The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Jordi Barretina,Giordano Caponigro,Nicolas Stransky,Kavitha Venkatesan,Adam A. Margolin,Adam A. Margolin,Sungjoon Kim,Christine D. Wilson,Joseph Lehar,Gregory V. Kryukov,Dmitriy Sonkin,Anupama Reddy,Manway Liu,Lauren Murray,Michael F. Berger,Michael F. Berger,John Monahan,Paula Morais,Jodi Meltzer,Adam Korejwa,Judit Jané-Valbuena,Judit Jané-Valbuena,Felipa A. Mapa,Joseph Thibault,Eva Bric-Furlong,Pichai Raman,Aaron Shipway,Ingo H. Engels,Jill Cheng,Guoying K. Yu,Jianjun Yu,Peter Aspesi,Melanie de Silva,Kalpana Jagtap,Michael D. Jones,Li Wang,Charlie Hatton,Emanuele Palescandolo,Supriya Gupta,Scott Mahan,Carrie Sougnez,Robert C. Onofrio,Ted Liefeld,Laura E. MacConaill,Wendy Winckler,Michael R. Reich,Nanxin Li,Jill P. Mesirov,Stacey Gabriel,Gad Getz,Kristin G. Ardlie,Vivien W. Chan,Vic E. Myer,Barbara L. Weber,Jeffrey A. Porter,Markus Warmuth,Peter Finan,Jennifer L. Harris,Matthew Meyerson,Matthew Meyerson,Todd R. Golub,Michael Morrissey,William R. Sellers,Robert Schlegel,Levi A. Garraway,Levi A. Garraway +65 more
TLDR
The results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents and 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.Abstract:
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.read more
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Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
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