Project 1: Delta immune Ecology of NSCLC

摘要-项目1 项目1将研究NSCLC中肿瘤-免疫相互作用的生物学生态学，重点是KRAS突变 癌的免疫疗法的反应率为30%至45%。大多数答复都是 抵抗力和进展的进化。最近我们量化了这些癌症的免疫生态 从治疗前的活组织检查中，发现了独特的生态相互作用，可以预先确定治疗反应。一 这项建议的重点是使用临床试验的治疗前和治疗中的活检来量化 免疫系统-治疗期间的生态学。我们将使用一个计算多路复用图像分析管道， 全载玻片图像，并将数据分割成细胞和样方，以分析空间和时空 这些特征代表了推动肿瘤进展的生态变化。然后，我们将利用此分析作为 时空生物标志物来通过数学建模改变治疗策略。计算 基础设施将以机械模型、空间统计、机器学习和深度学习为基础。 靶向治疗也是治疗这种疾病的关键组成部分。在一个令人兴奋的发现中， 已经开发了靶向KRAS-G12 C突变癌症的抑制剂，并且已经证明了临床应用。 功效此外，临床前工作已经证明，这些抑制剂改变了免疫系统的免疫生态。 肿瘤，提供了联合靶向治疗和免疫治疗可以显着改善的承诺， 应答我们将对小鼠肿瘤进行体内研究，并对人类肿瘤进行深层生态分析， 确定KRAS抑制剂引起的免疫系统-生态学的机制，以及探针组合 协同抗肿瘤反应的疗法。我们的最终目标是建立一个预测模型， 生态学-提供新的治疗策略并改善结果，用于未来的临床试验。

Summary – Project 1 Project 1 will study the ∆-Ecology of tumor-immune interactions in NSCLC, with a focus on KRAS-mutant cancers. Immunotherapy has demonstrated response rates of 30 to 45%. Most responses are followed by evolution of resistance and progression. Recently we have quantified the immune ecology of these cancers from pre-treatment biopsies and found unique ecological interactions that predetermine therapy response. A focus of this proposal will be to use both pre- and on-treatment biopsies from a clinical trial to quantify the immune ∆-Ecology during therapy. We will use a computational multiplexed-image analysis pipeline that takes whole-slide images and segments the data into cells and quadrats to analyze the spatial and spatiotemporal features representing ecological changes that drive tumor progression. We will then harness this analysis as a spatiotemporal biomarker to alter treatment strategies via mathematical modeling. The computational infrastructure will be grounded in mechanistic models, spatial statistics, machine learning, and deep learning. Targeted therapies are also a key component in the treatment of this disease. In an exciting discovery, inhibitors that target KRAS-G12C mutant cancers have been developed and have demonstrated clinical efficacy. Furthermore, preclinical work has demonstrated that these inhibitors alter the immune ecology of tumors, offering promise that combination targeted therapy and immunotherapy can significantly improve responses. We will perform in vivo work with murine tumors and deep ecological analysis of human tumors to determine the mechanisms of immune ∆-Ecology fomented by KRAS inhibitors, and probe combination therapies that synergize the anti-tumor response. Our ultimate goal is the development of a predictive model of ∆-Ecology that delivers novel treatment strategies and improves outcomes, for use in future clinical trials.