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

摘要 在世界范围内，非小细胞肺癌(NSCLC)是最常见和最致命的人类癌症之一 癌症每年新增约200万例，转移性疾病的5年存活率约为1%。 幸运的是，一些新的治疗方法改善了一些非小细胞肺癌患者的生活。在.期间 在过去的十年里，莫菲特胸科肿瘤科开创了使用免疫疗法的新策略 用于非小细胞肺癌。在莫菲特接受免疫治疗的近500名患者中，应答率(CR、PR和SD) 是30%到45%(2，3)。大多数应答都伴随着耐药性的演变和进展，但也有一些患者 在每个类别中都经历了持续1年的持久反应(2)。我们的基本假设 如果对免疫治疗有足够的了解，免疫治疗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 非小细胞肺癌患者接受免疫治疗。我们之前已经证明，癌症治疗的管理可以 通过进化数学模型进行优化，这些模型构建了复杂的、通常是非线性的基础 动力学。为了在NSCLC的免疫治疗中建立这样的模型，我们将分析Moffitt NSCLC 免疫治疗患者队列，所有患者都在研究方案中接受治疗，其中肿瘤 在治疗前和免疫治疗6周后进行活检。他们还经历了反复的 在治疗过程中，通过放射分析和血液检查进行成像，许多人在治疗后进行了活组织检查 进展的时间。对这一队列的回顾分析将调查进化(分子、细胞) 数据以及生态(组织学和放射学)动力学，管理对 免疫疗法。这些研究将从额外的体外研究中补充，在这些研究中，肿瘤和 从切除的原发肿瘤中获得的免疫细胞分散在培养物中，允许立即反应 对免疫治疗药物进行评估。我们还将进行体外研究，解剖范围广泛的 可用于非小细胞肺癌细胞的细胞和组织生态工程策略以及 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 使用治疗前数据预测PD-L1检查点抑制剂单一治疗的结果。到时候我们会的 通过集成其他免疫抑制机制来扩展这些模型，并在 第二个临床队列使用PD-LI和CTLA-4检查点抑制剂的联合治疗。

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.