SummarySystematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Author contributions M.G., K.Y., and C.B-D. conceived the project. F.B. led CRISPR-Cas9 screening, codeveloped Project Score webportal, performed analyses, verified WRN dependency. F.I. led computational analyses and figure preparation, contributed to the Project Score webportal. G.P. performed experiments to verify WRN dependency, carried out analyses, contributed to in vivo studies. E.G. contributed to computational analysis and figures. D.vdM. contributed to developing the Project Score webportal. G.

Mutations of the tricarboxylic acid cycle (TCA cycle) enzyme fumarate hydratase (FH) cause Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC)1. FH-deficient renal cancers are highly aggressive and metastasise even when small, leading to an abysmal clinical outcome2. Fumarate, a small molecule metabolite that accumulates in FH-deficient cells, plays a key role in cell transformation, making it a bona fide oncometabolite3. Fumarate was shown to inhibit α-ketoglutarate (aKG)-dependent dioxygenases involved in DNA and histone demethylation4,5. However, the link between fumarate accumulation, epigenetic changes, and tumorigenesis is unclear. Here we show that loss of FH and the subsequent accumulation of fumarate elicits an epithelial-to-mesenchymal-transition (EMT), a phenotypic switch associated with cancer initiation, invasion, and metastasis6. We demonstrate that fumarate inhibits Tet-mediated demethylation of a regulatory region of the antimetastatic miRNA cluster6 miR-200ba429, leading to the expression of EMT-related transcription factors and enhanced migratory properties. These epigenetic and phenotypic changes are recapitulated by the incubation of FH-proficient cells with cell-permeable fumarate. Loss of FH is associated with suppression of miR-200 and EMT signature in renal cancer patients, and is associated with poor clinical outcome. These results imply that loss of FH and fumarate accumulation contribute to the aggressive features of FH-deficient tumours.

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.

BackgroundCells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce.ResultsHere we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.ConclusionsModels generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.