Document text
Principal Investigator: Avinash Das Sahu
Organization: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
Fiscal Year: 2024
Award: $217,810
Funding agency: National Cancer Institute
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.
Terms: <AI system><Acceleration><Advanced Cancer><Advanced Malignant Neoplasm><Advisory Committees><After Care><After-Treatment><Aftercare><Anti-Cancer Agents><Antibodies><Antigen Presentation><Antineoplastic Agents><Antineoplastic Drugs><Antineoplastics><Area><Artificial Intelligence><Award><B-raf-1><BRAF><BRAF gene><Biological><Biological Markers><Biology><Bladder Cancer><CD8><CD8B><CD8B1><CD8B1 gene><Cancer Drug><Cancer Patient><Cancer Treatment><Cancers><Career Development Awards><Career Development Awards and Programs><Career Development Programs K-Series><Cell Line><CellLine><Clinical Data><Clinical Research><Clinical Study><Clinical Trials><Clinical Trials Design><Cloud Computing><Cloud Infrastructure><Collaborations><Combination immunotherapy><Combined Modality Therapy><Computational Biology><Computer Reasoning><Computer software><Data><Data Commons><Development Plans><Doctor of Philosophy><Drug Combinations><Drug Synergism><Drug usage><Drugs><Effectiveness><Environment><Foundations><Goals><Human><IMiD><Immune><Immune mediated therapy><Immune modulatory therapeutic><Immune system><Immunes><Immunologic Factors><Immunologic Model><Immunological Factors><Immunological Models><Immunologically Directed Therapy><Immunology><Immunology procedure><Immunomodulation><Immunooncology><Immunotherapy><In Vitro><Infiltration><Infrastructure><Investigational Drugs><Investigational New Drugs><Investigators><K-Awards><K-Series Research Career Programs><Kidney Cancer><Kidney Carcinoma><Knowledge><LYT3><Learning><Machine Intelligence><Malignant Bladder Neoplasm><Malignant Cell><Malignant Melanoma><Malignant Neoplasm Therapy><Malignant Neoplasm Treatment><Malignant Neoplasms><Malignant Tumor><Malignant Tumor of the Bladder><Malignant Tumor of the Lung><Malignant neoplasm of lung><Malignant neoplasm of urinary bladder><Mediating><Medication><Melanoma><Mentors><Mission><Modern Man><Multimodal Therapy><Multimodal Treatment><Neoplastic Disease Chemotherapeutic Agents><Outcome><Patients><Performance><Ph.D.><PhD><Pharmaceutical Preparations><Phase><Pre-Clinical Model><Preclinical Models><Prize><Pulmonary Cancer><Pulmonary malignant Neoplasm><RAFB1><RNA Seq><RNA sequencing><RNAseq><Rationalization><Renal Cancer><Renal Carcinoma><Research><Research Career Program><Research Personnel><Researchers><Software><Strains Cell Lines><Target Populations><Task Forces><Techniques><Technology><Testing><Training><Training Activity><Translational Research><Translational Science><Translations><Tumor-Specific Treatment Agents><Urinary Bladder Cancer><Urinary Bladder Malignant Tumor><Vaccines><Work><advisory team><anti-cancer drug><anti-cancer immunotherapy><anti-cancer therapy><anticancer immunotherapy><bio-markers><biologic><biologic marker><biomarker><cancer cell><cancer clinical trial><cancer immunology><cancer immunotherapy><cancer therapy><cancer type><cancer-directed therapy><career><career development><check point blocker><checkpoint blockers><cloud based computing><cloud computer><cohort><combination cancer therapy><combination therapy><combinatorial immunotherapy><combined modality treatment><combined treatment><computational resources><computer biology><computing resources><cultured cell line><data access><data to train><dataset to train><deep learning><deep learning method><deep learning strategy><design><designing><drug use><drug/agent><dual immunotherapy><global gene expression><global transcription profile><immune check point blocker><immune checkpoint blockers><immune microenvironment><immune modulating agents><immune modulating drug><immune modulating therapeutics><immune modulation><immune modulatory agents><immune modulatory drugs><immune regulation><immune therapeutic approach><immune therapeutic interventions><immune therapeutic regimens><immune therapeutic strategy><immune therapy><immune-based cancer therapies><immune-based therapies><immune-based treatments><immune-oncology><immuno oncology><immuno therapy><immunologic assay><immunologic assay/test><immunologic reactivity control><immunologic substance><immunological substance><immunology oncology><immunomodulating agents><immunomodulating drugs><immunomodulator agent><immunomodulator drug><immunomodulator medication><immunomodulator prodrug><immunomodulator therapeutic><immunomodulatory><immunomodulatory agents><immunomodulatory drugs><immunomodulatory therapeutics><immunoregulation><immunoregulatory><immunosuppressive microenvironment><immunosuppressive tumor microenvironment><immunotherapy for cancer><immunotherapy of cancer><improved><in silico><in vivo><individualized cancer care><individualized oncology><industrial partnership><industry partner><industry partnership><inhibitor><innovate><innovation><innovative><large data sets><large datasets><lung cancer><malignancy><method development><mouse model><multi-modal cancer therapy><multi-modal neoplasm therapy><multi-modal therapy><multi-modal treatment><multimodality cancer therapy><multimodality neoplasm therapy><murine model><neoplasm immunology><neoplasm/cancer><new therapeutic approach><new therapeutic intervention><new therapeutic strategies><new therapy approaches><new treatment approach><new treatment strategy><novel><novel therapeutic approach><novel therapeutic intervention><novel therapeutic strategies><novel therapy approach><oncoimmunology><oncology clinical trial><palliative chemotherapy><personalized oncology><post treatment><precision cancer care><precision cancer medicine><precision oncology><predict responsiveness><predicting response><prototype><response><response biomarker><response markers><skills><small molecule><software infrastructure><sound><statistics><synergism><therapy optimization><training data><training module><transcriptome><transcriptome sequencing><transcriptomic sequencing><transcriptomics><transfer learning><translation><translation research><translational investigation><treatment optimization><tumor><tumor immune microenvironment><tumor immunology><tumor-immune system interactions><v-raf Murine Sarcoma Viral Oncogene Homolog B1>