Fu - Proj 3

富 - 项目 3

• 批准号: 10212418
• 负责人: Feng Fu
• 金额: $ 32.52万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10212418
• 关键词: Address; Adoptive Cell Transfers; Animal Model; Antibodies; Attention; Biological Markers; Biology; Breast; Cancer Biology; Cancer Model; Cancer Patient; Cells; Centers of Research Excellence; Clinical; Clinical Data; Clinical Research; Clinical Trials; Clinical Trials Design; Clinical assessments; Collaborations; Combination immunotherapy; Combined Modality Therapy; Computer Models; Couples; Data; Data Analytics; Development; Dose; Engineering; Environment; Eye; Frequencies; Future; Gene Expression; Genomics; Goals; Growth; Human; Immune; Immune checkpoint inhibitor; Immunotherapy; In Vitro; Individual; Infrastructure; Lead; Longterm Follow-up; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Mathematics; Methods; Minority; Modality; Modeling; Monitor; Monoclonal Antibodies; Nature; Oncology; Organism; Outcome; Patients; Pharmaceutical Preparations; Pre-Clinical Model; Public Health; Relapse; Research; Research Personnel; Resistance; Schedule; Solid; Source Code; Statistical Models; T-Cell Activation; T-Lymphocyte; Techniques; Testing; Therapeutic; Toxic effect; Translational Research; Treatment Efficacy; Treatment Failure; Treatment outcome; Tumor Burden; United States; Validation; Work; animal data; base; bench to bedside; cancer cell; cancer immunotherapeutics; cancer immunotherapy; cancer therapy; clinically relevant; combinatorial; comparative efficacy; cost; cytokine release syndrome; design; dosage; dynamic system; efficacy evaluation; experimental study; flexibility; immune resistance; improved; in silico; individual patient; insight; interest; laboratory experiment; mathematical model; melanoma; melanoma biomarkers; novel; novel strategies; open source; personalized immunotherapy; pre-clinical; precision medicine; resistance mutation; response; side effect; simulation; single cell sequencing; targeted treatment; theories; treatment response; tumor; tumor growth; tumor progression

PROJECT SUMMARY It is of fundamental importance to understand the key mechanisms that govern the progression of cancer and elucidate the often-unknown factors that account for treatment failures. Although they fail to cure most patients with common metastatic solid cancers (like breast and lung), immunotherapies have had a significant impact in a minority of late-stage lung cancer and melanoma patients. While these potentially curative cancer therapies are being rapidly developed and tested, a major barrier is the lack of quantitative models to describe and evaluate their efficacy. This project proposes to explore clinically relevant math and in-silico models of cancer cell dynamics for personalized immunotherapy. We will focus on two distinct, yet strongly interconnected, approaches of cancer therapy: (1) adoptive-cell transfer, in which in-vitro engineered and personalized tumor- infiltrating T-cells are transfused to suppress tumor growth; and (2) checkpoint inhibitors that boost anti-tumor activities of effector immune cells. Very recently, a wealth of immune-related biomarker data has become available—their close integration with mechanistic, mathematical models would unleash their explanatory and predictive power in treatment response and outcome. Here, Project 3 will take advantage of these biomarker data to infer and quantify key parameters that govern cancer-immune interactions. Specifically, Aim 1 will develop a quantitative mathematical framework based on the dynamical systems approach to provide practical guidance for clinical assessment of the efficacy of adoptive cell transfer approach. Aim 2 will optimize therapeutic strategies for checkpoint inhibitors and their potential combinations, while Aim 3 will evaluate and identify immune-related biomarkers for melanoma cancer by closely integrating computational modeling with single-cell sequencing data from animal models and clinical trials. This design will use a theoretical framework to assess and compare the efficacies of different combinations, as well as to provide guidance on the minimum efficacy and optimal dosage schedule of checkpoint inhibitors required to achieve positive clinical outcomes. This proposal will develop clinically relevant math and in-silico models that will facilitate the way novel cancer immunotherapeutic strategies are conceived, tested, and understood. Owing to their innate flexibility, these in- silico models also can be readily incorporated with the specific cancer profile on the cancer-cell level, and thus enable informed treatment decisions and predict treatment outcomes in a personalized fashion. The ultimate goal is to use these in-silico and mathematical models to interpret lab and clinical results and to guide design principles of future lab experiments and clinical trials, all with an eye toward model-informed personalized immunotherapy.

项目摘要 了解控制癌症进展的关键机制和 阐明造成治疗失败的原因常常不明显的因素。尽管他们无法治愈大多数患者 对于常见的转移性固体癌（如乳腺癌和肺），免疫疗法对 少数晚期肺癌和黑色素瘤患者。这些潜在的治愈性癌症疗法 正在快速开发和测试，一个主要的障碍是缺乏描述和评估的定量模型 他们的有效性。该项目的提案旨在探索癌细胞与临床相关的数学和silico模型 个性化免疫疗法的动力学。我们将专注于两个不同但又很强的相互联系， 癌症疗法的方法：（1）收养细胞转移，其中体外工程和个性化肿瘤 - 浸润T细胞被输血以抑制肿瘤的生长； （2）增强抗肿瘤的检查点抑制剂 效应免疫球的活性。最近，大量与免疫相关的生物标志物数据已成为 可用 - 他们与机械，数学模型的紧密整合不会牵引他们的删除和 治疗反应和结果的预测能力。在这里，项目3将利用这些生物标志物 数据以推断和量化控制癌症免疫相互作用的关键参数。具体来说，AIM 1将 基于动态系统方法开发定量的数学框架，以提供实用 自适应细胞转移方法效率的临床评估指南。 AIM 2将优化治疗 检查点抑制剂及其潜在组合的策略，而AIM 3将评估和识别 通过与单细胞紧密整合计算建模，用于黑色素瘤癌症的免疫相关生物标志物 从动物模型和临床试验中进行测序数据。该设计将使用理论框架来评估 并比较不同组合的效率，并提供有关最低效率的指导 以及获得阳性临床结果所需的检查点抑制剂的最佳剂量时间表。这 提案将开发临床上相关的数学和内部模型，以促进新型癌症的方式 对免疫疗法的策略进行了构思，测试和理解。由于它们的天生灵活性，这些 在癌症细胞水平上，硅模型也可以很容易地与特定的癌症概况合并，因此 实现知情的治疗决策并以个性化的方式预测治疗结果。最终 目标是使用这些内部和数学模型来解释实验室和临床结果并指导设计 未来实验室实验和临床试验的原则，所有这些都注视着模型的个性化 免疫疗法。