Abstract
BackgroundIdentification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp.ResultsApplying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens.ConclusionsThis work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2375-1) contains supplementary material, which is available to authorized users.
Highlights
Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets
To enrich for candidate synthetic lethal gene pairs, data-driven workflows were created to identify mutually exclusive loss-offunction defined as either deleterious mutation, copy number loss, and/or low mRNA expression: 1. Genetic-only workflow: searches for mutual exclusivity of genetic loss using a combination of Mutually Exclusive Mutations (MEMU) and Functional REdundancy between synthetic lethal genes (FuRE)
We applied an expression-only workflow across 811 cell lines to identify 98,261 gene pairs that are never expressed at low levels together, and demonstrating significant overlap with the Yeast SL set (167 gene pairs, p < 0.0001; Fig. 3; Additional file 3: Table S2). These results strongly suggest that the reduction in search space by ~99.9 % from ~380 million gene pairs to
Summary
Identification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp. Tumour suppressor gene defects drive progression of many cancer types [1, 2], but are poorly served by therapies typically targeting activated oncogenes. Approaches to uncover mutually exclusive loss-of-function by considering all levels at which loss can be inferred could compliment published approaches and increase the proportion of synthetic lethal pairings detectable in large multi-omic data sets. Bimodality and non-normality offer a route by which we may identify more subtle changes in gene expression state that remain prominent enough to clearly stratify and classify patient samples [10], and thereby a useful tool to infer loss-of-function through loss of expression
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