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， 阐明治疗失败的未知因素。虽然他们没能治愈大多数病人 对于常见的转移性实体癌（如乳腺癌和肺癌），免疫疗法对肿瘤的治疗有显著影响。 少数晚期肺癌和黑素瘤患者。虽然这些潜在的治愈癌症的疗法 正在迅速发展和测试，一个主要的障碍是缺乏定量模型来描述和评估 他们的功效。本项目旨在探索癌细胞的临床相关数学和计算机模型 个性化免疫治疗的动力学。我们将重点关注两个不同的，但密切相关的， 癌症治疗的方法：（1）过继细胞转移，其中体外工程化和个性化的肿瘤- 输注浸润性T细胞以抑制肿瘤生长;和（2）加强抗肿瘤的检查点抑制剂 效应免疫细胞的活性。最近，大量与免疫相关的生物标志物数据已经成为 他们与机械的紧密结合，数学模型将释放他们的解释性和 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 数据来推断和量化控制癌症-免疫相互作用的关键参数。具体而言，目标1将 发展一个基于动力系统方法的定量数学框架， 过继性细胞转移方法有效性的临床评估指南。目标2将优化治疗 检查点抑制剂及其潜在组合的策略，而目标3将评估和确定 通过将计算建模与单细胞紧密结合， 测序数据来自动物模型和临床试验。本设计将使用一个理论框架来评估 并比较不同组合的疗效，以及提供最低疗效的指导 以及实现积极临床结果所需的检查点抑制剂的最佳剂量方案。这 该提案将开发临床相关的数学和计算机模型，以促进新型癌症的发展。 免疫策略被构思、测试和理解。由于其固有的灵活性，这些- 计算机模型也可以容易地与癌细胞水平上的特定癌症谱结合， 以个性化的方式做出明智的治疗决策并预测治疗结果。最终 目标是使用这些计算机模拟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，所有这些都着眼于模型通知的个性化 免疫疗法。