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Identification of plausible cancer gene-drug associations and development of the open-access tool GEDA
Date
April 12, 2021
Cancer is the second-largest cause of death worldwide, with 9.6 million fatalities in 2018. Despite significant progress over recent years in cancer treatment and new therapies, drug resistance remains one of the foremost biomedical challenges since many patients do not react well to the pharmacological treatments or develop tumor progression after some initial drug reactivity. Therefore, new strategies to find new drug candidates for therapy are crucial to providing a fighting chance for cancer patients. We used a pharmacogenomic approach to perform a large-screening focusing on known cancer genes in the Cancer Gene Census and drug activity-expression data derived from CellMiner. We analyzed 705 cancer genes and over 21,000 chemical compounds to identify new plausible interactions between drugs and cancer-associated genes. Bioinformatic analyses were performed to calculate the Pearson and Spearman correlations between the expression profiles of the genes in the NCI-60 cancer cell lines and the expression profiles when treated with different pharmacological compounds using R. In addition to the activity of FDA-approved cancer drugs; we explored other molecules that reported significant correlations. Based on the activity-expression patterns between FDA approved drugs and their known drug targets (i.e., their gene targets), we propose repurposing some of these drugs in the treatment of other types of cancers. We built a global bipartite network of 1007 significant gene-drug associations between 363 genes and 92 FDA-approved cancer drugs. We also identified new potential drug candidates for the treatment of cancer. Finally, we developed the open web-tool GEDA (Gene Expression and Drug Activity in Cancer Cells, http://cicblade.dep.usal.es/GEDA/) with all the data integrated and bipartite networks of interesting cancer-drug target modules. GEDA allows users to explore the correlations and networks and identify novel interactions. It may also aid researchers to identify new interactions and cancer gene-drug clusters. Our analysis presents several molecules as prospective drugs for expanding chemotherapeutic therapy.
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