The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues 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 pharmacologic annotation is available1. 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 pharmacologic profiles for 24 anticancer drugs across 479 of the 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. Altogether, our results suggest 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 regimens2.

Langerhans cell histiocytosis (LCH) has

The AACR Project GENIE is an international data-sharing consortium focused on generating an evidence base for precision cancer medicine by integrating clinical-grade cancer genomic data with clinical outcome data for tens of thousands of cancer patients treated at multiple institutions worldwide. In conjunction with the first public data release from approximately 19,000 samples, we describe the goals, structure, and data standards of the consortium and report conclusions from high-level analysis of the initial phase of genomic data. We also provide examples of the clinical utility of GENIE data, such as an estimate of clinical actionability across multiple cancer types (>30%) and prediction of accrual rates to the NCI-MATCH trial that accurately reflect recently reported actual match rates. The GENIE database is expected to grow to >100,000 samples within 5 years and should serve as a powerful tool for precision cancer medicine. Significance The AACR Project GENIE aims to catalyze sharing of integrated genomic and clinical datasets across multiple institutions worldwide, and thereby enable precision cancer medicine research, including the identification of novel therapeutic targets, design of biomarker-driven clinical trials, and identification of genomic determinants of response to therapy.

A detailed understanding of the mechanisms by which tumors acquire resistance to targeted anticancer agents should speed the development of treatment strategies with lasting clinical efficacy. RAF inhibition in BRAF-mutant melanoma exemplifies the promise and challenge of many targeted drugs; although response rates are high, resistance invariably develops. Here, we articulate overarching principles of resistance to kinase inhibitors, as well as a translational approach to characterize resistance in the clinical setting through tumor mutation profiling. As a proof of principle, we performed targeted, massively parallel sequencing of 138 cancer genes in a tumor obtained from a patient with melanoma who developed resistance to PLX4032 after an initial dramatic response. The resulting profile identified an activating mutation at codon 121 in the downstream kinase MEK1 that was absent in the corresponding pretreatment tumor. The MEK1 C121S mutation was shown to increase kinase activity and confer robust resistance to both RAF and MEK inhibition in vitro. Thus, MEK1 C121S or functionally similar mutations are predicted to confer resistance to combined MEK/RAF inhibition. These results provide an instructive framework for assessing mechanisms of acquired resistance to kinase inhibition and illustrate the use of emerging technologies in a manner that may accelerate personalized cancer medicine.