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.

项目概要 Avinash D Sahu 博士是一位计算生物学家，其首要职业目标是解决长期存在的问题 使用人工智能 (AI) 进行癌症免疫学和转化精准肿瘤学并设计新疗法 晚期癌症患者的策略，题为“识别药物与癌症免疫疗法的协同作用”，提出的建议。 研究将尖端人工智能技术与免疫肿瘤学 (IO) 相结合，产生一种系统方法 识别与免疫疗法具有协同作用的药物，并将其优先用于晚期黑色素瘤的临床试验， 膀胱癌、肾癌和肺癌。 职业发展规划：Sahu博士是迈克尔逊奖获得者，他的研究使命是开创精准 免疫肿瘤学，让患者从姑息化疗转向更个性化的 IO 治疗。 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为开展这项研究做好了准备 Sahu 博士概述了在四个领域扩展其技能的具体培训活动：1) 癌症 免疫学、2) 人工智能、3) 翻译研究和 4) 新的免疫学测定是获得这些技能所必需的。 研究独立性。 导师/环境：Sahu 博士的指导和咨询团队汇集了世界领先的人才。 Sahu 博士是计算生物学、翻译和临床研究、人工智能、统计学和免疫学领域的专家。 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 (CIDC) 提供的最先进的软件和谷歌云基础设施； 来自 DFCI、哈佛大学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； Sahu 博士通过合作者提供的患者数据，在识别最有希望的 IO 药物组合方面具有独特的优势。 研究：缺乏原则性方法来识别有前景的 IO 药物组合，这常常导致 任意设计的 IO 临床试验没有良好的生物学基础，该提案制定了第一个计算机预测因子。 目标 1 旨在评估药物的免疫调节作用以及与免疫疗法协同作用的潜力。 学习预测器——DeepImmune——通过转录组预测免疫治疗反应，目标 2 估计了 使用 DeepImmune Aim 3 分析药物引起的转录组变化的免疫调节作用。 优先考虑最受预测的免疫调节药物，并在临床前模型中验证其效果。 结果/影响：提案的成功完成将产生一个强大的预测器来合理地组合 癌症疗法与免疫疗法并为临床试验奠定了基础，以测试最有希望的联合疗法。 职业发展奖和指导研究将使 Sahu 博士成为新研究领域的领导者 精准免疫肿瘤学和人工智能的交叉点。

项目成果

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

游离 DNA 在快速尸检中捕获经过预处理的转移性癌症的肿瘤异质性和驱动因素变化。

• 发表时间: 2021-05-27
• 影响因子: 16.6
• 作者: Pereira, Bernard;Chen, Christopher T;Goyal, Lipika;Walmsley, Charlotte;Pinto, Christopher J;Baiev, Islam;Allen, Read;Henderson, Laura;Saha, Supriya;Reyes, Stephanie;Taylor, Martin S;Fitzgerald, Donna M;Broudo, Maida Williams;Sahu, Avinash;Ga
• 通讯作者: Ga

CRISPR Screens Identify Essential Cell Growth Mediators in BRAF Inhibitor-resistant Melanoma.

CRISPR 筛选鉴定出 BRAF 抑制剂耐药性黑色素瘤中的必需细胞生长介质。

• 发表时间: 2020
• 作者: Li, Ziyi;Wang, Binbin;Gu, Shengqing;Jiang, Peng;Sahu, Avinash;Chen, Chen;Han, Tong;Shi, Sailing;Wang, Xiaoqing;Traugh, Nicole;Liu, Hailing;Liu, Yin;Wu, Qiu;Brown, Myles;Xiao, Tengfei;Boland, Genevieve M;Shirley Liu, X
• 通讯作者: Shirley Liu, X

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.

教程：使用 RIMA 对大量 RNA 测序数据进行综合计算分析来表征肿瘤免疫。

• 发表时间: 2023-08
• 影响因子: 14.8
• 作者: Yang, Lin;Wang, Jin;Altreuter, Jennifer;Jhaveri, Aashna;Wong, Cheryl J;Song, Li;Fu, Jingxin;Taing, Len;Bodapati, Sudheshna;Sahu, Avinash;Tokheim, Collin;Zhang, Yi;Zeng, Zexian;Bai, Gali;Tang, Ming;Qiu, Xintao;Long, Henry W;Michor, Franzis
• 通讯作者: Michor, Franzis