Project 1: Delta immune Ecology of NSCLC

摘要-项目1 项目1将研究非小细胞肺癌肿瘤-免疫相互作用的∆生态学，重点是KRAS突变 癌症。免疫疗法的有效率为30%至45%。大多数回复后面紧跟着 抗药性和进步性的演变。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，发现了预先决定治疗反应的独特的生态相互作用。一个 这项提案的重点将是使用来自临床试验的治疗前和治疗中的活检来量化 免疫∆-治疗过程中的生态学。我们将使用计算多路传输图像分析流水线 整体幻灯片图像并将数据分割为像元和样方，以进行空间和时空分析 代表推动肿瘤进展的生态变化的特征。然后，我们将利用此分析作为 通过数学建模改变治疗策略的时空生物标记物。计算性的 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一个关键组成部分。在一个令人兴奋的发现中， 针对KRAS-G12C突变癌症的抑制剂已经开发出来，并已在临床上得到验证 功效。此外，临床前的工作表明，这些抑制剂改变了免疫生态。 肿瘤，提供了联合靶向治疗和免疫治疗可以显著改善的前景 回应。我们将对小鼠肿瘤进行体内工作，并对人类肿瘤进行深入的生态分析 确定KRAS抑制剂诱导的免疫∆生态机制，并结合探针 协同抗肿瘤反应的治疗方法。我们的最终目标是开发一种预测模型 ∆-提供新的治疗策略和改善结果的生态学，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.