Scientific Publications

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

Publication TypeJournal Article
AuthorsBarretina, J., Caponigro G., Stransky N., Venkatesan K., Margolin AA, Kim S., Wilson CJ, Lehár 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 Jr, 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 J. P., Gabriel SB, Getz G., Ardlie K., Chan V., Myer VE, Weber BL, Porter J., Warmuth M., Finan P., Harris JL, Meyerson M., Golub T. R., Morrissey MP, Sellers WR, Schlegel R., and Garraway LA
AbstractThe 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.
Year of Publication2012
JournalNature
Volume483
Issue7391
Pages603-7
Date Published (YYYY/MM/DD)2012/03/28
ISSN Number0028-0836
DOI10.1038/nature11003
PubMedhttp://www.ncbi.nlm.nih.gov/pubmed/22460905?dopt=Abstract