The Delta Ecology of NSCLC Treatment - 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.
