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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

基本信息

批准号：
10478888
负责人：
SCOTT J. ANTONIA
金额：
$ 59.15万
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：
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/10478888
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 lung cancer cell 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

项目摘要

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.

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。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A simulation of parental and glycolytic tumor phenotype competition predicts observed responses to pH changes and increased glycolysis after anti-VEGF therapy.

DOI：
10.1016/j.mbs.2022.108909
发表时间：
2022-09
期刊：
Mathematical biosciences
影响因子：
4.3
作者：
Frederika Rentzeperis;Naomi Miller;Arig Ibrahim-Hashim;R. Gillies;R. Gatenby;D. Wallace
通讯作者：
Frederika Rentzeperis;Naomi Miller;Arig Ibrahim-Hashim;R. Gillies;R. Gatenby;D. Wallace

Mistic: An open-source multiplexed image t-SNE viewer.

DOI：
10.1016/j.patter.2022.100523
发表时间：
2022-07-08
期刊：
PATTERNS
影响因子：
6.5
作者：
Prabhakaran, Sandhya;Gatenbee, Chandler;Robertson-Tessi, Mark;West, Jeffrey;Beg, Amer A.;Gray, Jhanelle;Antonia, Scott;Gatenby, Robert A.;Anderson, Alexander R. A.
通讯作者：
Anderson, Alexander R. A.

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition.

DOI：
10.1038/s43856-022-00110-x
发表时间：
2022
期刊：
COMMUNICATIONS MEDICINE
影响因子：
0
作者：
Strobl, Maximilian A. R.;Gallaher, Jill;West, Jeffrey;Robertson-Tessi, Mark;Maini, Philip K.;Anderson, Alexander R. A.
通讯作者：
Anderson, Alexander R. A.

Comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments.