Abstract 212: Sarcoma-CellMiner: An extensive resource for patient-derived sarcoma cell line epigenetics, genomics and pharmacology
Authors: Camille Tlemsani, Lorinc Pongor, Javed Khan, Fathi Elloumi, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, Christine Heske, Yves Pommier
Journal: Cancer Research (2021)

Abstract Sarcomas represent a heterogeneous group of cancers with many histological subtypes. Their prognosis remains poor and treatment is mainly based on palliative chemotherapy at metastatic stage. Tumor genome sequencing failed to identify recurrent somatic drivers while several oncogenic gene fusion-translocations have been identified in specific sarcoma subtypes. Because of the rarity and heterogeneity of sarcomas, only few sarcoma patients are enrolled in clinicals trials according their subtype. Increased translational research according subtypes of sarcoma patients is needed to improve patient management. To characterize the differences between subgroups and build translational hypotheses, we built a novel resource, Sarcoma-Cellminer, which integrates drug sensitivity and genomic data from 112 patient-derived sarcoma cell lines. These data will be available from a web-based tool (https://discover.nci.nih.gov/SclcCellMinerCDB/) derived from our CellMiner cross-database web application (https://discover.nci.nih.gov/cellminercdb). Among the 112 cell lines, 65 are bone sarcomas (including 38 Ewing sarcomas and 22 osteosarcomas), 45 are soft tissue sarcomas (including 21 rhabdomyosarcomas). Transcriptome (RNAseq and microarray), copy number, microRNA, genome-wide methylation, and drug sensitivity data are included and made publicly available. We also generated new genomic data including copy number and methylation (850 k) for 79 cell lines from the NCI in addition to the 42 cell lines from Broad Institute (CCLE) and the 40 cell lines from the MGH-Sanger (GDSC). We created the “SCLC-Global” expression set by regrouping all datasets by Z-score normalization, which enables cross-database analyses of gene expression and molecular pathways. Hierarchical clustering based on expression and methylation data identifies subtypes of sarcomas. Histone genes stand out suggesting that epigenetic regulation of canonical histones is a feature of sarcoma genesis. Sarcoma-CellMiner includes drug sensitivity data for over 500 different drugs tested in the NCI, CCLE and GDSC databases. They show two subgroups of Ewing sarcomas: one sensitive to PARP inhibitors and one resistant. Similarly, profile of response to dasatinib is different when comparing alveolar and embryonal rhabdomyosarcomas. Sarcoma-CellMiner is a powerful tool demonstrating the value of patient-derived cancer cell line databases. It provides hypothesis-driven rationale for using omics, especially transcriptome and epigenetic data to better understand sarcoma heterogeneity and select personalized treatments for clinical trials. Citation Format: Camille Tlemsani, Lorinc Pongor, Javed Khan, Fathi Elloumi, Sudhir Varma, Augustin Luna, Vinodh Rajapakse, Kurt Kohn, Julia Krushkal, Mirit Aladjem, Beverly Teicher, Paul Meltzer, William Reinhold, Christine Heske, Yves Pommier. Sarcoma-CellMiner: An extensive resource for patient-derived sarcoma cell line epigenetics, genomics and pharmacology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 212.