Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer
Sriganesh Srihari, Jitin Singla, Limsoon Wong, Mark A. Ragan

TL;DR
This study introduces a computational method to identify synthetic lethal gene pairs in human cancers by analyzing mutual exclusivity of genetic alterations, aiding targeted cancer therapy development.
Contribution
It presents a novel approach to infer synthetic lethal interactions directly from human cancer data, bypassing limitations of model organism extrapolation.
Findings
Identified 718 genes likely to be synthetic lethal with key DDR genes.
Enriched among top essential genes in DDR-deficient cancer cell lines.
Method validated by correlation with gene essentiality data.
Abstract
Background: Synthetic lethality (SL) refers to the genetic interaction between two or more genes where only their co-alteration (e.g. by mutations, amplifications or deletions) results in cell death. In recent years, SL has emerged as an attractive therapeutic strategy against cancer: by targeting the SL partners of altered genes in cancer cells, these cells can be selectively killed while sparing the normal cells. Consequently, a number of studies have attempted prediction of SL interactions in human, a majority by extrapolating SL interactions inferred through large-scale screens in model organisms. However, these predicted SL interactions either do not hold in human cells or do not include genes that are (frequently) altered in human cancers, and are therefore not attractive in the context of cancer therapy. Results: Here, we develop a computational approach to infer SL…
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