Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules

以新兴细胞动力学量化为指导的多尺度计算模型，用于预测最佳免疫检查点和靶向治疗方案

• 批准号: 10624253
• 负责人: Trachette Jackson
• 金额: $ 48.48万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10624253
• 关键词: 3-Dimensional; Address; Affect; American; Antigens; Basic Science; Behavior; Biological; Biological Assay; Biological Models; Bladder Neoplasm; CD8-Positive T-Lymphocytes; Calibration; Cancer Model; Cancer Patient; Cause of Death; Cell Communication; Cells; Cessation of life; Clinical; Clinical Research; Clinical Trials; Combination immunotherapy; Combined Modality Therapy; Computational Biology; Computer Models; Cytoplasmic Granules; Data; Diagnosis; Drug Targeting; Evaluation; FDA approved; FGFR3 gene; Fibroblast Growth Factor Receptors; Future; Goals; Human; Hybrids; Image; Immune; Immune checkpoint inhibitor; Immune system; Immunohistochemistry; Immunotherapy; Individual; Knowledge; Laboratories; Ligands; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Mathematics; Measures; Mediating; Modeling; Molecular; Molecular Target; Monoclonal Antibodies; Mutation; Nivolumab; Outcome; Pathway interactions; Patient-Focused Outcomes; Persons; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Proteins; Publishing; Receptor Protein-Tyrosine Kinases; Resistance; Schedule; Speed; Survival Rate; T-Lymphocyte; Technology; Testing; Theoretical Studies; Therapeutic; Time; Translating; Treatment Failure; Treatment Protocols; Tumor Necrosis Factor Ligand Superfamily Member 6; Tyrosine Kinase Inhibitor; United States; Urinary system; Work; alternative treatment; cancer cell; cancer therapy; cell behavior; cell growth; cell killing; cell motility; checkpoint inhibition; checkpoint therapy; chemotherapy; clinically relevant; computational platform; computer framework; cytotoxicity; design; experimental study; immune checkpoint; improved; in silico; information gathering; inhibitor; inhibitor therapy; innovation; insight; kinase inhibitor; live cell imaging; mathematical methods; mathematical model; movie; multi-scale modeling; neoplastic cell; novel; novel drug combination; objective response rate; predictive modeling; programmed cell death ligand 1; receptor; response; small molecule; small molecule inhibitor; spatiotemporal; synergism; targeted agent; targeted treatment; therapeutic effectiveness; therapy design; therapy development; tool; treatment optimization; treatment strategy; tumor; tumor growth

Project Summary The principal goal of this proposal is to combine multiscale mathematical modeling with novel computational model-driven quantitative experimental platforms to develop a comprehensive and predictive 3D computational framework. Bladder cancer is one of the 10 most common cancers in the United States and in its advanced stages the 5-year survival rates are below 35%. Given the poor outcomes with chemotherapy in advanced cases, immunotherapy has emerged as an exciting domain for exploration. Monoclonal antibodies targeting the PD- 1/PD-L1 “immune checkpoint” pathway have resulted in favorable outcomes in advanced bladder cancer, and 5 drugs targeting this pathway have been approved in the past two years. Unfortunately, the objective response rates of current FDA approved immunotherapy drugs remain less than 25%. An alternative treatment strategy for bladder cancer is small molecule inhibitors (SMIs) of fibroblast growth factor receptor (FGFR3), and early clinical studies using these molecular-targeted agents have shown promise. Recently published data supporting the co-acting combination of potent immune checkpoint inhibitors and specific FGFR3 inhibitors potentially offer an advance in targeted therapeutics for cancer. A powerful and practical way to optimize novel drug combinations for clinical cancer treatment is to use sophisticated, data-driven computational models. Our proposed agent- based model platform will both aid in the characterization of tumor-immune dynamics and also suggest the best strategies for administering therapeutic combinations of immune-checkpoint and receptor kinase inhibitors. The model will be parameterized at the molecular and cellular scales by an innovative high-throughput image quantification pipeline that allows T-cell or cancer cell behaviors and interactions to be observed, tracked, and quantified. Importantly, this model system pipeline can measure the antigen burden on tumor cells and the proportion of the two types of T-cell cytotoxicity (Fas-ligand vs. granule-based). Our experimentally-driven multiscale approach is posed to (1) significantly enhance the current understanding of the impact of differential cell-kill mechanisms on tumor-immune outcomes; (2) optimize the administration of combination therapy and maximize tumor response; and (3) to improve the ability to select the most promising drugs for the clinical trials. While based on tumors of the bladder, the platform that we are developing is easily adaptable for the study of any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type. The true significance of our work lies in its translational value: our experimental and theoretical studies will be able to test clinically relevant hypotheses regarding the prospect of receptor tyrosine kinase inhibitors and immune checkpoint inhibitors to impact the mechanism of tumor cell kill by immune cells in distinct ways. Cancer is one of the leading causes of death for Americans and at present the overall effectiveness of therapeutic treatments is only approximately 50%. The development treatment optimization tools could have enormous and immediate impact on the lives of millions of people diagnosed with cancer.

项目摘要 该提案的主要目标是将多尺度数学建模与新颖的计算相结合 模型驱动的定量实验平台，以开发全面和预测的3D计算 框架。膀胱癌是美国10种最常见的癌症之一 阶段的5年生存率低于35％。鉴于晚期化学疗法的结果差， 免疫疗法已成为一个令人兴奋的探索领域。靶向PD-的单克隆抗体 1/PD-L1“免疫检查点”途径已导致晚期膀胱癌的有利结果，5个 在过去的两年中，针对该途径的药物已得到批准。不幸的是，客观响应 当前FDA批准的免疫疗法药物的发生率仍低于25％。另一种治疗策略 对于膀胱癌是成纤维细胞生长因子受体（FGFR3）的小分子抑制剂（SMI）和早期 使用这些分子靶向剂的临床研究表明了有望。最近发布的数据支持 潜在的免疫检查点抑制剂和特定FGFR3抑制剂的共同作用组合潜在提供 针对性疗法的癌症疗法。优化新型药物组合的强大而实用的方法 对于临床癌症，治疗是使用复杂的数据驱动计算模型。我们提出的代理人 基于基于的模型平台既将有助于表征肿瘤免疫动力学的表征，又提出了最佳 管理免疫检查和受体激酶抑制剂的治疗组合的策略。这 模型将通过创新的高通量图像在分子和细胞尺度上进行参数化 定量管道允许观察，跟踪T细胞或癌细胞行为和相互作用 量化。重要的是，该模型系统管道可以测量肿瘤细胞和 两种类型的T细胞细胞毒性的比例（FAS-配体与基于颗粒的T）。我们的实验驱动 多尺度方法定位为（1）显着增强了对差异影响的当前理解 肿瘤免疫结局的细胞杀伤机制； （2）优化组合疗法的给药和 最大化肿瘤反应； （3）提高为临床试验选择最有前途的药物的能力。 虽然基于膀胱的肿瘤，但我们正在开发的平台很容易适应 任何针对任何肿瘤类型中的免疫切除蛋白和受体激酶的疗法。真实 我们工作的意义在于其翻译价值：我们的实验和理论研究将能够测试 关于受体酪氨酸激酶抑制剂和免疫的临床相关假设 检查点抑制剂以不同的方式影响免疫细胞杀死肿瘤细胞的机制。癌症是一个 美国人的主要死亡原因以及目前的治疗疗法的总体效率 仅约50％。开发处理优化工具可能具有巨大而直接的 影响数百万被诊断出患有癌症的人的生活。