fitting separate cox proportional hazards models within each study with and without adjustment for age at diagnosis, stage, and debulking status. comparing the genes associated with ttr with no adjustment for covariates at the significance level and the same direction of effect resulted in genes in common between tcga and mayo studies; genes had a hazard ratio  <  and genes had a hr > . in contrast, the adjusted analyses resulted in genes in common, with genes having a hr <  and having a hr >  table  presents the genes with p <  in both studies for both the adjusted and unadjusted analyses.table genes associated with epithelial ovarian cancer time to recurrence in analysis of tumors from the tcga and mayo clinic for both adjusted and unadjusted analyses ptprcap ubash3a ppbp pvrig igkv3-20 fcrl5 itk ighv3or16-12 vangl1 slc16a8 igkv3-20 acap1 vangl1 cd38 ela1 psme2 ubd note, three agilent probes did not have a current hugo gene id and are not presented in the table to further evaluate that these sets of genes were predictive of outcome, we clustered the samples based on the expression levels of the genes in common between the two studies using recursive partitioning mixture models. tcga and mayo clinic samples were clustered separately on the set of genes in common between the two studies. the resulting cluster assignments were then assessed for association with survival using log-rank tests. the log-rank p-values for testing cluster assignment with ttr were and for tcga and mayo clinic studies and and based on clustering of genes from model adjusting for covariates. connectivity mapping cmap analysis was completed for each of the deg sets using the cmap software developed at the broad institute. this resulted in: and drugs signatures were found to be either negatively or positively related with gene signatures based on analysis of tcga, tcga, mayo clinic and mayo clinic, respectively. we then looked at the overlap of the compounds found in both studies and found that compounds were in common between cmap analyses based on the covariate adjusted signature, as illustrated in fig.  this set of compounds included the following: tanespimycin, ethosuxiumide, cotinine, clemizole, 0175029–; wortmannin, 3-nitropropionic acid, adiphenine, cephaeline, doxorubicin, podophyllotoxin, mitoxantrone, cephalexin,. in vitro drug screens were completed using these drugs with the exception of 0175029– and for which commercial sources were not found and cephaeline which was cost prohibitive.fig. venn diagram depicting the overlap of compounds/drugs found from the cmap analyses in vitro drug screens in order to experimentally determine the effect of the eleven cmap-highlighted drugs on eoc cell viability, we conducted in vitro dose response studies on a set of eoc cell lines. a significant reduction in cell viability following 72 h of drug treatment was observed for five of these compounds: mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-aag. these drugs were known to be cytotoxic and therefore were expected to affect eoc cell growth and viability based on their mechanisms of action. the six remaining drugs failed to show in vitro anti-cancer activity, consistent with prior reports of minimal cytotoxic capacity. the dose response data for all drugs across the eoc cell lines can be found in additional file 3: figure s additional file 4: figure s additional file 5: figure s additional file 6: figure s additional file 7: figure s additional file 8: figure s additional file 9: figure s additional file 10: figure s additional file 11: figure s additional file 12: figure s and additional file 13: figure s fig. drug dose response curves for five compounds for ovarian cancer cell line a2780: mitoxantrone, podophyllotoxin, wortmannin, doxorubicin, and 17-aag discussion in this manuscript we have presented a bioinformatics approach for connectivity mapping based on clinical outcomes collected on a large patient population followed by functional validation of the identified drugs. the strengths of this approach are: non-reliance on a signature determined based on a small number of cancer cell lines; use clinically relevant outcomes that are directly tied to response; large clinical studies that provide the ability to look at overlap between cmap determined drugs; functional validation to confirm the ability of the discovered drugs to kill ovarian cancer cells. the choice of applying this approach to ovarian cancer was by design, as eoc treatment is somewhat uniform where the majority of patients undergo tumor debulking or cytoreduction surgery to remove as much of the tumor as possible, followed by platinum-taxane combination chemotherapy. application of this approach to other cancers with less standard treatments will introduce heterogeneity into clinical outcome and would need to be accounted for in the statistical analyses. nevertheless, there are still limitations to cmap analysis, which include: small sample size contributing to the cmap database, with none of the cell lines having been derived from eoc; “batch effects” in cmap cell line cultures; and the signatures are based on only gene expression measured using microarrays. this study is a proof of principle that clinical outcomes from large studies have the ability to be leveraged for drug discovery. of the drugs carried forward, we a priori hypothesized that of these drugs would affect eoc cell viability, for which all showed an ability to kill eoc cells in vitro for 72 h followed by measuring cell viability using the celltiter-blue assay). their anti-cancer activities have been studied extensively and mitoxantrone and doxorubicin have been used in the treatment of a variety of cancers. however, the cmap analyses of the tcga and mayo clinic studies did not identify the two most commonly used therapies for eoc, with none of the drugs identified having similar structure or mechanism of action to carboplatin or paclitaxel. however, three of the compounds are topoisomerase ii inhibitors, a class of drugs used often in the treatment of breast cancer, lung cancer, testicular cancer, lymphomas and sarcomas. the natural products podophyllotoxin, wortmannin, and 17-aag have proven highly toxic to human subjects but subsequent analogues such as etoposide, px- and ganetespib, respectively, have decreased side effects and are currently being investigated in the treatment of eoc. the six compounds that failed to exhibit significant in vitro cytotoxicity across the eoc cell line panel belong to classes of drugs related to central nervous system pathways, exhibit antimicrobial activity or are required in high micromolar concentrations which may not be physiologically achievable. interestingly, adiphenine was also identified using cmap analysis as an adjuvant therapy to treat the psychological distress associated with eoc diagnosis. while novel drugs were not identified, a filtered list of six was obtained for directed in vitro testing.  future research is needed to investigate the use of these cmap–like analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies. last of all, cmap analyses only determines candidate drugs that can be tested in future studies with no information provided on the optimal dose in humans; research into the optimal therapeutic dosage needs to be considered in the planning of future drug studies. additional files additional file 1: figure s results from clustering tumors from the tcga and mayo clinic studies. analyses were completed with genes found to be associated with clinical outcome in both studies. additional file 2: figure s results from clustering tumors from the tcga and mayo clinic studies. analyses were completed with genes found to be associated with clinical outcome in both studies. additional file 3: figure s the dose response data for 3-nitropropionic acid across the eoc cell lines. additional file 4: figure s. the dose response data for 17-aag across the eoc cell lines. additional file 5: figure s the dose response data for adiphenine hydrochloride across the eoc cell lines. additional file 6: figure s the dose response data for cephalexin across the eoc cell lines. additional file 7: figure s the dose response data for clemizole across the eoc cell lines. additional file 8: figure s the dose response data for cotinine across the eoc cell lines. additional file 9: figure s the dose response data for doxorubicin across the eoc cell lines. additional file 10: figure s the dose response data for ethosuximide across the eoc cell lines. additional file 11: figure s the dose response data for mitoxantrone across the eoc cell lines. additional file 12: figure s the dose response data for podophyllotoxin across the eoc cell lines. additional file 13: figure s the dose response data for wortmannin across the eoc cell lines. additional file 14: table s dge and cmap results. abbreviations cmapconnectivity mapping degsdifferentially expressed genes dmsodimethyl sulfoxide eocepithelial ovarian cancer hrhazard ratio rpmmrecursive partition mixture model semstandard error of the mean tcgathe cancer genome atlas ttrtime to recurrence 
connectivity mapping has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. future research is needed to investigate the use of these cmap and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies. each gene signature was inputted into cmap software to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the cmap analyses and the two studies were carried forward for validation studies involving drug screens on a set of eoc cell lines. therefore, we took a cmap approach based on tumor information and clinical endpoints from high grade serous eoc patients. of the drugs carried forward, five were known a priori to be cytotoxics and were indeed shown to effect eoc cell viability. we determined tumor gene expression signatures associated with time to recurrence among patients within tcga and, separately, from the mayo clinic. the standard treatment for patients with advanced eoc is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. epithelial ovarian cancer is the fifth leading cause of cancer death among women in the united states.
 epithelial ovarian cancer is the fifth leading cause of cancer death among women in the united states. the standard treatment for patients with advanced eoc is initial debulking surgery followed by carboplatin-paclitaxel combination chemotherapy. unfortunately, with chemotherapy most patients relapse and die resulting in a five-year overall survival around 45 %. thus, finding novel therapeutics for treating eoc is essential. connectivity mapping has been used widely in cancer drug discovery and generally has relied on cancer cell line gene expression and drug phenotype data. therefore, we took a cmap approach based on tumor information and clinical endpoints from high grade serous eoc patients. methods we determined tumor gene expression signatures associated with time to recurrence among patients within tcga and, separately, from the mayo clinic. each gene signature was inputted into cmap software to determine a set of drugs for which our signature “matches” the “reference” signature, and drugs that overlapped between the cmap analyses and the two studies were carried forward for validation studies involving drug screens on a set of eoc cell lines.  of the drugs carried forward, five were known a priori to be cytotoxics and were indeed shown to effect eoc cell viability.  future research is needed to investigate the use of these cmap and similar analyses for determining combination therapies that might work synergistically to kill cancer cells and to apply this in silico bioinformatics approach using clinical outcomes to other cancer drug screening studies. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords gene expression signaturetime to recurrencebioinformaticsconnectivity mappingdrug discoveryovarian cancerhttp://dx.doi.org/ /100000054national cancer institutep ca168524r ca182715fridley brooke l. http://dx.doi.org/ /100000057national institute of general medical sciencesp gm10418fridley brooke l. http://dx.doi.org/ /100000002national institutes of healthkl dh052027pathak harsh b. issue-copyright-statement© the author 2016 