University of California San Francisco (UCSF-1): Chemical-Genetic Interaction Mapping Strategy

The CTD2 Center at University of California San Francisco (UCSF-1) developed a chemical-genetic interaction mapping strategy to uncover the impact of cancer gene expression on responses to a panel of emerging therapeutics. To study the impact of aberrant gene activity in isolation, they developed an isogenic model of triple-negative breast cancer (TNBC) using the hormone receptor negative MCF10A non-tumorigenic cell line derived from healthy breast tissue which is diploid and largely devoid of somatic alterations. They created 51 stable cell lines by ectopic expression of wild-type and mutant genes to model the impact of recurrent gene mutation, amplification, and overexpression common in breast and other cancers. These cell lines were used in a drug screen to identify genes which could alter responses to a panel of 90 FDA-approved and emerging clinical compounds. The data represent a quantitative measure of the degree to which aberrant gene expression drives the sensitivity or resistance to compounds.

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Experimental Approaches

To measure the impact of gene activation on cellular responses systematically, they screened their isogenic panel against a library of 90 anti-cancer therapeutics spanning multiple stages of clinical development, with 79% used in at least one clinical trial and targeting a broad variety of canonical cancer pathways and targets. They developed a robust screening method to quantitatively assess the impact of gene expression on drug responses. In this screen, isogenic cells expressing control vector or a gene of interest are plated separately and their relative proliferation after 72 hours of drug treatment is compared by high-content microscopy. Cell numbers from each line and treatment are compared to determine the effect size measured by the fold-change in cell number at the IC50 compared to control, and the significance of the effect over replicates. The p-value of significance was converted to a signed chemical-genetic interaction score (S) with positive values indicating that the expression of the gene drove drug resistance and negative values indicating that the gene caused drug sensitivity as compared to vector controls. The screen had a high correlation of scores across replicates (r=0.618) and an empirical false-discovery rate (FDR) of 1% and 10% at score cutoffs of approximately S=±4 and S=±2.5, respectively. The quantitative scores for 4,541 gene-drug interactions were determined, and 174 resistance interactions and 97 sensitivity interactions at S=±2 were identified, corresponding to a 10% FDR.

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If you cannot access the manuscript, or if you have additional questions, please email Sourav Bandyopadhyay.


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Last updated: July 01, 2017