Abstract
Drug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive analysis of the mutational landscape that can prospectively estimate drug-resistance mutations. Here we describe and computationally validate a framework that combines the cancer-specific likelihood with the resistance impact to enable the detection of single point mutations with the highest chance to be responsible of resistance to a particular targeted cancer therapy. Moreover, for these treatment-threatening mutations, the model proposes alternative therapies overcoming the resistance. We exemplified the applicability of the model using EGFR-gefitinib treatment for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC) and the ERK2-VTX11e treatment for melanoma and colorectal cancer. Our model correctly identified the phenotype known resistance mutations, including the classic EGFR-T790M and the ERK2-P58L/S/T mutations. Moreover, the model predicted new previously undescribed mutations as potentially responsible of drug resistance. Finally, we provided a map of the predicted sensitivity of alternative ERK2 and EGFR inhibitors, with a particular highlight of two molecules with a low predicted resistance impact.
Highlights
Cell population of a tumor are of the order of thousands or even millions of mutations depending of the tumor type and size[23]
Despite the fact that these two classes performed the lower recall of the IS class indicated that this class had a higher number of false negatives (FN; i.e., instances of the ISEN class misassigned to another class)
We have demonstrated the power of this framework to predict previously clinically described drug resistance mutants and identified novel potential mutants that can potentially infer drug resistance
Summary
Cell population of a tumor are of the order of thousands or even millions of mutations depending of the tumor type and size[23]. The advent of the massive cancer genomic data has prompted the development of several mathematical and computational models[27] Some of these models focus on characterizing tumor evolutionary processes[28,29,30] while others, study tumor response to single targeted treatment[31,32,33,34] or combinational therapy[35]. The structural nature of the predictions helped to elucidate the specific mechanism of resistance of each mutation For both EGFR and ERK2 treatment-threatening mutations, the model proposed alternative inhibitors that might overcome resistance
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