# Predicting the Breast Cancer Risk for Women Veterans

> **NIH VA I01** · PORTLAND VA MEDICAL CENTER · 2020 · —

## Abstract

Despite years of research, optimal breast cancer screening strategies remain elusive, especially for women
between the age of 40 and 49. Academic societies and agencies differ in their recommendations regarding the
age to begin mammography and the screening intervals. One potential solution is risk-adapted screening,
where decisions around the starting age, stopping age, frequency, and modality of screening are based on
individual risk to maximize the early detection of aggressive cancers and minimize the harms of unnecessary
screening. In addition to demographics, family history, breast density, and other risk factors, single nucleotide
polymorphism (SNP) profiling of germ line DNA has been incorporated into breast cancer prediction models
that can further guide our clinical recommendations for screening. Of relevance to every woman, the ~100 low
penetrant single nucleotide polymorphism (SNP) confers a small risk of breast cancer development but affects
many women due to the high risk allele frequency. High to median penetrant mutations of cancer susceptible
genes, such as BRCA and Lynch syndrome genes, are associated with a higher risk of breast cancer
development but affect only a minority of women who are carriers. Women Veterans in the Million Veteran
Program (MVP) represent a cohort of women for whom comprehensive genetic information and clinical
covariates have been obtained, providing an exceptional opportunity to develop, optimize and/or validate a risk
adapted breast cancer screening strategy. Women predicted to have an elevated risk of developing breast
cancer by prediction models may benefit from screening beginning at a younger age and more frequent breast
imaging including the incorporation of breast MRI. Women predicted to have a low(er) risk for breast cancer
may do well with less intense screening. Because women Veterans in MVP may have unique military and
environmental exposures, it is unknown whether previously developed breast cancer risk prediction models
can be applied to this population. Moreover, since 28% of women Veterans in the current MVP cohort are of
African American descent, while the genetic markers that contribute to the construction of genetic prediction
models are developed from studies involving Caucasians, it is not clear if these instruments can be applied to
women that are of diverse ethnic backgrounds. Our study will determine if breast cancer prediction models built
on currently available SNPs can be validated in women Veterans in the MVP. Moreover, we will determine
whether mutant alleles of cancer susceptibility genes with median to high penetrance will confer the same
(breast) cancer risks as previously established. A higher cancer risk incurred by mutation in these cancer
susceptible genes may make universal testing cost effective, which can further facilitate and motivate the
adoption of genetic profiling to build breast cancer prediction models for every woman. We propose to build
breast cancer risk ...

## Key facts

- **NIH application ID:** 10003815
- **Project number:** 5I01BX004188-02
- **Recipient organization:** PORTLAND VA MEDICAL CENTER
- **Principal Investigator:** CYNTHIA A. BRANDT
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003815

## Citation

> US National Institutes of Health, RePORTER application 10003815, Predicting the Breast Cancer Risk for Women Veterans (5I01BX004188-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10003815. Licensed CC0.

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