# Exploiting Ecology and Evolution to Prevent Therapy Resistance in EGFR-Driven Lung Cancer

> **NIH NIH R37** · CLEVELAND CLINIC LERNER COM-CWRU · 2022 · $447,631

## Abstract

ABSTRACT
Lung cancer is the leading cause of cancer deaths in the USA, with an estimated 158,000 deaths in 2016. The
direct cause of the majority of these deaths is the eventual emergence of resistance to initially effective therapies.
This evolution of drug resistance represents one of the greatest unmet needs in oncology. While most research
is focused on the individual molecular alterations that confer this resistance, we instead propose to focus on the
eco-evolutionary processes that generate these alterations. To study the Darwinian evolution and ecological
interactions occurring within heterogeneous tumors, we will tightly integrate bespoke mathematical models and
experimental techniques designed to inform them. Focusing on EGFR mutated non-small cell lung cancer, a
cancer type with a highly efficacious targeted therapy with which we have experience in our lab, we will approach
this problem with three, orthogonal, integrated mathematical-experimental Aims. First, to understand the
ecological interactions occurring at the inter-cellular level in heterogeneous tumors, we will couple our experience
with evolutionary game theory with our first-in-class evolutionary game assay, which we have designed to
specifically for this purpose. Here, we hope to learn to target the interactions that drive resistance – a novel
strategy which could open up entirely new avenues of drug design. Second, we will allow evolution to show us
the convergent phenotypes it creates in the face of specific selective pressures through long-term directed
evolution. During this long-term evolution, we will measure phenotype, in the form of drug sensitivity to a panel
of chemotherapeutics and targeted therapies at regular intervals, creating the first temporal collateral sensitivity
map in any solid tumor. By pooling common phenotypes observed throughout the evolutionary life history, we
will then use interactomic and seed-based protocols to generate molecular signatures of these states of
sensitivity, which we will validate in publicly available data and in an in library of PDX lines. Finally, we will delve
deeply into the relevant time scales of the ecological and evolutionary processes we study in the first two aims.
We plan to apply the replicator-mutator framework of evolutionary game theory to a spatial transform that we
pioneered in cancer. To validate and parameterize these models, we will also test the evolutionary stability of
the ecological dynamics we measure with our game assay by performing the assay through evolutionary time
during a long-term evolution experimental. Each of our three orthogonal aims is supported by recent high impact
publications, and each represent tightly coupled experimental and computational protocols. Our clean, well-
designed integration, together with innovative focus on the direct study of the evolutionary biology, promises to
shed light on this difficult area of cancer research, and offers the possibility of providing generalizable i...

## Key facts

- **NIH application ID:** 10312107
- **Project number:** 5R37CA244613-03
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** Jacob Gardinier Scott
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $447,631
- **Award type:** 5
- **Project period:** 2019-12-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312107

## Citation

> US National Institutes of Health, RePORTER application 10312107, Exploiting Ecology and Evolution to Prevent Therapy Resistance in EGFR-Driven Lung Cancer (5R37CA244613-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10312107. Licensed CC0.

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