TOPIC 438 - PREDICTION OF CANCER DRUG RESISTANCE TO AID IN CLINICAL DECISION MAKING

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Cancer is the second leading cause of death behind heart disease with ~600,000 deaths annually according to the CDC. Approximately 90% of cancer deaths are attributed to drug resistance making it a major health problem. Both intrinsic and acquired drug resistance in cancers have been attributed to the presence of genetic variant in the genes involved in growth or apoptosis. However, many of the variants found in patients’ tumor are of unknown significance. The proposed research develops a computational method that leverages machine learning applied to molecular dynamics simulations of wild-type and variant proteins that are drug targets to predict drug resistance and its severity. This quantitative information will be incorporated into protein network models describing cancer growth and apoptosis to predict how off-target variants can cause drug resistance through pathway interactions. This proposal brings together a collaborating team of experts in molecular simulation, machine learning, pathway modeling, software design and development and systems biology accomplishing this paradigm shifting work. At the conclusion of the proposed work, a prototype will be developed that can help oncologists and their team to understand and deliver information to patients about possible drug resistance in the patient’s tumor and to make clinical treatment decisions.