Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)

用于指导序贯靶向抑制剂的时空肿瘤分析：免疫治疗组合（ST-Analytics）

• 批准号: 10526101
• 负责人: James R. Heath
• 金额: $ 270.26万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10526101
• 关键词: Address; Adoptive Cell Transfers; Algorithms; Automobile Driving; Biological; Biopsy; Cell physiology; Cells; Clinical; Clinical Data; Clinical Trials; Clinical Trials Design; Combination immunotherapy; Combined Modality Therapy; Communities; Coupled; Data; Databases; Development; Disadvantaged; Disease model; Distal; Dose; Education and Outreach; Educational Models; Environment; Environment Design; Epigenetic Process; Evaluation; Genetic Transcription; Immune; Immune system; Immunologic Factors; Immunooncology; Immunotherapy; Individual; Internships; K-12 Education; Kinetics; Knowledge; Lead; Leadership; Malignant Neoplasms; Measurement; Metastatic malignant neoplasm to brain; Mitogen-Activated Protein Kinase Inhibitor; Modeling; Nature; Neoplasm Metastasis; Outcome; Patients; Pharmaceutical Preparations; Pilot Projects; Play; Primary Neoplasm; Proteomics; Research; Research Personnel; Research Project Grants; Resistance; Resources; Role; Science; Science, Technology, Engineering and Mathematics Education; Scientist; Self-Examination; Series; Structure; System; Systems Biology; Therapeutic; Time; Tissues; Visual; Work; base; biobank; cancer immunotherapy; combinatorial; computing resources; demographics; design; immune checkpoint blockade; in silico; in vivo; inhibitor; inhibitor therapy; medical schools; melanoma; model design; mouse model; multiple omics; outreach; outreach program; prevent; programs; recruit; spatiotemporal; tool; transcriptomics; tumor; tumor-immune system interactions

Overall Project Summary The proposed U54 program Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor -- Immunotherapy Combinations (ST-Analytics) is designed to develop the recent conceptual advance that targeted inhibitor + cancer immunotherapy (IT) combination treatments may yield significantly greater patient benefit if those treatments are administered in sequence rather than simultaneously. Analysis of retrospective clinical data coupled with in vivo therapeutic modeling using syngeneic models of murine melanoma strongly support this concept. In fact, the picture that has emerged in melanoma is that immune factors can play a strong role in driving resistance to MAPK inhibitor (MAPKi) therapy, and that lead-in immune checkpoint blockade (ICB) can ‘prime’ both the primary tumor and distal metastases (including brain metastases) for eradication when the IT is subsequently combined with MAPKi. This observation opens the doors for immune based strategies, such as ICB or adoptive cell therapy (ACT), as sequential combinatorial agents to prevent MAPKi resistance. However, this concept introduces a number of new variables, including dosing, sequence, and timing. This can make the design and execution of clinical trials that can yield statistically significant outcomes impractical. This is the scientific and translational problem we address in the proposed ST-Analytics U54. The ST-Analytics U54 center is populated by leading scientists at the ISB, the UCLA Geffen School of Medicine, and Yale, and is comprised of two research projects and two research cores, with each project integrating both state-of-the-art experimentation and computational work. This structure is further designed to bring together the scientific, experimental, and computational and administrative resources to develop a data base that captures the kinetics of lead-in monotherapy tumor priming, and apply that data base to the development of predictive in silico models that can inform the design of such targeted inhibitor – immunotherapy sequence combinations for clinical trials. This requires close integration and cycles of iteration between of state-of-the-art experimentation, leading edge computation, and realistic disease models, continuously calibrated through the analysis of highly relevant, biopsied patient tumors. The resulting science also provides exciting opportunities for high impact STEM outreach. We propose to act on those opportunities by leveraging a long-standing systems education outreach program at ISB that already has impacted K-12 STEM education in all 50 states, and places an emphasis on those communities that have been historically under-represented in STEM.

项目总体概要 拟议的 U54 计划用于指导序贯靶向抑制剂的时空肿瘤分析—— 免疫疗法组合（ST-Analytics）旨在开发最新的概念进展， 靶向抑制剂+癌症免疫疗法（IT）联合治疗可能会显着提高患者的治疗效果 如果这些治疗按顺序而不是同时进行，就会受益。回顾性分析 临床数据与使用小鼠黑色素瘤同基因模型的体内治疗模型相结合，强烈 支持这个概念。事实上，在黑色素瘤中出现的情况是，免疫因素可以发挥强大的作用 在驱动 MAPK 抑制剂 (MAPKi) 治疗耐药性中的作用，并导致免疫检查点阻断 (ICB) 当根除原发肿瘤和远端转移瘤（包括脑转移瘤）时，可以“启动”根除 IT 随后与 MAPKi 相结合。这一观察结果为基于免疫的策略打开了大门，例如 如 ICB 或过继细胞疗法 (ACT)，作为序贯组合药物以预防 MAPKi 耐药。 然而，这个概念引入了许多新的变量，包括剂量、顺序和时间。这个可以 使得能够产生具有统计学意义的结果的临床试验的设计和执行变得不切实际。这 是我们在提议的 ST-Analytics U54 中​​解决的科学和转化问题。 ST-Analytics U54 中​​心由 ISB、加州大学洛杉矶分校格芬医学院、 和耶鲁大学，由两个研究项目和两个研究核心组成，每个项目都整合了 最先进的实验和计算工作。该结构进一步设计为将 科学、实验、计算和管理资源，以开发一个捕获数据的数据库 引入单一疗法肿瘤启动的动力学，并将该数据库应用于预测性肿瘤的开发 计算机模型可以为此类靶向抑制剂的设计提供信息 - 免疫治疗序列组合 临床试验。这需要最先进的实验之间的紧密集成和迭代周期， 领先的计算和现实的疾病模型，通过高度的分析不断校准 相关的、活检的患者肿瘤。由此产生的科学也为高影响力提供了令人兴奋的机会 STEM 外展。我们建议通过利用长期的系统教育来抓住这些机会 ISB 的外展计划已经影响了所有 50 个州的 K-12 STEM 教育，并在 重点关注那些历来在 STEM 领域代表性不足的社区。