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Paper
In-Silico Predictions of Drug Resistance in Lung Cancers with Egfr Mutation
Presenter
Dr. Sally Ellingson, an Assistant Professor in Biomedical Informatics, works with the Cancer Research Informatics Shared Resource Facility of the University of Kentucky’s Markey Cancer Center. She is a computational scientist working at the intersection of computational biology, informatics, and high-performance computing. She has undergraduate degrees in computer science and mathematics from Florida Institute of Technology. She obtained her doctoral degree at the University of Tennessee and Oak Ridge National Laboratory under a fellowship funded by the National Science Foundation in computational biology. Dr. Ellingson engages in mentoring and outreach, especially for underrepresented groups in computational sciences.
Description
Cancer treatment is often hindered by the emergence of drug resistance, frequently driven by novel mutations in oncogenes or drug-targeted pathways. Predicting resistance mechanisms is critical for informing therapeutic strategies and improving patient outcomes. Here, we present a computational workflow that leverages high-performance computing (HPC) resources to systematically evaluate the impact of emerging mutations on drug efficacy. Our workflow integrates deep learning structure prediction, molecular dynamics simulations, molecular docking, and binding predictions of known compounds to predict resistance mechanisms and propose alternative therapeutic options. We also explore quantum chemical calculations as a tool to complement experimental validations to better understand the binding preferences between different protein forms.