# Biomarker and Phenotypic Risk Factors for Breast Cancer Lymphedema

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $623,978

## Abstract

   
DESCRIPTION (provided by applicant):  Lymphedema (LE) following treatment for breast cancer is the most common form of secondary LE in the industrialized world. It occurs in 20% to 87% of patients following treatment for breast cancer and results in significant disability. At the
present time, the definitive phenotypic, genotypic and epigenotypic predictors that place patients at highest risk for the development of LE are not known. Therefore, the specific aims of this study, in a sample of patients following treatment for breast cancer, are to: determine genetic predictors of LE using a candidate gene approach and evaluate for epigenetic changes, as measured by DNA methylation and subsequent gene expression, in candidate genes associated with the diagnosis of LE. The secondary aims of this study are to: evaluate for latent classes of women with distinct phenotypic predictors of LE; and evaluate for differences in symptoms, functional status, and QOL outcomes between women with and without LE and among the latent classes with LE. The results of this study will provide new information on the underlying mechanisms for LE and allow for the development and testing of novel approaches to prevent or reduce the negative effects of LE.

## Key facts

- **NIH application ID:** 9966884
- **Project number:** 5R01CA187160-06
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** CHRISTINE A. MIASKOWSKI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $623,978
- **Award type:** 5
- **Project period:** 2015-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966884

## Citation

> US National Institutes of Health, RePORTER application 9966884, Biomarker and Phenotypic Risk Factors for Breast Cancer Lymphedema (5R01CA187160-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9966884. Licensed CC0.

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