Drug Sensitivity In Cancer Project Research Articles

GSK2126458 has the potential to inhibit the proliferation of pancreatic cancer uncovered by bioinformatics analysis and pharmacological experiments

BackgroundPancreatic cancer is one of the most serious digestive malignancies. At present, there is an extreme lack of effective strategies in clinical treatment. The purpose of this study is to identify key genes and pathways in the development of pancreatic cancer and provide targets for the treatment of pancreatic cancer.MethodsGSE15471 and GSE62165 were used to screen differentially expressed genes by GEO2R tool. Hub genes prognostic potential assessed using the GEPIA and Kaplan–Meier plotter databases. The drug susceptibility data of pan-cancer cell lines is provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC). Finally, the effects of PI3K–Akt signaling pathway inhibitors on cell viability of pancreatic cancer cells were detected by cell proliferation and invasion assays.ResultsA total of 609 differentially expressed genes were screened and enriched in the focal adhesion, phagosome and PI3K–Akt signaling pathway. Of the 15 hub genes we found, four were primarily associated with the PI3K–Akt signaling pathway, including COL3A1, EGF, FN1 and ITGA2. GDSC analysis showed that mTOR inhibitors are very sensitive to pancreatic cancer cells with mutations in EWSR1.FLI1 and RNF43. Cell proliferation and invasion results showed that mTOR inhibitors (GSK2126458) can inhibit the proliferation of pancreatic cancer cells.ConclusionsThis study suggested that the PI3K–Akt signaling pathway may be a key pathway for pancreatic cancer, our study uncovered the potential therapeutic potential of GSK2126458, a specific mTOR inhibitor, for pancreatic cancer.

The Novel Imipridone ONC212 Induces Pronounced Anti-Leukemia Effects in Vitro and In Vivo and Is Highly Synergistic with the BCL-2 Inhibitor ABT-199

Imipridones constitute a novel class of molecules that includes the first-in-class anti-cancer compound ONC201. ONC201 has shown promising clinical activity against hematological and solid malignancies (Allen et al., Oncotarget, 2016). ONC212 is a second-generation imipridone with higher potency than ONC201. We first confirmed the overall in vitro anti-tumor effects of ONC212 using the collection of 1,088 human cancer cell lines available from the Genomic of Drug Sensitivity in Cancer Project. Ranking the sensitivity of these cancer cell lines to ONC212 by tumor type revealed that leukemias and lymphomas were especially sensitive. In fact, ONC212 exerted prominent apoptogenic effects in AML cell lines with > 20-fold potency compared to ONC201 (ED50s at 72 hr; 6.4 μM vs 258.7 nM, respectively for OCI-AML3 cells). Similar results were observed in primary AML cells, but there was no activity against normal bone marrow (BM) cells. We previously demonstrated that ONC201 induces apoptosis via an atypical integrated stress response (ISR, Ishizawa et al., Sci Signal, 2016). Similarly, ONC212 showed ISR activation accompanying its apoptogenic effects in AML cells which was agnostic to the mutation status of p53. However, because of the higher sensitivity of wild-type p53 cells to ONC212, we speculated that this effect could also reflect a p53-dependent apoptosis mechanism. Indeed, p53 knockdown reduced the ONC212-induced apoptosis in MV4;11 and MOLM13 cells, suggesting both, p53-dependent and -independent, mechanisms.We next investigated the effects of ONC212 in vivo in a systemic AML xenograft model using OCI-AML3 cells expressing luciferase. Biweekly oral administration of 50 mg/kg ONC212 markedly inhibited AML expansion and prolonged overall survival (p = 0.0003). Median survival increased from 43 d in controls to 49 d in the ONC212-treated group (+14%). For in vivo functional assessment of ONC212's anti-tumor effects against leukemia stem and progenitor cells, we utilized our patient-derived xenograft (PDX) mouse model. We treated primary PDX cells with ONC212 (250 nM, 36 hr) ex vivo, and then injected identical numbers of viable cells (treated or untreated) into recipient NSG mice. After one month, the percentages of human CD45+ cells in the peripheral blood, spleen, and BM were significantly decreased in the ONC212-treated group. The median survival in this group was remarkably different: 82 d compared to 36 d for controls (+128%, p<0.0001). These results indicated that ONC212 targets AML stem cells with potential to eradicate the disease.Because BCL-2 is generally considered protective against ISR-mediated apoptosis and ONC212 induced p53-mediated apoptosis as demonstrated above, we hypothesized that the BCL-2 inhibitor ABT-199 or the p53 activator nutlin-3a could further sensitize AML cells to ONC212. Indeed, the in vitro combination of ONC212 plus ABT-199 or nutlin-3a synergistically induced apoptosis in AML cells. Furthermore, the combination of ONC212 and ABT-199 showed highly significant synergistic anti-leukemia effects in vivo in a MOLM-13 xenograft mouse model. The combinatorial treatment prolonged overall median survival from 20 d for untreated controls to 21 d for each agent as monotherapy to 30 d for the combination.Since the G-protein-coupled receptor (GPCR) dopamine receptor D2 is the putative target of first-generation imipridone ONC201, we performed a PathHunter β-arrestin screening to determine if ONC212 also bound GPCRs. ONC212 did not engage DRD2 but instead specifically engaged the orphan GPCR GPR132, which is a putative tumor suppressor when activated. Interestingly, expression of GPR132 mRNA was highly correlated with sensitivity to ONC212. Furthermore, GPR132 overexpression induced cell death and ONC212 treatment induced GPR132 mRNA expression in AML cell lines, suggesting that GPR132 could be a potential therapeutic target in hematological malignancies.Taken together, ONC212, and its putative target GPR132, represent a promising novel anti-cancer strategy for AML therapy that warrants further development. DisclosuresPrabhu:Oncoceutics: Employment, Equity Ownership. Allen:Oncoceutics: Employment, Equity Ownership, Patents & Royalties. Oster:Oncoceutics: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Stogniew:Oncoceutics: Employment. Andreeff:Daiichi Sankyo: Consultancy.

Abstract LB-121: The novel ATR inhibitor M4344 and CHK1 inhibitor SRA737 overcome chemoresistance in SLFN11-negative cells in combination treatment with DNA-damaging agents

Abstract Precision medicine is an unmet need for DNA-damaging agents with potentially severe side effects. Loss of Schlafen 11 (SLFN11) expression is frequently detected in ~50% cancer cell lines of the NCI, the Broad Institute cancer cell line panel (CCLE), and the Genomics of Drug Sensitivity in Cancer project (GDSC). Deficiency of SLFN11 expression causes chemoresistance to a broad range of DNA-damaging agents, including topoisomerase I (TOP1) inhibitors, topoisomerase II (TOP2) inhibitors, alkylating agents, DNA synthesis inhibitors and PARP inhibitors. Moreover, gene silencing of SLFN11 is observed in small cell lung cancer patient-derived xenograft models with acquired drug resistance. To identify synthetic lethal therapeutic targets to overcome chemoresistance in SLFN11 deficient cells, we performed a genome-wide RNAi screen with the human druggable genome siRNA library by using camptothecin (CPT), a TOP1 inhibitor, in SLFN11 wild-type (WT) and knock-out (KO) prostate cancer DU145 cells. Gene Ontology analysis identified the inhibition of ATR-mediated DNA repair pathway genes (ATR, CHK1, BRCA2 and RPA1) is synergistic with CPT in SLFN11 KO cells, whereas inhibition of the RNA metabolism-related genes (POLR2, PSMD3, POLR2F and PSMD11) had higher combination effects in SLFN11 WT cells. The synergistic effects by depletion of ATR-mediated DNA repair pathway in SLFN11 KO cells were validated by additional siRNAs-mediated cell viability assays. To determine whether inhibition of ATR-mediated pathway can be applied clinically, we tested the new clinical ATR (M4344) and CHK1 (SRA737) inhibitors. Treatment with non-toxic-doses of M4344 and SRA737 reversed drug resistance of the SLFN11 KO cells to TOP1 inhibitors [CPT, and clinically used topotecan and LMP400 (indotecan)]. Additionally, the same synergistic effects by combination of the ATR/CHK1inhibitors and CPT were detected in isogenic CCRF-CEM SLFN11 WT and KO leukemic lymphoblasts cells and non-isogenic DMS114 and H446 small cell lung cancer cells. We also confirmed synergy with ATR/CHK1 inhibitors in combination of other clinical DNA-damaging agents (TOP2 inhibitor: etoposide, alkylating agent: cisplatin, and PARP inhibitor: talazoparib). Molecular changes induced by the combination treatment were examined in the cell cycle by assessing DAPI and EdU incorporation, cell death with Annexin V, and DNA damage response by confocal microscopy with M4344 and CPT. Co-treatment with ATR inhibitor and CPT resulted in G2/M arrest and apoptotic cell death, and formation of micronuclei and fragmented nuclei in SLFN11 KO cells, compared with SLFN11 WT cells. Collectively, our results provide a new therapeutic rationale for the clinical development of combination treatments of chemotherapeutic DNA-targeted agents with ATR/CHK1 inhibitors based on SLFN11 status. Citation Format: Ukhyun Jo, Yasuhisa Murai, Junko Murai, Shar-yin N Huang, Sirisha Chakka, Lu Chen, Ken Cheng, Yves Pommier. The novel ATR inhibitor M4344 and CHK1 inhibitor SRA737 overcome chemoresistance in SLFN11-negative cells in combination treatment with DNA-damaging agents [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-121.

Abstract 2099: Assessing cancer drug response prediction from gene expression

Abstract Customizing treatments targeted to a patient specific disease landscape remains an open challenge in precision medicine. Gene expression data, generated through next generation sequencing technologies, has shown significant power for supervised machine learning approaches. Here, we evaluated neural network performance at predicting cancer cell-line specific drug sensitivity. Gene expression data for 17,737 genes across 1014 human cancer cell-lines with IC50 concentrations for 251 anti-cancer drugs were obtained from the Genomics of Drug Sensitivity in Cancer Project. Cell-lines were considered sensitive if IC50 concentration was below maximum drug concentration. For neural network input, gene expression values were scaled to a range of -0.5 to 0.5 through log transformation, z-scoring, subtracting the minimum and dividing by the range. A hyperparameter optimized neural network model consisting of two hidden layers of size 1024 and 512 respectively resulted in a test set accuracy ranging from 60.2% to 67.8% across drugs compared to an average baseline performance of 53.4%. Dimension reduction through clustering and Nearest Neighbor Networks did not result in improved performance. We found that classical machine learning classification methods including support vector machines showed comparable performance to our neural network approach. However, neural network cross-training on drugs sharing the same putative targets (e.g. AURKA) led to a 5.03% improvement in neural network prediction accuracy. Overall, our analysis shows that utilizing gene expression profiles independent of other -omics data for cancer drug response prediction through machine learning frameworks offers modest predictive capabilities. To increase performance, we suggest augmenting training size through shared pathway cross-training, optimizing feature encoding to maximize neural network predictive capabilities, and incorporating other -omics data. Citation Format: James Talwar, Hannah Carter. Assessing cancer drug response prediction from gene expression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2099.

Database Drives Biomarker Research.

The Genomics of Drug Sensitivity in Cancer project recently added 4 years of additional data to a large-scale database designed to identify predictive biomarkers for cancer therapies. Freely available online, these data are driving research on the relationship between genomic features and drug responses to better tailor treatment to individual patients.

A novel mutation panel for predicting etoposide resistance in small-cell lung cancer.

PurposePlatinum-based chemotherapy, consisting of etoposide and cisplatin (EP), has been the cornerstone of therapy for extensive-stage small-cell lung cancer (ES-SCLC) for decades. Despite the marked initial sensitivity of SCLC to chemotherapy, EP regimens cannot avoid the emergence of drug resistance in clinical practice. With the rise of new chemotherapy regimens in recent years and the primary resistance or insensitivity of ES-SCLC to EP regimens, it is desirable to be able to identify patients with resistant or insensitive ES-SCLC.MethodsThe sequencing and drug sensitivity data of SCLC cell lines were provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC). The data regarding sensitivity to etoposide of 54 SCLC cell lines were analyzed, and etoposide-sensitive cell lines and etoposide-resistant cell lines were differentiated according to the IC50 values defined by the GDSC. ROC curve analysis was performed on all mutations and combinations of mutations to select the optimal panel to predict resistance to etoposide.ResultsROC analysis of etoposide resistance revealed that the most significant single gene mutation indicating resistance to etoposide was CSMD3, and the accuracy of predicting resistance to etoposide proved to be the highest when there was any mutation in CSMD3/PCLO/RYR1/EPB41L3, area under the curve =0.804 (95% confidence interval: 0.679–0.930,P<0.001).ConclusionThis study found that a panel with four genes (CSMD3, EPB41L3, PCLO, and RYR1) can accurately predict sensitivity to etoposide. These findings provide new insights into the overall treatment for patients with ES-SCLC that is resistant or insensitive to etoposide.

A novel mutation panel for predicting etoposide in small cell lung cancer.

e20090 Background: Platinum-based chemotherapy, consisting of etoposide and cisplatin (EP), has been the cornerstone of therapy for extensive-stage small cell lung cancer (ES-SCLC) for decades. Despite the marked initial sensitivity of SCLC to chemotherapy, EP regimens cannot avoid the emergence of drug resistance in clinical practice. With the rise of new chemotherapy regimens in recent years and the primary resistance or insensitivity of ES-SCLC to EP regimens, it is desirable to be able to identify patients with resistant or insensitive ES-SCLC. Methods: The sequencing and drug sensitivity data of SCLC cell lines were provided by The Genomics of Drug Sensitivity in Cancer Project (GDSC); the data regarding of sensitivity to etoposide of 54 SCLC cell lines were analyzed, and etoposide-sensitive cell lines and etoposide-resistant cell lines were differentiated according to the IC50 values defined by the GDSC. ROC curve analysis was performed on all mutations and combinations of mutations to select the optimal panel to predict resistance to etoposide. Results: Receiver Operating Characteristic(ROC) analysis of etoposide resistance revealed that the most significant single gene mutation indicating resistance to etoposide was CSMD3, and the accuracy of predicting resistance to etoposide proved to be the highest when there was any mutation in CSMD3/PCLO/RYR1/EPB41L3, area under the curve (AUC) = 0.804 (95% confidence interval (CI): 0.679-0.930, p &lt; 0.001). Conclusions: This study found that a panel with four genes (CSMD3, EPB41L3, PCLO, and RYR1) can accurately predict sensitivity to etoposide. These findings provide new insights into the overall treatment for patients with ES-SCLC that is resistant or insensitive to etoposide.

Dual-Layer Strengthened Collaborative Topic Regression Modeling for Predicting Drug Sensitivity.

An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.

Abstract 4957: The novel imipridone ONC212 highly synergizes with the BCL-2 inhibitor ABT-199 in AML and activates orphan receptor GPR132

Abstract Imipridones are first-in-class anti-tumor compounds including ONC201, which has shown promising clinical activity. ONC212 was designed as a second-generation imipridone. We first confirmed the ONC212 effects in a collection of 1,088 human cancer cell lines available from the Genomic of Drug Sensitivity in Cancer Project; leukemia was identified as the most sensitive tumor type. In fact, ONC212 exerted prominent apoptogenic effects in acute myeloid leukemia (AML) cell lines and primary AML, but not normal bone marrow (BM) cells. We investigated the effects of ONC212 in vivo in an aggressive systemic AML xenograft model using OCI-AML3 cells. ONC212 markedly inhibited AML expansion and prolonged median survival (controls: 43 d, ONC212: 49 d; p = 0.0003). For in vivo functional assessment of ONC212's anti-tumor effects against leukemia stem and progenitor cells (LSPCs), we treated patient-derived xenograft (PDX) cells with ONC212 (250 nM, 36 hr) ex vivo, and then injected into recipient NSG mice. After one month, the human leukemic CD45+ cells in the peripheral blood, spleen, and BM were significantly decreased in the ONC212 treated group. The median survival was remarkably prolonged (controls: 36 d, ONC212: 82 d; p &amp;lt; 0.0001). These results indicate that ONC212 has anti-LSPC effects to reduce the engraftment potential. We previously demonstrated that the prototype it compound ONC201 induces apoptosis via an atypical integrated stress response (ISR; Ishizawa et al., Sci Signal, 2016). As expected, ONC212 induced the transcription factor ATF4, a key effector of ISR. Because BCL-2 is generally considered to be protective against ISR-mediated apoptosis, we hypothesized that the BCL-2 inhibitor ABT-199 could further sensitize AML cells to ONC212. Indeed, the in vitro combination of ONC212 plus ABT-199 synergistically induced apoptosis in AML cells. Furthermore, the combination showed highly significant synergistic anti-leukemia effects in vivo. The combinatorial treatment prolonged overall median survival (controls: 20 d, each agent: 21 d, the combination 30 d; p &amp;lt; 0.0001). Since the G-protein-coupled receptor (GPCR) dopamine receptor D2 is the putative target of ONC201, we hypothesized that ONC212 also targets GPCRs. The PathHunter β-arrestin screening discovered that ONC212 specifically activated the orphan GPCR GPR132. Consistently, the GPR132 mRNA expression was correlated with ONC212 sensitivity. On the other hand, GPR132 overexpression induced cell death in AML cells, which is consistent with previous reports implicating GPR132 as a tumor suppressor. Furthermore, ONC212 increased GPR132 mRNA expression. These results suggest that GPR132 could be a potential therapeutic target in AML. Taken together, ONC212 has potential as a novel agent for AML therapy. This study provides the first reported opportunity to therapeutically target GPR132 in oncology. Citation Format: Takenobu Nii, Jo Ishizawa, Varun V. Prabhu, Vivian Ruvolo, Neel Madhukar, Ran Zhao, Hong Mu, Lauren Heese, Kensuke Kojima, Mathew Garnett, Ultan McDermott, Cyril Benes, Neil Charter, Sean Deacon, Olivier Elemento, Joshua Allen, Wolfgang Oster, Martin Stogniew, Michael Andreeff. The novel imipridone ONC212 highly synergizes with the BCL-2 inhibitor ABT-199 in AML and activates orphan receptor GPR132 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4957.

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma.

Objective:Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib.Materials and Methods:We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was “Robust Multi-array analysis” normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis.Results:Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways.Conclusion:The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.

The dynamics of gene expression changes in a mouse model of oral tumorigenesis may help refine prevention and treatment strategies in patients with oral cancer.

A better understanding of the dynamics of molecular changes occurring during the early stages of oral tumorigenesis may help refine prevention and treatment strategies. We generated genome-wide expression profiles of microdissected normal mucosa, hyperplasia, dysplasia and tumors derived from the 4-NQO mouse model of oral tumorigenesis. Genes differentially expressed between tumor and normal mucosa defined the “tumor gene set” (TGS), including 4 non-overlapping gene subsets that characterize the dynamics of gene expression changes through different stages of disease progression. The majority of gene expression changes occurred early or progressively. The relevance of these mouse gene sets to human disease was tested in multiple datasets including the TCGA and the Genomics of Drug Sensitivity in Cancer project. The TGS was able to discriminate oral squamous cell carcinoma (OSCC) from normal oral mucosa in 3 independent datasets. The OSCC samples enriched in the mouse TGS displayed high frequency of CASP8 mutations, 11q13.3 amplifications and low frequency of PIK3CA mutations. Early changes observed in the 4-NQO model were associated with a trend toward a shorter oral cancer-free survival in patients with oral preneoplasia that was not seen in multivariate analysis. Progressive changes observed in the 4-NQO model were associated with an increased sensitivity to 4 different MEK inhibitors in a panel of 51 squamous cell carcinoma cell lines of the aerodigestive tract. In conclusion, the dynamics of molecular changes in the 4-NQO model reveal that MEK inhibition may be relevant to prevention and treatment of a specific molecularly-defined subgroup of OSCC.

Abstract AS02: Network-based stratification: combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma

Abstract Background: Like many solid tumors, high grade serous ovarian carcinoma (HGS-OvCa) is a heterogeneous entity with a widely variable clinical course even among patients of the same stage and histological subtype. We have recently developed a method called Network-based Stratification (NBS), which combines genome-scale somatic mutation profiles with genetic interaction networks and performs unsupervised clustering of patients into subtypes. Remarkably, patients in these subtypes display distinct biological and clinical characteristics. Methods: Briefly, somatic mutations for each patient are represented as a profile of binary (1,0) states on genes, in which a ‘1’ indicates a mutated gene. For each patient we project mutation profiles onto a human gene interaction network, then apply network propagation to spread the influence of each subsampled mutation profile over its network neighborhood. These ‘network-smoothed’ profiles are then clustered into a predefined number of subtypes using the unsupervised technique of non-negative matrix factorization (NMF). Results: Using the NBS technique four distinct subtypes were identified from the TCGA HGS-OvCa cohort of 330 exome sequenced and clinically-annotated tumors. The median overall survival (OS) in months for these four subtypes was 36, 48, 56, and not-yet-reached respectively, which was highly significant (Logrank p = 1.59 x 10-5). No significant differences between subtypes were found in age, tumor stage at presentation, or percentage of patients with an optimal surgical resection. Furthermore, the survival difference is significant when comparing a Cox proportional hazards model of NBS subtypes together with the above factors and mutation rate is compared to a model of these factors alone (Likelihood ratio test p = 3.3x10-4). Each subtype exhibits a distinct set frequently mutated pathways, with subtype 1 (which had the worst OS) enriched for mutation in the fibroblast growth factor receptor, caspase and nucleoskeleton pathways, subtype 2 enriched for ‘classic’ HGS-OvCa mutations including mutation in DNA damage response genes. Recognizing that full exome sequencing is not yet standard of care, we have built a limited classifier that only requires as input the mutational status of 50 genes. This 50 gene classifier performed with 94% accuracy and 80% precision relative to full exome. Using this 50 gene classifier we have stratified an independent cohort of 93 Australian HGS-OvCa patients from the ICGC and found survival relationships similar to what was seen in the TCGA cohort (median OS of 22 months for subtype 1 vs. not-yet-reached for other subtypes, Logrank p = 3.6x10-4). The 50 gene NBS classifier was also applied to a panel of cell lines from The Genomics of Drug Sensitivity in Cancer Project. Cell lines classified as subtype 1 had significantly higher cisplatin IC50 relative to other subtypes, whereas there was no difference in docetaxel or paclitaxel IC50. Citation Format: John Paul Shen, Matan Hofree, Ana Bojorquez-Gomez, Cheryl Saenz, Trey Ideker. Network-based stratification: combining genome-scale somatic mutation profiles with genetic interaction networks to identify clinically relevant subtypes in high grade serous ovarian carcinoma [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS02.

Mining cancer genomes in COSMIC

COSMIC, The Catalogue Of Somatic Mutations In Cancer [http://cancer.sanger.ac.uk], is one of the largest repositories for somatic mutational events in human cancer. Data in COSMIC are curated from multiple sources, including from over 14,310 scientific publications, and are alongside data from the Cancer Genome Project at the Sanger Institute and global international consortia, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. The COSMIC database currently accommodates over 300,000 mutations across 750,000 analyzed samples from 21,850 genes (COSMIC v60, July 2012). The Cancer Gene Census [http://cancer.sanger.ac.uk/cancergenome/projects/census/] is a list of almost 500 known cancer genes for which mutations have been identified as causally implicated in cancer. These genes are prioritized for full literature curation. The collection of whole exome and genome sequencing data in COSMIC continues to grow at a rapid pace. There are: 17,614 coding mutations, 84,747 non-coding variants in 396 whole genome screens; 121,619 coding mutations and 12,949 non-coding variants as result of 1,266 full exome sequencing; 3,512 structural mutations derived from 77 rearrangement screens. The data overview for each whole genome screen is presented using Circos, for example, the NCI-H209 Circos summary [http://cancer.sanger.ac.uk/cosmic/sample/overview?id=688013]. Analyzing information from whole genome sequencing can greatly enhance the chance of discovering novel genes implicated in human cancer. Unlike hot spot screening of gene regions where somatic mutations are most frequent, the use of whole genome data can identify all mutations in all genes, providing much more expansive annotations to recurrence analysis as used to discover new cancer genes. For instance, there are recurrent somatic mutations identified in genes, for example: SPOP in 19 prostate samples; SDK1 in 20 large-intestine samples. There are several ways to access and analyze the data in COSMIC. The website allows data viewing in a genomic context supported by GBrowse while maintaining our gene-centric perspective. New additional features include a filter for excluding identified SNPs from the 1000 Genomes Project, and displaying Pfam domains and links to biological pathways for selected genes. For mining a large dataset, COSMICmart (an instance of BioMart) is a tool for downloading user-customized datasets federated with external databases such as Ensembl and Uniprot. Moreover, we provide data export in multiple formats and Oracle database export through the FTP site [ftp://ftp.sanger.ac.uk/pub/CGP/cosmic]. In addition to somatic mutation data, we have integrated the data from the Genomics of Drug Sensitivity in Cancer Project [http://www.cancerrxgene.org], which is screening a wide range of anticancer therapeutics against over 1,000 genetically characterized human cancer cell lines. Data analysis is becoming increasingly challenging due to the rapid expansion in cancer genome sequencing capacity. COSMIC is a major cancer genetics resource aiming to help such investigations, providing a centralized somatic mutations database with a wide suite of tools for its examination.

COSMIC: the catalogue of somatic mutations in cancer

The Catalogue Of Somatic Mutations In Cancer (COSMIC) [1] is one of the largest repositories of information on somatic mutations in human cancer. The project has been running for more than ten years as part of the Cancer Genome Project (CGP) at the Wellcome Trust Sanger Institute in the UK. The data in COSMIC are curated from a variety of sources, primarily the scientific literature and large international consortia. The project includes information from the CGP, along with data from other consortia such as the International Cancer Genome Consortium and The Cancer Genome Atlas. In addition, COSMIC is regularly updated with the genes highlighted in the Cancer Gene Census, which curates the scientific literature for known cancer genes [2]. With the advent of whole exome and genome sequencing technology, the amount of data in COSMIC is increasing rapidly. The recent COSMIC release (version 53; 18 May 2011) contains 608,042 tumor and cell line samples, annotating 176,856 mutations across 19,439 genes, with 352 full exomes, 43 whole genome rearrangement screens and 4 full genomes now available. The data are updated regularly, with new releases scheduled every two months. COSMIC provides a large number of graphical and tabular views for interpreting and mining the large quantity of information, as well as the facility to export the relevant data in various formats. The website can be navigated in many ways to examine mutation patterns on the basis of genes, samples and phenotypes, which are the main entry points to COSMIC. COSMIC also provides various options to browse the data in a genomic context. Integration with the Ensembl genome browser allows the visualization of full genome annotations, together with COSMIC data, on the GRCh37 genome coordinates. COSMIC also contains its own genome browser, which facilitates data analysis by combining genome-wide gene structures and sequences with rearrangement breakpoints, copy number variations and all somatic substitutions, deletions, insertions and complex gene mutations. The main COSMIC website [1] encompasses all of the available data. However, within COSMIC, the Cancer Cell Line Project [3] is a specialized component, which provides details of the genotyping of almost 800 commonly used cancer cell lines, through the set of known cancer genes. Its focus is to identify driver mutations, or those likely to be implicated in the oncogenesis of each tumor. This information forms the basis for integrating COSMIC with the Genomics of Drug Sensitivity in Cancer project [4], which is a joint effort with the Massachusetts General Hospital [5] to screen this panel of cancer cell lines against potential anticancer therapeutic compounds to investigate correlations between somatic mutations and drug sensitivity. Data on somatic mutations in cancer are being produced at a rapidly increasing rate, and the combined analysis of large distributed datasets is becoming ever more difficult. However, COSMIC curates and standardizes this information in a single database, providing user-friendly browsing tools and analytical functions, thus ensuring its role as a key resource in human cancer genetics.