Abstract
The connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.
Highlights
Cancer, the second leading cause of death worldwide, is continuously affecting human health
We evaluated the performance of our inference method on capturing predictive genetic features for drug response by extending the application of such methodology from proteomic data to pharmacogenomic data, obtained from the Cancer Cell Line Encyclopedia (CCLE)[8], including gene mutation, drug response, as well as mRNA expression data
We find that TP53 mutation is associated with 6 anti-cancer drugs in our analysis, including 2 MEK inhibitors, 2 RTK inhibitors, Nutlin[3], and an RAF inhibitor, substantiating that TP53 plays a key role in the determinant of drug sensitivity
Summary
The second leading cause of death worldwide, is continuously affecting human health. We formulated a global model, which provides improved inference performance compared to MI based methods, aiming to reconstitute protein or drug-gene networks from noisy and large sets of genomic or proteomic data for the first time. We further propose that genetic signatures connected to patients’ response to drugs can be efficiently extracted from numerous biomarkers by employing a global method to infer drug-gene dependence in pharmacogenomic data, providing potential guidelines for personalized medicine. We evaluated the performance of our inference method on capturing predictive genetic features for drug response by extending the application of such methodology from proteomic data to pharmacogenomic data, obtained from the Cancer Cell Line Encyclopedia (CCLE)[8], including gene mutation, drug response, as well as mRNA expression data. Analyzing the direct couplings between the mutation statuses of cancer associated genes in cancer cell lines and corresponding drug response gives us more predictive gene candidates for drug response
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