# Optimizing Detection and Interventions Against Rare Pre-existing Drug Resistance Mutations

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $664,591

## Abstract

Abstract
Genetic mutations that cause drug failure are a major obstacle in many diseases including cancer, HIV,
cytomegalovirus, and tuberculosis. Therapy-induced selection for resistance-conferring aberrations can arise
either from new mutations or from those that pre-existed in a small subpopulation of the cells or viruses. This
latter idea of pre-existing subclonal drug resistance is highly understudied across diseases, and there is no
general clinical consensus on the best way to implement counter-resistance therapies. A large part of this is due
to technical difficulties in detecting the subpopulations at both sufficient resolution and high enough throughput.
Here, we leverage a novel high-sensitivity DNA mutation detection technology, multiplex blocker displacement
amplification, and melanoma as a model system to study subclonal drug resistance for multiple genes
inhundreds of pre-therapy patient samples. We will pair this clinical study with novel mouse models of subclonal
resistance to optimize risk-reward strategies for counter-resistance therapies. Our preliminary data from
melanoma patients are consistent with data from other cancers suggesting that very low allelic-frequency
subclonal resistance mutations could pre-exist in over a third of patients' tumors. Therefore, our overall approach
is aimed at determining how to best treat patients with potential subclonal resistance, first by improving mutation
detection in patients and second by determining which mutation-positive patients would most benefit from
optimally-timed counter-resistance interventions. Although we start with melanoma as a model system, our
approach will serve as a broadly-applicable blueprint for recognizing and overcoming pre-existing subclonal
resistance.

## Key facts

- **NIH application ID:** 10449299
- **Project number:** 5R01HG011356-03
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Lawrence Kwong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $664,591
- **Award type:** 5
- **Project period:** 2020-09-21 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449299

## Citation

> US National Institutes of Health, RePORTER application 10449299, Optimizing Detection and Interventions Against Rare Pre-existing Drug Resistance Mutations (5R01HG011356-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10449299. Licensed CC0.

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