# Improving the translational value of head and neck cancer patient-in-mouse models

> **NIH NIH R37** · UNIVERSITY OF WISCONSIN-MADISON · 2024 · $84,891

## Abstract

Project Summary
Patient-derived model systems are commonly used to study tumor biology and test novel treatments for head
and neck cancer. Our goal is to improve the use of mammalian model systems of head and neck cancer to
improve treatment outcomes for patients using valid, predictive, well characterized models. The goal of this
supplement is to study cancer evolution and therapeutic response with rigorous assessment of tumor biology
and genomic alterations to understand how choices made at the time of establishing models impact their
relevance over time. Aim 1 will test the concordance of response between patient-derived models and patients.
Aim 2 will investigate the impact of the immune system on tumor evolution. We will use this supplement to
support a minority graduate student in the Molecular and Cellular Pharmacology graduate program to study the
effect of cisplatin on tumor evolution and the stability of response over multiple passage, to understand how
the immune system alters tumor evolution, to provide her with additional training in team science, mentoring,
teaching, and communication, and to support her pursuit of an independent research career.

## Key facts

- **NIH application ID:** 11060735
- **Project number:** 3R37CA255330-04S2
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Randall J. Kimple
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $84,891
- **Award type:** 3
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11060735

## Citation

> US National Institutes of Health, RePORTER application 11060735, Improving the translational value of head and neck cancer patient-in-mouse models (3R37CA255330-04S2). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/11060735. Licensed CC0.

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