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.

抽象的 在全球范围内，非小细胞肺癌（NSCLC）是最常见的人，也是人类最致命的人之一 每年约有200万例新病例的癌症，转移性疾病约1％的癌症生存5年。 幸运的是，许多新疗法改善了一些NSCLC患者的生活。在 过去的十年，莫菲特胸部肿瘤学部门使用免疫疗法开创了新策略 对于NSCLC。在莫菲特（Moffitt）接受免疫疗法治疗的近500例患者中，缓解率（CR，PR和SD） 为30％至45％（2，3）。大多数反应之后是抵抗力和进展的演变，但有些患者 在每个类别中，经历了持久的响应量> 1年（2）。我们的基本假设 是否可以通过足够了解 进化（细胞和分子）和生态（组织）动力学，这些动力学控制了反应和抗性 NSCLC进行免疫疗法。我们以前已经证明，癌症治疗可以是 通过进化数学模型进行优化，该模型构建了复杂的，通常是非线性的基础 动力学。为了在NSCLC的免疫疗法中开发此类模型，我们将分析Moffitt NSCLC 免疫疗法患者队列中所有的人都接受了肿瘤的研究方案中的治疗 在治疗之前和6周的免疫疗法后进行活检。他们也重复 在治疗过程中对放射分析和血液研究进行成像，许多人在 进展时间。对该队列的回顾性分析将研究进化（分子，细胞） 数据以及生态（组织学和放射学）动态 免疫疗法。这些调查将从其他的离体研究中得到补充，其中肿瘤和 从切除的原发性肿瘤获得的免疫细胞分散在培养中，可以立即反应 进行免疫疗法剂进行评估。我们还将进行体外研究，以剖析广泛的范围 NSCLC细胞可用的细胞和组织生态工程策略以及 免疫疗法适应。最后，我们将测试我们开发的数学模型的预测能力 使用疗法前数据来预测使用PD-L1检查点抑制剂单一疗法的结果。然后我们会 通过整合其他免疫抑制机制来扩展这些模型，并在A中测试这些模型 第二种临床队列通过PD-LI和CTLA-4检查点抑制剂的组合处理。