# Characterization and targeting of rapid nucleotide exchange RAS mutations

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2020 · $373,434

## Abstract

RAS mutations drive a large proportion of deadly human tumors but no targeted therapies have
advanced to clinic. We and others have shown that certain oncogenic RAS mutations have unique
biochemical properties and have shown that functional classes of RAS mutations are differentially regulated,
opening the door to RAS allele-tailored treatment approaches. RAS allele-specific therapies have the potential
for rapid clinical translation and would be worthwhile because (1) RAS mutation status is already routinely
assessed in current clinical practice for multiple common cancers, (2) potent and selective small molecules
targeting members of RAS signaling pathways, or regulators of those members are already available, and (3)
RAS mutations occur frequently enough in cancer that a significant number of patients would benefit even
though therapies apply to subsets of RAS mutation-positive tumors. This proposal seeks to develop direct and
indirect therapeutic strategies based on the unique mode of activation and resultant signaling properties of
KRAS A146T and other RAS exchange mutants. This study has translational therapy implications given that
KRAS A146T is already screened for in clinical practice, but no targeted therapies currently exist.

## Key facts

- **NIH application ID:** 9862577
- **Project number:** 1R01CA244341-01
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Kenneth Westover
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $373,434
- **Award type:** 1
- **Project period:** 2020-03-01 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9862577

## Citation

> US National Institutes of Health, RePORTER application 9862577, Characterization and targeting of rapid nucleotide exchange RAS mutations (1R01CA244341-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9862577. Licensed CC0.

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