The Delta Ecology of NSCLC Treatment

摘要-莫菲特CSBC建议书概述 Moffitt CSBC的建议关注的是癌细胞之间的基本和高度动态的相互作用 肺微环境的进化和不断变化的生态。肿瘤不是简单的集合 在隔离中生长的突变细胞。相反，它们既对物理微环境做出反应，又对其进行修改 和各种宿主细胞，而这些生态学上的变化(∆生态学)是理解肿瘤的关键 进展和对治疗的反应--特别是耐药性的发展。这项提议的核心 是量化、理解和行动在治疗过程中对肿瘤和 非小细胞肺癌(NSCLC)的非肿瘤生态学 致命的人类癌症。我们将定义治理非小细胞肺癌的生态和进化动力学 通过2个项目和2个共享资源核心的进展和治疗成功或失败。每个项目都将 关注存在不同驱动因素突变(RAS、EGFR、ALK)的不同D-生态动力学 哪种靶向治疗是可用的。每个项目都展示了我们紧密的统一主题 通过开发、预测、校准、测试、优化和 验证。因此，每个项目都从可用的临床数据开始，然后是计算机(预测和优化)、 体外(校准和测试)和体内(验证)模型系统导致易于翻译的治疗选择。 数学和生态核心结合了所需的数学和计算方法 搭建项目之间的桥梁。数学核心将作为中心的机械模型引擎，促进 生态进化过程的空间和非空间模型，以生成和测试关于∆的假设- 生态学。生态核心充当数据存储库和生态分析引擎，与 这三个项目侧重于非小细胞肺癌∆-生态学(免疫和基质)的不同方面，以开发和 应用空间生态模型。这些核心将为我们的两个项目提供服务： 项目1：非小细胞肺癌1.1的增量免疫生态学量化患者样本的∆生态 预测结果的治疗；1.2 KRAS抑制剂联合免疫治疗对肺癌的影响 生态学；1.3使用预测性建模来生成进化启发的多智能体治疗策略。 项目2：NSCLC2.1的三角洲基质生态学解密，体内，∆-获得性抗性生态学 非小细胞肺癌的靶向治疗；2.2定义了基质遮盖对耐药和 肿瘤生长复发；2.3发现抑制耐药性的最佳治疗策略。

Summary – Moffitt CSBC Proposal Overview The Moffitt CSBC proposal is focused on the essential and highly dynamical interplay between cancer cell evolution and the constantly changing ecology of the lung microenvironment. Tumors are not simply collections of mutated cells that grow in isolation. Rather, they respond to and modify both the physical microenvironment and a variety of host cells, and these changes in ecology (“∆-Ecology”) are key to understanding tumor progression and the response to therapy – particularly the development of resistance. Central to this proposal is quantifying, understanding, and actioning the change that occurs during treatment to both the tumor and non-tumor ecology in non-small cell lung cancer (NSCLC), which is the most common and among the most lethal of human cancers. We will define the ecological and evolutionary dynamics that govern NSCLC progression and treatment success or failure through 2 projects and 2 shared resource cores. Each project will focus on different D-Ecology dynamics in the presence of different driver mutations (RAS, EGFR, ALK) for which targeted therapy is available. Each project demonstrates our unifying theme of tight empirical/mathematical integration through the sequence of develop, predict, calibrate, test, optimize, and validate. Thus, each project begins with available clinical data followed by in silico (predict and optimize), in vitro (calibrate and test) and in vivo (validate) model systems leading to readily translatable treatment options. The mathematical and ecological Cores incorporate the mathematical and computational methods needed for bridging the projects. The Math Core will serve as the mechanistic model engine of the center, facilitating both spatial and non-spatial models of eco-evolutionary processes to generate and test hypotheses regarding ∆- Ecology. The Ecology Core serves as data repository and ecological analysis engine working in lockstep with the three Projects focused on different aspects of NSCLC ∆-Ecology (Immune and Stromal) to develop and apply spatial ecological models. These cores will serve our two projects: Project 1: Delta immune Ecology of NSCLC 1.1 Quantify the ∆-Ecology of patient samples pre- and on- treatment to predict outcomes; 1.2 Impact of KRAS inhibitor therapy with immunotherapy on lung tumor ecology; 1.3 Use predictive modeling to generate evolutionary inspired multi-agent treatment strategies. Project 2: Delta stromal Ecology of NSCLC 2.1 Decipher, in vivo, the ∆-Ecology of acquired resistance to targeted therapies in NSCLC; 2.2 Define the impact of stromal sheltering on the emergence of resistance and tumor growth relapse; 2.3 Discover optimal therapeutic strategies to suppress resistance.