Abstract
An individual tumor harbors multiple molecular alterations that promote cell proliferation and prevent apoptosis and differentiation. Drugs that target specific molecular alterations have been introduced into personalized cancer medicine, but their effects can be modulated by the activities of other genes or molecules. Previous studies aiming to identify multiple molecular alterations for combination therapies are limited by available data. Given the recent large scale of available pharmacogenomic data, it is possible to systematically identify multiple biomarkers that contribute jointly to drug sensitivity, and to identify combination therapies for personalized cancer medicine. In this study, we used pharmacogenomic profiling data provided from two independent cohorts in a systematic in silico investigation of perturbed genes cooperatively associated with drug sensitivity. Our study predicted many pairs of molecular biomarkers that may benefit from the use of combination therapies. One of our predicted biomarker pairs, a mutation in the BRAF gene and upregulated expression of the PIM1 gene, was experimentally validated to benefit from a therapy combining BRAF inhibitor and PIM1 inhibitor in lung cancer. This study demonstrates how pharmacogenomic data can be used to systematically identify potentially cooperative genes and provide novel insights to combination therapies in personalized cancer medicine.
Highlights
Many studies have identified second biomarkers that determine tumor sensitivity to anti-cancer therapies[14,17,18,19,20,21,22,23]
We started the analysis with the Cell Line Encyclopedia (CCLE) dataset[32], which includes the mutation and copy number variation (CNV) status and transcriptome profiles of around 500 human cancer cell lines, and pharmacological profiles of these cell lines treated with 24 anti-cancer drugs
These results suggest that drug sensitivity of a given cell line is the result of the combinatorial effects of multiple genomic alterations, involving genes from the root node to the leaf node in the decision tree
Summary
Many studies have identified second biomarkers that determine tumor sensitivity to anti-cancer therapies[14,17,18,19,20,21,22,23]. EGFR T790 M secondary mutation[14,15], MET amplification[17], or expression of the MET receptor ligand HGF23 are known to be involved in resistance to EGFR inhibitors in lung cancer These studies were addressing individual hypotheses based on feedback activation associated with clinical therapies. Two large-scale pharmacogenomic profiles, the Cancer Cell Line Encyclopedia (CCLE)[32] and Cancer Genome Project (CGP)[33], were reported Both studies provided high-throughput genomic information and pharmacological profiling of anti-cancer drugs across many cancer cell lines. By independently integrating the results of our initial CCLE analysis with the CGP dataset, we identified a set of candidate biomarker pairs that could potentially be targeted by two drugs to improve cell sensitivity. The list of predicted candidate pairs is a potentially useful resource for future validation by others
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