Project 1: Delta immune Ecology of NSCLC

项目1：NSCLC的Delta免疫生态学

基本信息

批准号：
10730405
负责人：
Alexander Robertson Allan Anderson
金额：
$ 50.07万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10730405
关键词：
Affect Antitumor Response Autopsy Biological Markers Biopsy Blood CT26 Calibration Cancer Patient Cells Clinical Clinical Data Clinical Research Clinical Trials Colon Carcinoma Combined Modality Therapy Data Development Disease Ecological Change Ecology Evolution Future Goals Human Immune Immune checkpoint inhibitor Immunotherapeutic agent Immunotherapy KRAS2 gene Lung Neoplasms MEKs Machine Learning Malignant Neoplasms Modeling Mus Nature Non-Small-Cell Lung Carcinoma Pathway interactions Patients Pharmaceutical Preparations Phase I/II Trial RAS inhibition Randomized Resistance Resistance development Sampling Signal Pathway Specimen Stains Study models System T cell response T cell therapy Testing Thoracic Oncology Tissue Donations Treatment Protocols Tumor-Infiltrating Lymphocytes Work analysis pipeline anti-PD-1 arm cancer cell cancer therapy clinical efficacy computer infrastructure deep learning design experimental study improved improved outcome in vivo inhibitor inhibitor therapy mathematical model multiplexed imaging mutant neoplastic cell novel patient response pembrolizumab phase I trial pre-clinical predicting response predictive modeling prevent programs resistance mechanism response spatiotemporal statistics synergism targeted agent targeted treatment tool treatment response treatment strategy tumor tumor progression whole slide imaging

项目摘要

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.

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，重点是Kras突变。癌症。免疫疗法显示30％至45％的反应率。大多数回答之后是抵抗和进展的演变。最近，我们量化了这些癌症的免疫生态从治疗前活检，发现了预先确定治疗反应的独特生态相互作用。一个该提案的重点将是使用临床试验的前和治疗活检来量化治疗过程中的免疫∆生态学。我们将使用采用计算多路复用图像分析管道全扫描图像并将数据分为单元格和Quadrats，以分析时空和时空代表驱动肿瘤进展的生态变化的特征。然后，我们将将此分析作为一个时空生物标志物通过数学建模改变治疗策略。计算基础设施将基于机械模型，空间统计，机器学习和深度学习。靶向疗法也是治疗该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-G12C突变体癌的抑制剂，并证明了临床功效。此外，临床前工作表明，这些抑制剂改变了免疫生态肿瘤，提供了靶向治疗和免疫疗法可以显着改善的承诺回答。我们将与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抑制剂引起的免疫∆生物学和探针组合的机制疗法可以协同抗肿瘤反应。我们的最终目标是开发一个预测模型提供新颖的治疗策略并改善预后的∆生物学，用于将来的临床试验。