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Chromatin regulators included in this study

Clinical characteristics of the Institut Curie patient cohort

Supplementary figure 1. The protein levels of SLFN11 in 25 EWS-FLI1-expressing cells (black) and 4 EWS-ERG-expressing cells (red) were detected by Western blotting using antibodies against SLFN11 (98 kDa) and actin (42 kDa, loading control). The relative SLFN11 levels of each cell line were normalized to actin levels and the SLFN11/actin ratio of TC-106 was set as 1.

Supplementary figure 2. Scatterplots showing correlations between FLI1 expression (x axis, Log2 normalized intensity) and ETS1 expression (y axis, Log2 normalized intensity) in breast and prostate cancers. n, number of samples; r, correlation coefficient.

Purpose: SLFN11 was identified as a critical determinant of response to DNA-targeted therapies by analyzing gene expression and drug sensitivity of NCI-60 and CCLE datasets. However, how SLFN11 is regulated in cancer cells remained unknown. Ewing sarcoma, which is characterized by the chimeric transcription factor EWS-FLI1, has notably high SLFN11 expression, leading us to investigate whether EWS-FLI1 drives SLFN11 expression and the role of SLFN11 in the drug response of Ewing sarcoma cells.Experimental Design: Binding sites of EWS-FLI1 on the SLFN11 promoter were analyzed by chromatin immunoprecipitation sequencing and promoter-luciferase reporter analyses. The relationship between SLFN11 and EWS-FLI1 were further examined in EWS-FLI1-knockdown or -overexpressing cells and in clinical tumor samples.Results: EWS-FLI1 binds near the transcription start site of SLFN11 promoter and acts as a positive regulator of SLFN11 expression in Ewing sarcoma cells. EWS-FLI1–mediated SLFN11 expression is responsible for high sensitivity of Ewing sarcoma to camptothecin and combinations of PARP inhibitors with temozolomide. Importantly, Ewing sarcoma patients with higher SLFN11 expression showed better tumor-free survival rate. The correlated expression between SLFN11 and FLI1 extends to leukemia, pediatric, colon, breast, and prostate cancers. In addition, expression of other ETS members correlates with SLFN11 in NCI-60 and CCLE datasets, and molecular experiments demonstrate that ETS1 acts as a positive regulator for SLFN11 expression in breast cancer cells.Conclusions: Our results imply the emerging relevance of SLFN11 as an ETS transcription factor response gene and for therapeutic response to topoisomerase I inhibitors and temozolomide–PARP inhibitor combinations in ETS-activated cancers. Clin Cancer Res; 21(18); 4184–93. ©2015 AACR.See related commentary by Kovar, p. 4033

Summary CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness., Graphical Abstract, Highlights • CellMinerCDB integrates pharmacogenomic data of the major cancer cell line databases • It seamlessly enables genomic and drug data exploration within and across databases • It tests genomic data reproducibility and proposes drug response determinants • We expand the GDSC drug panel and advance LIX1L as a novel mesenchymal gene, Genomics; Bioinformatics; Biological Database; Cancer Systems Biology

The analgesic drug dipyrone is used to treat side effects (including pain and fever) of cancer chemotherapeutic agents. Dipyrone is metabolized to 4-aminoantipyrine (4-AA), a PGE2-dependent blocker and inhibitor of cyclooxygenase (COX). We evaluated the genotoxic, mutagenic, apoptotic, and immunomodulatory activities of 4-AA in vivo and the effects of its combination with the antineoplastic drugs doxorubicin, cisplatin, and cyclophosphamide. 4-AA did not cause genotoxic/mutagenic damage, splenic phagocytosis, or leukocyte alterations. However, when combined with the antineoplastic agents, 4-AA decreased their genotoxic, mutagenic, apoptotic, and phagocytic effects. These results suggest that 4-AA might interfere with DNA damage-mediated chemotherapy.

As precision medicine demands molecular determinants of drug response, CellMinerCDB provides (https://discover.nci.nih.gov/cellminercdb/) a web-based portal for multiple forms of pharmacological, molecular, and genomic analyses, unifying the richest cancer cell line datasets (NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables genomic and pharmacological data queries for identifying pharmacogenomic determinants, drug signatures, and gene regulatory networks for researchers without requiring specialized bioinformatics support. It leverages overlaps of cell lines and tested drugs to allow assessment of data reproducibility. It builds on the complementarity and strength of each dataset. A panel of 41 drugs evaluated in parallel in the NCI-60 and GDSC is reported, supporting drug reproducibility across databases, repositioning of bisacodyl and acetalax for triple negative breast cancer, and identifying novel drug response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins in development. CellMinerCDB also allowed the identification of LIX1L as a novel mesenchymal gene regulating cellular migration and invasiveness.

CellMinerCDB provides a web-based resource and portal for integrating multiple forms of pharmacological and genomic analyses, unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and identifying pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs. A panel of 41 drugs was evaluated in parallel in the NCI-60 and GDSC, supporting reproducibility and further pathway analyses across databases. We demonstrate the value of CellMinerCDB for elucidating gene expression determinants, such as methylation and copy number variation; highlight difficulties in assessing mutation burden; the dominant role of SLFN11 for drug response; novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins; drug repositioning for bisacodyl and acetalax for triple negative breast cancer; and the discovery of LIX1L as a novel mesenchymal gene regulating cancer cellular migration and invasiveness.

Loss of function of DNA repair (DNAR) genes is associated with genomic instability and cancer predisposition; it also makes cancer cells reliant on a reduced set of DNAR pathways to resist DNA-targeted therapy, which remains the core of the anticancer armamentarium. Because the landscape of DNAR defects across numerous types of cancers and its relation with drug activity have not been systematically examined, we took advantage of the unique drug and genomic databases of the US National Cancer Institute cancer cell lines (the NCI-60) to characterize 260 DNAR genes with respect to deleterious mutations and expression down-regulation; 169 genes exhibited a total of 549 function-affecting alterations, with 39 of them scoring as putative knockouts across 31 cell lines. Those mutations were compared to tumor samples from 12 studies of The Cancer Genome Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE). Based on this compendium of alterations, we determined which DNAR genomic alterations predicted drug response for 20,195 compounds present in the NCI-60 drug database. Among 242 DNA damaging agents, 202 showed associations with at least one DNAR genomic signature. In addition to SLFN11, the Fanconi anemia-scaffolding gene SLX4 (FANCP/BTBD12) stood out among the genes most significantly related with DNA synthesis and topoisomerase inhibitors. Depletion and complementation experiments validated the causal relationship between SLX4 defects and sensitivity to raltitrexed and cytarabine in addition to camptothecin. Therefore, we propose new rational uses for existing anticancer drugs based on a comprehensive analysis of DNAR genomic parameters.

Purpose: The rcellminer R package provides a wide range of functionality to help R users access and explore molecular profiling and drug response data for the NCI-60. The package enables flexible programmatic access to CellMiner’s unparalleled breadth of NCI-60 data, including gene and protein expression, copy number, whole exome mutations, as well as activity data for ∼21K compounds, with information on their structure, mechanism of action and repeat screens. Functions are available to easily visualize compound structures, activity patterns and molecular feature profiles. Additionally, embedded R Shiny applications allow interactive data exploration. Availability and implementation: rcellminer is compatible with R 3.2 and above on Windows, Mac OS X and Linux. The package, documentation, tutorials and Shiny-based applications are available through Bioconductor (http://www.bioconductor.org/packages/rcellminer); ongoing updates will occur according to the Bioconductor release schedule with new CellMiner data. The package is free and open-source (LGPL 3). Contact: lunaa@cbio.mskcc.org or vinodh.rajapakse@nih.gov

The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from Next-Generation DNA and RNA Sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen from, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.

Purpose: SLFN11 was identified as a critical determinant of response to DNA-targeted therapies by analyzing gene expression and drug sensitivity of NCI-60 and CCLE datasets. However, how SLFN11 is regulated in cancer cells remained unknown. Ewing sarcoma, which is characterized by the chimeric transcription factor EWS-FLI1, has notably high SLFN11 expression, leading us to investigate whether EWS-FLI1 drives SLFN11 expression and the role of SLFN11 in the drug response of Ewing sarcoma cells. Experimental Design: Binding sites of EWS-FLI1 on the SLFN11 promoter were analyzed by chromatin immunoprecipitation sequencing and promoter-luciferase reporter analyses. The relationship between SLFN11 and EWS-FLI1 were further examined in EWS-FLI1-knockdown or -overexpressing cells and in clinical tumor samples. Results: EWS-FLI1 binds near the transcription start site of SLFN11 promoter and acts as a positive regulator of SLFN11 expression in Ewing sarcoma cells. EWS-FLI1–mediated SLFN11 expression is responsible for high sensitivity of Ewing sarcoma to camptothecin and combinations of PARP inhibitors with temozolomide. Importantly, Ewing sarcoma patients with higher SLFN11 expression showed better tumor-free survival rate. The correlated expression between SLFN11 and FLI1 extends to leukemia, pediatric, colon, breast, and prostate cancers. In addition, expression of other ETS members correlates with SLFN11 in NCI-60 and CCLE datasets, and molecular experiments demonstrate that ETS1 acts as a positive regulator for SLFN11 expression in breast cancer cells. Conclusions: Our results imply the emerging relevance of SLFN11 as an ETS transcription factor response gene and for therapeutic response to topoisomerase I inhibitors and temozolomide–PARP inhibitor combinations in ETS-activated cancers. Clin Cancer Res; 21(18); 4184–93. ©2015 AACR. See related commentary by Kovar, p. 4033

The limited capacity to predict the response of a given patient to cancer therapy is a major hurdle in cancer management. To date, many standard-of-care chemotherapy agents act by directly binding or modifying DNA or indirectly during processes that rely on chromatin architecture, as exemplified with docetaxel. Therefore, alterations in chromatin organization may impact resistance mechanisms for these drugs. Here, we explore the mis-regulation of chromatin regulators—factors involved in the establishment and maintenance of functional chromatin domains—as a tool to predict the response to docetaxel. For a series of chromatin regulators, we exploit Affymetrix microarray and NanoString gene expression data generated from breast cancer patient derived xenograft (PDX) models, colorectal cancer organoids, and breast cancer patient samples treated with docetaxel. Random Forest classification reveals a limited set of chromatin regulators that readily distinguish docetaxel responders from non-responders. Since the candidate predictive factors feature key subunits of the chromatin remodeler SWI/SNF complex, we then explore the behavior of SWI/SNF subunits in the response to docetaxel in the large NCI-60 dataset and in cultured cells. All together, our results showcase the predictive value of chromatin regulators and reveal that the SWI/SNF chromatin remodeler associates tightly with docetaxel response in several settings. Our findings open avenues for applications as biomarkers, but also as therapeutic targets to sensitize patients towards docetaxel. These approaches offer a means to potentially combat drug resistance. Citation Format: Zachary A. Gurard-Levin, Laurence OW Wilson, Vera Pancaldi, Sophie Postel-Vinay, Fabricio G. Sousa, Cecile Reyes, Elisabetta Marangoni, David Gentien, Alfonso Valencia, Yves Pommier, Paul Cottu, Genevieve Almouzni. Epigenetic regulators to predict docetaxel sensitivity; a guide for treatment choice. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-129.

Exome sequencing provides unprecedented insights into cancer biology and pharmacological response. Here we assess these two parameters for the NCI-60, which is among the richest genomic and pharmacological publicly available cancer cell line databases. Homozygous genetic variants that putatively affect protein function were identified in 1,199 genes (approximately 6% of all genes). Variants that are either enriched or depleted compared to non-cancerous genomes, and thus may be influential in cancer progression and differential drug response were identified for 2,546 genes. Potential gene knockouts are made available. Assessment of cell line response to 19,940 compounds, including 110 FDA-approved drugs, reveals ≈80-fold range in resistance versus sensitivity response across cell lines. 103,422 gene variants were significantly correlated with at least one compound (at p

The limited capacity to predict a patient's response to distinct chemotherapeutic agents is a major hurdle in cancer management. The efficiency of a large fraction of current cancer therapeutics (radio- and chemotherapies) is influenced by chromatin structure. Reciprocally, alterations in chromatin organization may affect resistance mechanisms. Here, we explore how the misexpression of chromatin regulators—factors involved in the establishment and maintenance of functional chromatin domains—can inform about the extent of docetaxel response. We exploit Affymetrix and NanoString gene expression data for a set of chromatin regulators generated from breast cancer patient-derived xenograft models and patient samples treated with docetaxel. Random Forest classification reveals specific panels of chromatin regulators, including key components of the SWI/SNF chromatin remodeler, which readily distinguish docetaxel high-responders and poor-responders. Further exploration of SWI/SNF components in the comprehensive NCI-60 dataset reveals that the expression inversely correlates with docetaxel sensitivity. Finally, we show that loss of the SWI/SNF subunit BRG1 (SMARCA4) in a model cell line leads to enhanced docetaxel sensitivity. Altogether, our findings point toward chromatin regulators as biomarkers for drug response as well as therapeutic targets to sensitize patients toward docetaxel and combat drug resistance. Mol Cancer Ther; 15(7); 1768–77. ©2016 AACR.