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: 10266758
• 负责人: Avinash Das Sahu
• 金额: $ 12万
• 依托单位: DANA-FARBER CANCER INST
• 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 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266758
• 关键词: 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; Combined Modality Therapy; Computational Biology; Computer software; Data; Data Commons; Development Plans; Doctor of Philosophy; Drug Combinations; Drug usage; Effectiveness; Environment; Foundations; Goals; Human; Immune; Immune system; Immunologic Factors; Immunological Models; Immunology; Immunology procedure; Immunomodulators; Immunooncology; Immunotherapy; In Vitro; Infiltration; Infrastructure; Institutes; Investigation; 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 I/II Clinical Trial; Pre-Clinical Model; Prize; Psychological Transfer; Renal carcinoma; Research; Research Personnel; Science; Target Populations; Techniques; Technology; Testing; Training; Training Activity; Translational Research; Translations; Vaccines; Work; base; cancer cell; cancer clinical trial; cancer immunotherapy; cancer therapy; cancer type; career; career development; cohort; computing resources; data access; deep learning; design; immune checkpoint blockers; immunoregulation; improved; in silico; in vivo; industry partner; inhibitor/antagonist; 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; transcriptome; transcriptome sequencing; transcriptomics; 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.

项目摘要 Avinash D Sahu博士是一名计算生物学家，其总体职业目标是解决长期存在的问题 癌症免疫学和使用人工智能（AI）翻译精度肿瘤学并设计新疗法 晚期癌症患者的策略。拟议的 研究将尖端AI技术与免疫肿瘤学（IO）结合在一起，以产生系统的方法 鉴定与免疫疗法协同作用的药物，并将其优先用于晚期黑色素瘤的临床试验， 膀胱，肾脏和肺癌。 职业发展计划：Sahu博士是米歇尔森奖的获得者，他的研究任务是启动精确度 免疫肿瘤学通过将患者从姑息化疗转移到更个性化的IO治疗方法中。他的 先前在AI，统计，方法开发，癌症和翻译生物学方面的培训已经准备好进行 拟议的研究。萨胡（Sahu）博士概述了特定的培训活动，以在四个领域扩大他的技能：1）癌症 免疫学，2）AI，3）翻译研究和4）新的免疫学测定。这项技能是必要的 研究独立性。导师/环境：Sahu Dr. Mentaling和咨询团队组装世界领先 计算生物学，翻译和临床研究，AI，统计和免疫学专家。另外，萨胡（Sahu） 开发了学术合作和行业合作伙伴，为他提供了该提案的实验支持。 利用癌症免疫数据共享（CIDC）提供的最先进的软件和Google-Cloud基础架构； 来自DFCI，哈佛大学和Broad Institute的计算资源；以及独特的获取最大免疫疗法 来自合作者的患者数据，Sahu博士是独特的，可以识别最有前途的IO药物组合。 研究：缺乏识别有希望的IO药物组合的主要方法，这些方法通常导致 任意设计的IO临床试验没有合理的生物学基础。该提案在计算机预测指标中提出了第一个 估计药物的免疫调节作用和与免疫疗法协同作用的潜力。 AIM 1建立小说深 学习预测因子 - 深度免疫 - 以预测转录组的免疫疗法反应。 AIM 2估计 药物对其药物诱导的转录组变化的免疫调节作用使用DeepMmune。目标3 优先考虑最高预测的免疫调节药物，并在临床前模型中验证其作用。 结果/影响：提案的成功完成将导致合理结合的强大预测指标 通过免疫疗法进行癌症疗法，为临床试验树立了基础，以测试最有希望的联合疗法。 职业发展奖和修改研究将使Sahu博士成为新研究领域的领导者 在精确免疫肿瘤和AI的交集中。