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: 9788320
• 负责人: SCOTT J. ANTONIA
• 金额: $ 65.08万
• 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/9788320
• 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; Immunosuppressive Agents; Immunotherapeutic agent; Immunotherapy; In Vitro; Intuition; Investigation; Malignant Neoplasms; Malignant neoplasm of lung; Mathematics; Modeling; Molecular; Non-Small-Cell Lung Carcinoma; Nonlinear Dynamics; Nonmetastatic; Oncologist; PDCD1LG1 gene; Patients; Periodicity; Peripheral Blood Lymphocyte; Primary Neoplasm; Protocols documentation; Radiology Specialty; Resected; Resistance; Stains; Testing; Thoracic Oncology; Time; Tissues; Tumor Cell Line; 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; mathematical model; multidisciplinary; multiple omics; oncology; optimal treatments; outcome prediction; phase I trial; pre-clinical; predictive modeling; predictive test; 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.

抽象的 在世界范围内，非小细胞肺癌 (NSCLC) 是人类最常见且最致命的癌症之一 每年约有 200 万新发癌症病例，转移性疾病的 5 年生存率约为 1%。 幸运的是，许多新疗法改善了一些非小细胞肺癌患者的生活。期间 过去十年，莫菲特胸部肿瘤科开创了使用免疫疗法的新策略 对于非小细胞肺癌。在莫菲特接受免疫疗法治疗的近 500 名患者中，缓解率（CR、PR 和 SD） 为 30% 至 45% (2, 3)。大多数反应之后都会出现耐药性和进展，但有些患者 每个类别都经历了持续超过 1 年的持久反应 (2)。我们的基本假设 是通过充分了解免疫疗法的观察结果可以得到改善 控制反应和抵抗的进化（细胞和分子）和生态（组织）动力学 非小细胞肺癌免疫治疗。我们之前已经证明，癌症治疗可以 通过进化数学模型进行优化，该模型构建了复杂的、通常是非线性的底层 动力学。为了在 NSCLC 免疫治疗中开发此类模型，我们将分析 Moffitt NSCLC 免疫治疗患者队列，所有患者都在一项研究方案中接受治疗，其中肿瘤 活检在治疗前和免疫治疗 6 周后进行。他们也经历了反复 在治疗过程中进行放射组学分析和血液研究成像，许多人在治疗期间进行了活检 进展时间。对该群体的回顾性分析将调查进化（分子、细胞） 数据以及控制反应和抵抗力的生态（组织学和放射学）动态 免疫疗法。这些研究将得到额外的离体研究的补充，其中肿瘤和 从切除的原发性肿瘤中获得的免疫细胞分散在培养物中，可以立即产生反应 待评估的免疫治疗药物。我们还将进行体外研究，剖析广泛的 适用于 NSCLC 细胞的细胞和组织生态工程策略以及时间尺度 免疫治疗适应。最后，我们将测试我们开发的数学模型的预测能力 使用治疗前数据来预测 PD-L1 检查点抑制剂单一疗法的结果。我们随后将 通过整合额外的免疫抑制机制来扩展这些模型，并在 第二个临床队列采用 PD-LI 和 CTLA-4 检查点抑制剂联合治疗。