# Exploiting convergent evolution to design biomarker extraction tools for the prediction of therapeutic response in cancer

> **NIH NIH F30** · CASE WESTERN RESERVE UNIVERSITY · 2021 · $51,036

## Abstract

PROJECT SUMMARY/ABSTRACT
 The effective treatment of drug resistant tumors represents one of the greatest unmet needs in oncology
research. The evolution of therapeutic resistance in cancer is a dynamic process, shaped by many external
forces, including selection pressures, microenvironment, and the timescales of clinical treatments. As tumors
evolve under these heterogeneous settings, a variety of genotypes emerge and lead to large differences in drug
response phenotypes between patients. By grouping tumors based on their response to treatment, we can
exploit principles of convergent evolution, where similar phenotypes evolve independently between individuals. In
doing so, this work aims to aid precision medicine by identifying commonalities between tumors with similar drug
response phenotypes.
 Gene expression signatures are a powerful tool that can be used to predict convergent states of drug sensitiv-
ity and resistance. Using vast open-source datasets, Aim 1 of this proposal will demonstrate a novel method for
extracting and validating gene expression signatures to predict therapeutic response in cancer. Cell lines with the
best and worst response to a given drug are pooled and compared using differential gene expression analysis.
Genes with increased expression in a state of sensitivity or resistance become seed genes in a co-expression
network based on gene expression from tumor samples. From there, only seed genes with strong co-expression
within patient samples are extracted to form the final gene expression signature. This novel approach integrates
clinical sample data to the signature extraction method in order to increase translational value compared to molec-
ular signatures extracted using only cell line datasets. Next, Aim 2 of this proposal investigates the phenomenon
of collateral sensitivity, where resistance to one drug aligns with sensitivity to another drug. Because the evo-
lution of collateral resistance and sensitivity can be unpredictable, molecular signatures of convergent states of
collateral sensitivity and resistance could greatly enhance treatment planning once resistance to first-line ther-
apy has evolved. Using EGFR+ non-small cell lung cancer cell lines as a model system, this project aims to
identify molecular signatures of evolutionarily convergent collateral sensitivity/resistance phenotypes during the
experimental evolution of therapeutic resistance to targeted therapies.

## Key facts

- **NIH application ID:** 10138410
- **Project number:** 1F30CA257076-01
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Jessica Anne Scarborough
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 1
- **Project period:** 2021-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10138410

## Citation

> US National Institutes of Health, RePORTER application 10138410, Exploiting convergent evolution to design biomarker extraction tools for the prediction of therapeutic response in cancer (1F30CA257076-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10138410. Licensed CC0.

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