A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy

Abstract

Summary An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here, we use a multi-species approach to develop a resource of synthetic-lethal interactions among genes mutated in cancer, including tumor suppressor genes (TSG) and druggable genes. First, we screen in yeast ~169,000 potential interactions amongst TSG orthologs and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic-lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules we prioritize >105 human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

“…We systematically control for confounding factors including cancer type, sex, age, genomic instability, tumor purity (Aran et al , ), and ethnicity in the Cox model (Materials and Methods). Phylogenetic screening : Because functionally interacting genes are known to co‐evolve (Srivas et al , ) in a species, we select SR pairs composed of genes with high phylogenetic similarity. The top 5% of phylogenetically similar pairs among the ones passing the previous steps are chosen as the final set of putative SR pairs.…”

“…The estimated likelihoods are then combined to estimate the effects of gene interactions on survival. Phylogenetic profiling screening: We further filter and select SR pairs composed of genes having high phylogenetic similarity, motivated by the findings of Srivas et al (). This is done by comparing the phylogenetic profiles of the SR‐paired genes across a diverse set of 87 divergent eukaryotic species adopting the method of Tabach et al (,b).…”

Precision Oncology: The Road Ahead

“…These include applying various machine learning methodologies to predict genetic interactions in different species [128–131], and in cancer (employing yeast SLi) [119, 132], utilizing metabolic modeling [133, 134], evolutionary characteristics [119, 129], transcriptomic profiles [101, 135], and more recently, by mining cancer patient data [136–138] (Table S2D). One recent study evaluated the TCGA copy number and transcriptomics data to identify, as candidate SLis, gene pairs that are almost never found inactivated in the same tumors [136].…”

The BioGRID interaction database: 2017 update

“…Extensive network contexts now provide a basis for the rationalization of perturbations caused by disease-associated mutations (10–12) and have helped deconvolve complex mutational profiles generated by genome-wide association studies (GWAS) and next-generation sequencing-based approaches for analysis of the genome (13), transcriptome, and epigenome (14). The network paradigm thus holds the promise of predictive and precision medicine, as illustrated for example by the synthetic lethal interaction networks between cancer driver mutations and established drug targets (15). …”