Summa - Moffitt CSBC Pro osal 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 ("Δ-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 ti.-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 si/ico (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 Δ-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 Δ-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 Δ-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 Δ-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.