# Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversity

> **NIH NIH U01** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2020 · $683,035

## Abstract

Abstract
Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening
timing and modality, life-style interventions, genetic testing, preventive therapy, and risk-reducing surgery.
Although a number of tools are used in practice, they face various challenges including: (i) modest
discriminatory ability due to lack of a unified model that incorporates a comprehensive set of risk-factors; (ii)
inability to produce sub-type specific risk, especially considering aggressive subtypes of breast cancer and/or
prophylactic endocrine therapy that is effective only for hormone receptor positive tumors; (iii) lack of data to
build models for different ethnic populations; and, (iv) scant validation of models, especially in healthcare
settings where models can be widely disseminated in practice. In this proposal, we will assimilate and analyze
data on a large and diverse sample of women from studies participating in the NCI Cohort Consortium to
develop a comprehensive tool that will predict breast cancer risk, overall and by sub-types, across major ethnic
groups in the US. We further propose to prospectively validate the model in different clinical settings, including
a risk-stratified screening trial. In Aim 1 we will develop a comprehensive model for predicting absolute risk of
overall breast cancer for women from multiple ethnicities, incorporating information on family history; polygenic
risk-scores (PRS); anthropometric, life-style and reproductive factors; hormonal biomarkers; and
mammographic density. In Aim 2 we will tailor these risk models to specific breast cancer subtypes, notably
estrogen receptor negative and positive cancers. In Aim 3 we will evaluate the validity of these risk prediction
models in integrated health care systems, mammography registries, and an ongoing risk-based
mammographic screening trial in the US. The resulting models could be used in diverse clinical settings to
guide preventive therapy or risk-stratified screening programs, increasing the number of breast cancer deaths
prevented while minimizing overdiagnosis and overtreatment.

## Key facts

- **NIH application ID:** 9961029
- **Project number:** 1U01CA249866-01
- **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH
- **Principal Investigator:** Nilanjan Chatterjee
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $683,035
- **Award type:** 1
- **Project period:** 2020-09-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9961029

## Citation

> US National Institutes of Health, RePORTER application 9961029, Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversity (1U01CA249866-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9961029. Licensed CC0.

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