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: 10526103
• 负责人: James R. Heath
• 金额: $ 82.5万
• 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/10526103
• 关键词: Adoptive Cell Transfers; Aftercare; Antigens; Area; Biopsy; Cancer Histology; Cells; 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; Patients; Physiological; Progression-Free Survivals; Regimen; Resistance; 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-associated macrophages; Validation; Work; anti-CTLA4; anti-PD-1; anti-PD-L1; base; biobank; clinical development; clinically relevant; combinatorial; computational pipelines; data resource; design; driver mutation; engineered T cells; immune checkpoint blockade; immunogenic; immunotherapy trials; improved; in vivo; inhibitor; inhibitor therapy; insight; melanoma; movie; multiple omics; mutant; 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概要/摘要 将免疫治疗与其他治疗方案，特别是靶向治疗相结合，是免疫治疗的一个高度活跃的领域。 旨在提高抗肿瘤疗效并将治疗获益扩展至更多患者的探索，或 肿瘤类型作为第一种突变-免疫共靶向治疗，抗PD-L1与 BRAFV 600 MUT和MEK抑制剂（所谓的“三联”疗法）已被批准用于BRAFV 600 MUT患者 黑素瘤然而，这三个方面的数据似乎是混合的，其他试验没有达到关键终点， 这表明同时组合不是最佳的。我们最近在同基因小鼠黑色素瘤方面的工作 模型一致显示，在不同驱动突变和癌症组织学的肿瘤模型中， 1周抗PD-1/L1（±抗CTLA-4）预处理方案通过以下方式增强三联疗法的疗效： 增强MAPKi的持久性并显著抑制黑色素瘤脑转移。改良疗法 疗效来自于促进肿瘤相关巨噬细胞的促炎性极化， 在肿瘤免疫系统中引发稳健的T细胞克隆扩增和克隆型收敛 抗PD-1/L1导入诱导的微环境（TIME）。这与临床观察结果一致。 试验数据表明，MAPKi之前的免疫治疗与改善的无进展生存期相关。这些 结果强调了每个治疗组分的顺序/定时在合理设计中的重要作用， 联合治疗，也指出需要一个机制的理解的早期阶段的影响， 每一个组合治疗成分的时间。 然而，这种序贯联合治疗试验的设计是具有挑战性的，因为数量庞大， 待测变量（序列顺序、给药和时间）。复杂程度要求预测 框架，以显着减少参数空间，并告知有效的顺序识别 免疫治疗靶向抑制剂组合。在此，我们假设时空，多组学 对早期（几天）单药治疗诱导的TIME变化的分析可以提供深刻的见解 大大简化了免疫治疗靶向抑制剂序贯组合试验的设计。的 项目1的目标是提供一个可以挖掘的数据集，为有效的顺序组合设计提供信息 养生法我们将利用最先进的空间多组学组织分析工具， 在已建立的同基因黑色素瘤肿瘤模型及其相关脑中不断演变的时间的“电影” 在用每种组合疗法组分治疗后，治疗转移的患者。由此产生的时空 多组学数据将被分析，以提取一些高度信息化的时间特征， 可以构建用于预测有效的序贯组合方案的模型（项目2）。回顾性 临床肿瘤活检的研究被提出来验证模型发现。