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Nature DOI:10.1038/nature11003

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

Publication TypeJournal Article
Year of Publication2012
AuthorsBarretina, J, Caponigro, G, Stransky, N, Venkatesan, K, Margolin, AA, Kim, S, Wilson, CJ, Lehar, J, Kryukov, GV, Sonkin, D, Reddy, A, Liu, M, Murray, L, Berger, MF, Monahan, JE, Morais, P, Meltzer, J, Korejwa, A, Jané-Valbuena, J, Mapa, FA, Thibault, J, Bric-Furlong, E, Raman, P, Shipway, A, Engels, IH, Cheng, J, Yu, GK, Yu, J, Aspesi, P, de Silva, M, Jagtap, K, Jones, MD, Wang, L, Hatton, C, Palescandolo, E, Gupta, S, Mahan, S, Sougnez, C, Onofrio, RC, Liefeld, T, MacConaill, L, Winckler, W, Reich, M, Li, N, Mesirov, JP, Gabriel, SB, Getz, G, Ardlie, K, Chan, V, Myer, VE, Weber, BL, Porter, J, Warmuth, M, Finan, P, Harris, JL, Meyerson, M, Golub, TR, Morrissey, MP, Sellers, WR, Schlegel, R, Garraway, LA
Date Published2012 Mar 28
KeywordsAntineoplastic Agents, Cell Line, Tumor, Cell Lineage, Chromosomes, Human, Clinical Trials as Topic, Databases, Factual, Drug Screening Assays, Antitumor, Encyclopedias as Topic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, ras, Genome, Human, Genomics, Humans, Mitogen-Activated Protein Kinase Kinases, Models, Biological, Neoplasms, Pharmacogenetics, Plasma Cells, Precision Medicine, Receptor, IGF Type 1, Receptors, Aryl Hydrocarbon, Sequence Analysis, DNA, Topoisomerase Inhibitors

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.


Alternate JournalNature
PubMed ID22460905
PubMed Central IDPMC3320027
Grant ListR33 CA155554-02 / CA / NCI NIH HHS / United States
R33 CA126674-04 / CA / NCI NIH HHS / United States
DP2 OD002750 / OD / NIH HHS / United States
R33 CA126674 / CA / NCI NIH HHS / United States
R33 CA155554 / CA / NCI NIH HHS / United States
DP2 OD002750-01 / OD / NIH HHS / United States