# Drug repurposing in breast cancer

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $446,914

## Abstract

Project Summary/Abstract
 Reducing advanced breast cancer mortality requires urgent development of better drugs and improved
therapeutic strategies; however, new drug development is extremely time-consuming and costly. With the
explosive growth of large-scale cancer genomic and phenotypic data (e.g., the Cancer Genome Atlas [TCGA])
and publicly available high-throughput screening data for thousands of small molecules (many of which have
already received regulatory approval for at least one medical condition), computational drug repositioning or
repurposing holds great potential for precision medicine and may provide tools to significantly improve breast
cancer treatment and outcomes.
 Our hypothesis is that optimal therapeutic choices can be identified for hard to treat breast cancers by
applying transcriptome-based drug sensitivity prediction methods. Our long term goal is to identify and validate
the efficacy of existing drugs in hard to treat breast cancers, namely triple negative breast cancer (TNBC) and
metastatic breast cancer (MBC). Toward this goal, this proposal contains two specific aims to develop, apply,
and improve methods to predict drug sensitivity (either as a single agent or in combination). We will also
validate these predictions in additional large-scale cancer genomic datasets and translate the results using cell
based and in vivo (mouse) models of TNBC and MBC. In Aim 1, we will focus on identifying effective drugs as
monotherapy, while Aim 2 is to identify and validate optimal therapeutic combinations.
 Our study is significant because it will accelerate the development of novel therapies for hard to treat
breast cancers by repurposing existing drugs, thus avoiding the lengthy and risky new drug development
process. The ability to tailor therapy for specific disease subtypes and identification and validation of new drug
indications will provide valuable therapeutic options in the battle against TNBC and MBC, and subsequently
reduce their associated mortality. Our proposed research is innovative in both the methodologies employed
and their applications, as our transcriptome-based drug sensitivity prediction represents a paradigms shift in
drug sensitivity prediction; furthermore, we are applying these novel prediction approaches to patient tumor
data not only for biomarker discovery in order to tailor individual therapy, but also for drug repurposing. The
ability to bring biomarker discovery and drug repurposing together will present a new opportunity for cancer
therapy, as the whole genome expression profile of a tumor will be used to provide optimal therapeutic options
in different cancers, and many “old” drugs can find a new purpose in improving cancer treatment outcomes.

## Key facts

- **NIH application ID:** 10104450
- **Project number:** 5R01CA204856-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Rong Stephanie Huang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $446,914
- **Award type:** 5
- **Project period:** 2018-02-13 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10104450

## Citation

> US National Institutes of Health, RePORTER application 10104450, Drug repurposing in breast cancer (5R01CA204856-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10104450. Licensed CC0.

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