PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations

项目 1：基于时间的时空癌症免疫图预测免疫治疗靶向治疗顺序组合

• 批准号: 10708924
• 负责人: James R. Heath
• 金额: $ 75.24万
• 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/10708924
• 关键词: Adoptive Cell Transfers; Aftercare; Antigens; Area; Biopsy; Cancer Histology; Child; Clinical; Clinical Data; Clinical Trials; Clonal Expansion; Combination immunotherapy; Combined Modality Therapy; Cutaneous Melanoma; Data; Data Analyses; Data Set; Dose; Engineering; Environment; Formalin; Foundations; Freezing; Goals; Immune; Immune system; Immunocompetent; Immunologic Factors; Immunotherapy; Inflammatory; Lead; MAP Kinase Gene; MEKs; Malignant Neoplasms; Metastatic malignant neoplasm to brain; Mitogen-Activated Protein Kinase Inhibitor; Modeling; Molecular; Multiomic Data; Mus; Mutation; Oncogenes; Outcome; Paraffin Embedding; Pathway interactions; Patients; Physiological; Progression-Free Survivals; Regimen; Resistance; Resistance development; Resolution; Resources; Retrospective Studies; Role; Sampling; T cell therapy; T-Cell Receptor; T-Lymphocyte; Testing; Therapeutic; Therapy trial; Time; Tissues; Treatment Efficacy; Triplet Multiple Birth; Tumor Antigens; Tumor Immunity; Tumor-associated macrophages; Validation; Work; anti-CTLA4; anti-PD-1; anti-PD-L1; biobank; clinical development; clinically relevant; combinatorial; computational pipelines; data resource; design; driver mutation; engineered T cells; immune checkpoint blockade; immunogenic cell death; immunotherapy trials; improved; in vivo; inhibitor; inhibitor therapy; insight; melanoma; movie; multiple omics; mutant; neoantigens; neoplastic cell; overexpression; posters; prevent; rational design; resistance mechanism; response; spatiotemporal; subcutaneous; targeted treatment; therapy outcome; tool; treatment response; tumor; tumor-immune system interactions

Project 1 Summary/Abstract Combining immunotherapy with other therapy regimens, particularly targeted therapy, is a highly active area of exploration with the goal of improving anti-tumor efficacy and extending therapeutic benefits to more patients or tumor types. As the first mutation-immune co-targeted therapy, the simultaneous combination of anti-PD-L1 with BRAFV600MUT and MEK inhibitors (so-called “triplet” therapy) has been approved for patients with BRAFV600MUT melanoma. However, the data on this triplet appear mixed, with other trials not meeting key endpoints, suggesting that simultaneous combination is not optimal. Our recent work in syngeneic murine melanoma models showed uniformly, across tumor models of distinct driver mutations and cancer histologies, that a regimen of 1-week anti-PD-1/L1 (± anti-CTLA-4) pretreatment augments the efficacy of triplet therapy by enhancing MAPKi durability and dramatically suppressing melanoma brain metastasis. The improved therapy efficacy resulted from the promotion of pro-inflammatory polarization of tumor-associated macrophages and the elicitation of robust T cell clonal expansion and clonotypic convergence within the tumor-immune microenvironment (TIME) induced by the anti-PD-1/L1 lead-in. This is consistent with observations in the clinical trial data that prior immunotherapy before MAPKi is associated with improved progression-free survival. These results highlight the vital role of the sequence/timing of each therapy component in the rational design of combination therapies and also point to the need for a mechanistic understanding of the early-stage impact of each combinatorial therapy component on the TIME. However, the design of such sequential combination therapy trials is challenging because of the sheer number of variables (sequence order, dosing, and timing) to be tested. The level of complexity calls for a predictive framework to significantly reduce the parameter space and inform the identification of effective sequential immunotherapy-targeted inhibitor combinations. Herein, we hypothesize that a spatiotemporal, multi-omics analysis of early-stage (few days) monotherapy-induced changes in the TIME can provide deep insights for greatly simplifying the design of immunotherapy-targeted inhibitor sequential combination trials. The goal of Project 1 is to provide a data set that can be mined to inform the design of effective sequential combination regimens. We will leverage state-of-the-art, spatial multi-omics tissue profiling tools to build a spatiotemporal “movie” of the evolving TIME in established syngeneic melanoma tumor models, and their associated brain metastases, after treatment with each of the combinatorial therapy components. The resultant spatiotemporal multi-omic data will be analyzed to extract a number of highly informative TIME features from which agent-based models (Project 2) for predicting effective sequential combination regimens can be constructed. Retrospective studies of clinical tumor biopsies are proposed to validate the model findings.

项目1摘要/摘要 将免疫疗法与其他治疗方案（尤其是针对性疗法）相结合，是一个高度活跃的领域 探索目的是提高抗肿瘤效率并扩大对更多患者或 肿瘤类型。作为第一个突变 - 免疫靶向疗法，抗PD-L1的简单组合与 BRAFV600MUT和MEK抑制剂（所谓的“三重疗法”疗法）已批准BRAFV600MUT患者 黑色素瘤。但是，此三联集的数据似乎混合在一起，其他试验不符合关键端点， 表明同时组合不是最佳的。我们最近在Syngeneic Murine黑色素瘤方面的工作 在不同的驱动突变和癌症组织学的肿瘤模型中均匀显示的模型， 1周抗PD-1/L1的方案（±抗CTLA-4）预处理提高了三胞胎治疗的效率 增强了MAPKI耐用性并大大抑制黑色素瘤脑转移。改进的治疗 功效是由促进肿瘤相关巨噬细胞的促炎性极化和 在肿瘤免疫中启发强大的T细胞克隆膨胀和克隆型收敛 由抗PD-1/L1引导引起的微环境（时间）。这与临床观察结果一致 MAPKI之前先前免疫疗法的试验数据与改善无进展的生存有关。这些 结果突出了每个治疗成分在合理设计中的序列/计时的至关重要作用 结合疗法，还指出需要对早期影响的机械理解 每个组合治疗成分在时间。 但是，这种连续的组合疗法试验的设计是具有挑战性的 要测试的变量（序列顺序，剂量和定时）的。复杂程度需要预测 框架可显着减少参数空间并告知有效顺序的识别 免疫疗法抑制剂组合。本文中，我们假设是时空的多词 分析早期（几天）单一疗法引起的时间变化可以提供深刻的见解 为了大大简化靶向免疫疗法的抑制剂顺序组合试验的设计。这 项目1的目标是提供一个可以开采的数据集，以告知有效顺序组合的设计 方案。我们将利用最先进的空间多摩学组织分析工具来构建时空 既定的合成性黑色素瘤肿瘤模型及其相关的大脑中不断发展的时间的“电影” 转移，用每种组合治疗成分治疗后。由此产生的时空 将分析多摩变数据，以提取许多基于代理的信息丰富的时间功能 可以构建用于预测有效顺序组合方案的模型（项目2）。回顾 提出了临床肿瘤活检的研究来验证模型发现。