CCSG Pilot: Integrating AI-based Protein 3D Structure Prediction and In-silico Ligand Screening to Discover FDA approved Drugs Overcoming Therapy Resistance of Uncommon EGFR-mutant Lung Cancers

Grants and Contracts Details


Abstract: A major reason for lung cancer related death is protein mutation-induced drug resistance. Many drugs perform their functions by regulating the function of a cancer related protein via binding on the specific domain of the protein. A mutation of the target protein could cause drug resistance if this mutation changes the protein conformation and alters the binding affinity of the protein to the drug molecule. Single-cell analysis and genetic technology can identify mutations in cancer proteins quickly, but it remains time-consuming and tedious to determine whether mutations may cause drug resistance and if so, which therapeutics will overcome the resistance. We need a quick and reliable approach to evaluate the deleterious level of a mutation and discover new effective therapeutics for the mutation if it is identified as dangerous. In this project, we propose a pipeline that utilizes deep learning and molecular dynamics to discover mutant protein conformations, then determine if they are clinically actionable and suggest personalized treatment plans using computational drug binding calculations. The development of this pipeline could discover new treatments for mutation-induced drug resistance for lung cancer and dramatically decrease the death rate. This initial study focuses on epidermal growth factor receptor (EGFR), a protein commonly mutated in lung cancers seen at our cancer center. For this study, we have assembled a complimentary and diverse team of researchers across the Translational Oncology (TO) and Molecular and Cellular Oncology (MCO) research programs. We have expertise in the computational tools needed for the pipeline and in translational and clinical research to ensure proper validation of the pipeline. This proposal will also utilize the Cancer Research Informatics (CRISRF), Biostatistics and Bioinformatics (BBSRF), and Oncogenomics (OGSRF) Shared Resource Facilities. Several services will be utilized at the facilities, but most importantly the project will leverage the ability of all three facilities to work together in discovering and studying protein mutations relevant to our catchment area.
Effective start/end date7/1/226/30/23


  • National Cancer Institute


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