Eco-Evolutionary dynamics of NSCLC to immunotherapy: Response and Resistance

非小细胞肺癌对免疫治疗的生态进化动力学：反应和耐药

• 批准号: 10005265
• 负责人: SCOTT J. ANTONIA
• 金额: $ 60.35万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005265
• 关键词: Antineoplastic Protocols; Biopsy; Blood; CTLA4 gene; Cancer Patient; Categories; Cells; Clinical; Clinical Data; Clinical Protocols; Clinical Research; Clinical Trials; Cohort Analysis; Collaborations; Combined Modality Therapy; Complex; Computer Simulation; Cytotoxic agent; Data; Disease; Engineering; Evolution; Fibroblasts; Hematoxylin and Eosin Staining Method; Histologic; Histology; Human; Image; Immune; Immune checkpoint inhibitor; Immune response; Immunization; Immunohistochemistry; Immunotherapeutic agent; Immunotherapy; In Vitro; Intuition; Investigation; Malignant Neoplasms; Malignant neoplasm of lung; Mathematical Model Simulation; Mathematics; Modeling; Molecular; Non-Small-Cell Lung Carcinoma; Nonlinear Dynamics; Nonmetastatic; Oncologist; Oncology; Patients; Periodicity; Peripheral Blood Lymphocyte; Primary Neoplasm; Protocols documentation; Radiology Specialty; Resected; Resistance; Stains; Testing; Thoracic Oncology; Time; Tissues; Tumor Cell Line; Tumor-infiltrating immune cells; anti-PD-L1; base; cancer cell; cancer immunotherapy; cancer therapy; clinical biomarkers; clinical predictors; cohort; experience; experimental study; imaging scientist; imaging study; immune resistance; improved; improved outcome; individual patient; individualized medicine; mathematical model; multidisciplinary; multiple omics; optimal treatments; outcome prediction; phase I trial; pre-clinical; predictive modeling; predictive test; programmed cell death ligand 1; prospective; radiomics; response; response biomarker; spatial temporal variation; therapy design; tool; treatment strategy; tumor

Abstract Worldwide, Non-Small Cell Lung Cancer (NSCLC) is the most common and among the most lethal of human cancers with about 2 million new cases per year and a 5 year survival for metastatic disease of about 1%. Fortunately, a number of new treatments have improved the lives of some patients with NSCLC. During the past decade, the Moffitt Thoracic Oncology Department has pioneered new strategies using immunotherapy for NSCLC. In almost 500 patients treated with immunotherapy at Moffitt, the response rate (CR, PR, and SD) is 30 to 45% (2, 3). Most responses are followed by evolution of resistance and progression but some patients in each category have experienced durable responses maintained for > 1 year (2). Our underlying hypothesis is that the observed results from immunotherapy can be improved with sufficient understanding of the evolutionary (cellular and molecular) and ecological (tissue) dynamics that govern response and resistance of NSCLC to immunotherapy. We have previously demonstrated that administration of cancer therapy can be optimized through evolutionary mathematical models that frame the complex, often non-linear underlying dynamics. To develop such models in immunotherapy of NSCLC, we will analyze a Moffitt NSCLC immunotherapy patient cohort all of whom were treated within an investigational protocol in which tumor biopsies are performed prior to therapy and after 6 weeks of immunotherapy. They also underwent repeated imaging with radiomic analyses and blood studies during the course of therapy, and many had biopsies at the time of progression. Retrospective analysis of this cohort will investigate the evolutionary (molecular, cellular) data as well as ecological (histological and radiological) dynamics that govern response and resistance to immunotherapy. These investigations will be supplemented from additional ex vivo studies in which tumor and immune cells obtained from resected primary tumors are dispersed in culture allowing the immediate response to immunotherapy agents to be assessed. We will also perform in vitro studies that dissect the wide range of cellular and tissue ecological engineering strategies available to NSCLC cells as well as the timescales of immunotherapy adaptation. Finally, we will test the predictive power of our developed mathematical models to use pre-therapy data to predict outcomes from monotherapy with PD-L1 checkpoint inhibitors. We will then extend these models by integrating additional immunosuppressive mechanisms and test these models in a second clinical cohort treated with combinations of PD-LI and CTLA-4 checkpoint inhibitors.

摘要 非小细胞肺癌（NSCLC）是世界范围内最常见、最致命的人类肿瘤之一， 癌症，每年约有200万新病例，转移性疾病的5年生存率约为1%。 幸运的是，一些新的治疗方法已经改善了一些NSCLC患者的生活。期间 在过去的十年里，莫菲特胸部肿瘤科率先采用免疫疗法的新策略 对于NSCLC。在Moffitt接受免疫治疗的近500例患者中，缓解率（CR，PR和SD） 是30%到45%（2，3）。大多数缓解后出现耐药和进展，但有些患者 在每个类别中，都经历了持续超过1年的持久反应（2）。我们的基本假设 免疫治疗的观察结果可以通过充分了解免疫治疗的作用来改善。 进化（细胞和分子）和生态（组织）动力学，控制反应和抵抗力， 非小细胞肺癌免疫治疗。我们以前已经证明，癌症治疗的管理可以 通过进化的数学模型进行优化，这些模型构建了复杂的，通常是非线性的基础模型， 动力学为了在NSCLC的免疫治疗中开发这样的模型，我们将分析Moffitt NSCLC， 免疫治疗患者队列，所有患者均在研究方案中治疗，其中肿瘤 在治疗前和免疫治疗6周后进行活组织检查。他们还经历了多次 在治疗过程中进行放射性分析和血液研究，许多人在治疗过程中进行了活检。 进步的时间。该队列的回顾性分析将研究进化（分子，细胞） 数据以及生态学（组织学和放射学）动态，这些动态决定了对 免疫疗法这些研究将从额外的离体研究中得到补充，其中肿瘤和 将从切除的原发性肿瘤获得的免疫细胞分散在培养物中 免疫治疗剂进行评估。我们还将进行体外研究， NSCLC细胞可用的细胞和组织生态工程策略以及 免疫适应最后，我们将测试我们开发的数学模型的预测能力， 使用治疗前数据预测PD-L1检查点抑制剂单药治疗的结局。然后我们将 通过整合额外的免疫抑制机制来扩展这些模型，并在 用PD-LI和CTLA-4检查点抑制剂的组合治疗的第二临床群组。