Identifying drug synergistic with cancer immunotherapy

确定药物与癌症免疫疗法的协同作用

• 批准号: 10828594
• 负责人: Avinash Das Sahu
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10828594
• 关键词: Acceleration; Advanced Malignant Neoplasm; Advisory Committees; Aftercare; Antibodies; Antigen Presentation; Antineoplastic Agents; Area; Artificial Intelligence; Award; BRAF gene; Biological; Biological Markers; Biology; CD8B1 gene; Cancer Patient; Cell Line; Clinical Data; Clinical Research; Clinical Trials; Clinical Trials Design; Cloud Computing; Collaborations; Combination immunotherapy; Combined Modality Therapy; Computational Biology; Computer software; Data; Data Commons; Development Plans; Doctor of Philosophy; Drug Combinations; Drug Synergism; Drug usage; Effectiveness; Environment; Foundations; Goals; Human; Immune; Immune system; Immunologic Factors; Immunological Models; Immunology; Immunology procedure; Immunooncology; Immunotherapy; In Vitro; Infiltration; Infrastructure; Investigational Drugs; K-Series Research Career Programs; Knowledge; Learning; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of urinary bladder; Mediating; Mentors; Mission; Outcome; Patients; Performance; Pharmaceutical Preparations; Phase; Pre-Clinical Model; Prize; Rationalization; Renal carcinoma; Research; Research Personnel; Target Populations; Techniques; Technology; Testing; Training; Training Activity; Translational Research; Translations; Vaccines; Work; cancer cell; cancer clinical trial; cancer immunotherapy; cancer therapy; cancer type; career; career development; cohort; combination cancer therapy; computing resources; data access; deep learning; design; immune checkpoint blockers; immune modulating agents; immunoregulation; improved; in silico; in vivo; industry partner; inhibitor; innovation; large datasets; melanoma; method development; mouse model; novel; novel therapeutic intervention; palliative chemotherapy; precision oncology; predicting response; prototype; response; response biomarker; skills; small molecule; software infrastructure; sound; statistics; synergism; transcriptome; transcriptome sequencing; transcriptomics; transfer learning; treatment optimization; tumor; tumor immunology; tumor-immune system interactions

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.

项目总结 Avinash D Sahu博士是一位计算生物学家，他的首要职业目标是解决长期存在的问题 使用人工智能(AI)的癌症免疫学和翻译精确肿瘤学，并设计出新的治疗方法 晚期癌症患者的策略。题为确定药物与癌症免疫疗法的协同作用，建议的 研究将尖端人工智能技术与免疫肿瘤学(IO)相结合，以产生一种系统的方法来 确定与免疫疗法协同的药物，并将其优先用于晚期黑色素瘤的临床试验， 膀胱癌、肾癌和肺癌。 职业发展规划：萨胡博士是迈克尔逊奖获得者，他的研究使命是启动精确度 通过将患者从姑息化疗转移到更个性化的IO治疗，实现免疫肿瘤学。他的 之前在人工智能、统计学、方法开发、癌症和翻译生物学方面的培训使他做好了进行 拟开展的研究。萨胡博士概述了具体的培训活动，以扩大他在四个领域的技能：1)癌症 免疫学，2)人工智能，3)翻译研究和4)新的免疫学检测。这套技能将是获得 研究独立性。导师/环境：萨胡博士导师和顾问团队汇集了世界领先的 计算生物学、翻译和临床研究、人工智能、统计学和免疫学方面的专家。此外，萨胡博士也有 发展了学术合作和行业合作伙伴，为他的提案提供实验支持。 利用癌症免疫数据共享中心(CIDC)提供的最先进的软件和谷歌云基础设施； 来自DFCI、哈佛和布罗德研究所的计算资源；以及获得最大免疫疗法的独特途径 根据来自合作者的患者数据，Sahu博士在识别最有希望的IO药物组合方面处于独特的地位。 研究：缺乏一种原则性的方法来确定有希望的IO药物组合，这经常导致 随意设计IO临床试验，没有良好的生物学基础。该方案制定了首个计算机预报器 评估药物的免疫调节作用及其与免疫治疗的协同作用。《目标1》构筑了一部深度的小说 学习预测因子-深度免疫-根据转录本预测免疫治疗反应。目标2估计 深度免疫对药物诱导转录改变的免疫调节作用。目标3 优先选择预测最好的免疫调节药物，并在临床前模型中验证它们的效果。 结果/影响：提案的成功完成将产生一个强有力的预测因素，以便合理组合 癌症治疗与免疫疗法相结合，并为测试最有前景的联合疗法的临床试验奠定了基础。 职业发展奖和指导性研究将使萨胡博士成为新研究领域的领导者 在精密免疫肿瘤学和人工智能的交叉点上。

项目成果

Cell-free DNA captures tumor heterogeneity and driver alterations in rapid autopsies with pre-treated metastatic cancer.

• DOI: 10.1038/s41467-021-23394-4
• 发表时间: 2021-05-27
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Pereira B;Chen CT;Goyal L;Walmsley C;Pinto CJ;Baiev I;Allen R;Henderson L;Saha S;Reyes S;Taylor MS;Fitzgerald DM;Broudo MW;Sahu A;Gao X;Winckler W;Brannon AR;Engelman JA;Leary R;Stone JR;Campbell CD;Juric D
• 通讯作者: Juric D