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Grant Details

Grant Number: 1U01CA249866-01 Interpret this number
Primary Investigator: Kraft, Peter
Organization: Harvard School Of Public Health
Project Title: Multifactoral Breast Cancer Risk Prediction Accounting for Ethnic and Tumor Diversity
Fiscal Year: 2020


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.



Publications

Principles and methods for transferring polygenic risk scores across global populations.
Authors: Kachuri L. , Chatterjee N. , Hirbo J. , Schaid D.J. , Martin I. , Kullo I.J. , Kenny E.E. , Pasaniuc B. , Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group , Witte J.S. , et al. .
Source: Nature reviews. Genetics, 2024 Jan; 25(1), p. 8-25.
EPub date: 2023-08-24.
PMID: 37620596
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Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.
Authors: Balasubramanian J.B. , Choudhury P.P. , Mukhopadhyay S. , Ahearn T. , Chatterjee N. , GarcĂ­a-Closas M. , Almeida J.S. .
Source: ArXiv, 2023-10-13; , .
EPub date: 2023-10-13.
PMID: 37873020
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Polygenic risk scores for prediction of breast cancer in Korean women.
Authors: Jee Y.H. , Ho W.K. , Park S. , Easton D.F. , Teo S.H. , Jung K.J. , Kraft P. .
Source: International journal of epidemiology, 2023-06-06; 52(3), p. 796-805.
PMID: 36343017
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Potential utility of risk stratification for multicancer screening with liquid biopsy tests.
Authors: Kim E.S. , Scharpf R.B. , Garcia-Closas M. , Visvanathan K. , Velculescu V.E. , Chatterjee N. .
Source: NPJ precision oncology, 2023-04-22; 7(1), p. 39.
EPub date: 2023-04-22.
PMID: 37087533
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Validating Breast Cancer Risk Prediction Models in the Korean Cancer Prevention Study-II Biobank.
Authors: Jee Y.H. , Gao C. , Kim J. , Park S. , Jee S.H. , Kraft P. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2020 Jun; 29(6), p. 1271-1277.
EPub date: 2020-04-03.
PMID: 32245787
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