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 总结/摘要 将免疫疗法与其他治疗方案（特别是靶向治疗）相结合是一个高度活跃的领域 旨在提高抗肿瘤疗效并将治疗益处惠及更多患者或 作为第一个突变免疫联合靶向治疗，抗PD-L1与肿瘤类型同时联合。 BRAFV600MUT 和 MEK 抑制剂（所谓的“三联”疗法）已被批准用于 BRAFV600MUT 患者 然而，这个三联体的数据似乎好坏参半，其他试验未达到关键终点， 表明同时组合并不是最佳的。 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）预处理方案可通过以下方式增强三联疗法的疗效： 增强 MAPKi 持久性并显着抑制黑色素瘤脑转移。 功效是由于促进肿瘤相关巨噬细胞的促炎极化和 引发肿瘤免疫内稳健的 T 细胞克隆扩增和克隆型趋同 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无进展生存期相关。 结果强调了每个治疗成分的顺序/时机在合理设计中的重要作用 联合疗法，还指出需要从机制上理解早期影响 时间上的每个组合治疗成分。 然而，由于数量庞大，此类序贯联合治疗试验的设计具有挑战性。 待测试的变量（顺序、剂量和时间）的复杂程度需要预测。 框架显着减少参数空间并告知有效序列的识别 在此，我们急于开发一种时空多组学的抑制剂组合。 分析早期（几天）单一疗法引起的 TIME 变化可以提供深入的见解 大大简化了免疫治疗靶向抑制剂序贯组合试验的设计。 项目 1 的目标是提供一个可以挖掘的数据集，为有效顺序组合的设计提供信息 我们将利用最先进的空间多组学组织分析工具来构建时空模型。 已建立的同基因黑色素瘤肿瘤模型及其相关大脑中时间演变的“电影” 使用每种组合治疗成分治疗后的转移。 将分析多组学数据，以提取许多信息丰富的 TIME 特征，从中基于代理的 可以构建预测有效序贯联合治疗方案的模型（项目 2）。 建议对临床肿瘤活检进行研究以验证模型结果。