Identifying drug synergistic with cancer immunotherapy

PROJECT SUMMARY Avinash D Sahu, Ph.D., is a computational biologist whose overarching career goal is to solve longstanding problems in cancer immunology and translational precision oncology using artificial intelligence (AI) and to devise new therapeutic strategies for late-stage cancer patients. Entitled Identifying drug synergistic with cancer immunotherapy, the proposed research combines cutting-edge AI technology with Immuno-oncology (IO) to produce a systematic approach to identifying drugs that synergize with immunotherapy, and prioritize them for clinical trials for advanced melanoma, bladder, kidney, and lung cancer. Career development plan: Dr. Sahu is a recipient of the Michelson Prize, and his research mission is to initiate precision immuno-oncology by moving patients away from palliative chemotherapy to more personalized IO treatments. His previous training in AI, statistics, method development, cancer, and translation biology have prepared him to conduct the proposed research. Dr. Sahu has outlined specific training activities to expand his skill set in four areas: 1) cancer immunology, 2) AI, 3) translation research and 4) new immunological assays. This skill set will be necessary to gain research independence. Mentors/Environment: Dr. Sahu mentoring and the advisory team assembles world-leading experts in computational biology, translation and clinical research, AI, statistics, and immunology. Also, Dr. Sahu has developed academic collaborations and industry partners to provide him experimental support for the proposal. Leveraging the state-of-art software and google-cloud infrastructure provided by Cancer Immune Data Commons (CIDC); computational resources from DFCI, Harvard, and Broad Institute; as well as unique access to largest immunotherapy patient data from collaborators, Dr. Sahu is uniquely placed to identify most promising IO drug combinations. Research: There is a lack of a principled approach to identify promising IO drug combinations that has often led to arbitrarily designed IO clinical trials without a sound biological basis. The proposal formulates the first in silico predictor to estimate drug’s immunomodulatory effect and potential to synergize with immunotherapies. Aim 1 builds a novel deep learning predictor —DeepImmune— to predict immunotherapy response from transcriptomes. Aim 2 estimates the immunomodulatory effects of drugs from for its drug-induced transcriptomic changes using DeepImmune. Aim 3 prioritize top predicted immunomodulatory drugs and validate their effect in pre-clinical models. Outcomes/Impact: The successful completion of the proposal will result in a robust predictor to rationally combine cancer therapies with immunotherapy and set the basis for a clinical trial to test the most promising combination therapy. The career development award and mentored research will enable Dr. Sahu to become a leader in the new field of research at the intersection of precision immuno-oncology and AI.