SUMMARY Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

Cancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency. and the function of these alleles remain undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic. One rare KRAS allele, D33E, displayed tumorigenicity and constitutive activation of known RAS effector pathways. By comparing gene expression changes induced upon expression of wild type and mutant alleles, we inferred the activity of specific alleles. Since alleles found to be mutated only once in 5,338 tumors rendered cells tumorigenic, these observations underscore the value of integrating genomic information with functional studies.

Summary Polycomb repressive complexes (PRCs) play key roles in developmental epigenetic regulation. Yet the mechanisms that target PRCs to specific loci in mammalian cells remain incompletely understood. In this study, we show that Bmi1, a core component of Polycomb Repressive Complex 1 (PRC1), binds directly to the Runx1/CBFβ transcription factor complex. Genome-wide studies in megakaryocytic cells demonstrate significant chromatin occupancy overlap between the PRC1 core component Ring1b and Runx1/CBFβ, and functional regulation of a considerable fraction of commonly bound genes. Bmi1/Ring1b and Runx1/CBFβ deficiency generate partial phenocopies of one another in vivo. We also show that Ring1b occupies key Runx1 binding sites in primary murine thymocytes and that this occurs via Polycomb Repressive Complex 2 (PRC2) independent mechanisms. Genetic depletion of Runx1 results in reduced Ring1b binding at these sites in vivo. These findings provide evidence for site-specific PRC1 chromatin recruitment by core binding transcription factors in mammalian cells.

The mitogen-activated protein kinase (MAPK) ERK2 is ubiquitously expressed in mammalian tissues and is involved in a wide range of biological processes. Although MAPKs have been intensely studied, identification of their substrates remains challenging. We have optimized a chemical genetic system using analog-sensitive ERK2,a form of ERK2 engineered to utilize an analog of ATP, to tag and isolate ERK2 substrates in vitro. This approach identified 80 proteins phosphorylated by ERK2, 13 of which are known ERK2 substrates. The 80 substrates are associated with diverse cellular processes, including regulation of transcription and translation, and mRNA processing, as well as regulation of the activity of the Rho-family guanosine triphosphatases. We found that one of the newly identified substrates, ETV3 (a member of the E-twenty six family of transcriptional regulators) was extensively phosphorylated on sites within canonical and non-canonical ERK motifs. Phosphorylation of ETV3 regulated transcription by preventing its binding to DNA at promoters for several thousand genes, including some involved in negative feedback regulation of itself and of upstream signals.

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.