Systematic identification of genomic markers of drug sensitivity in cancer cells

By Mathew Garnett, E. Edelman, S. Heidorn, C. Greenman, A. Dastur, K. Lau, Patricia Greninger, I. Thompson, Xi Luo, Jorge Soares and 44 more. Nature, 2012

2,417 citations recorded by the source corpus.

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines—which represent much of the tissue-type and genetic diversity of human cancers—with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing’s sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/0950cf335baf14c08ae98140b8c2169347238bb8

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Citations in the corpus

Stereospecific PARP trapping by BMN 673 and comparison with olaparib and rucaparib (2013, background): “Ewing’s sarcoma cells have recently been identified as being selectively sensitive to olaparib (30), andEW8 is an Ewing’s sarcoma cell line that carries the EWS-FLI1 translocation (40).”
Targeting transcription regulation in cancer with a covalent CDK7 inhibitor (2014, methodology): “To better understand the breadth of antiproliferative activity of THZ1, we screened it against a diverse panel of over 1,000 cancer cell lines 16 .”
Pattern discovery and cancer gene identification in integrated cancer genomic data (2013, background): “Complementary to the tumor project, the Cancer Cell Line Encyclopedia (CCLE) (6) and the Sanger cell line project (7) has cataloged a compilation of ge-netic and molecular data in almost 1,000 human cancer cell lines, coupled with pharmacological pro ﬁ les for a large panel of anti-cancer drugs.”
Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction (2019, background): “The Genomics of Drug Sensitivity in Cancer (GDSC) database [21,22] contains the gene expression data of 789 cancer cell lines and provides sensitivity data of various cancer cell lines to”
Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties (2012, methodology): “We used the data from the Genomics of Drug Sensitivity in Cancer project [3], which contains 639 cancer cell lines, each of them characterised by a set of genomic features (details in the next section).”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, background): “Therefore, most of the learning studies to date have been done using large panels of cancer cell lines (Garnett et al. 2012; Barretina et al. 2012; Seashore-Ludlow et al. 2015; Iorio et al. 2016), although there are also a few recent examples aiming at clinical treatment predictions in patient…”
Co-occurring genomic alterations define major subsets of KRAS - mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities (2015, positive): “A, reanalysis of drug sensitivity data (34) for 19 KRAS-mutant NSCLC cell lines based on the presence of co-mutations in STK11/LKB1 and TP53.”