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
Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies.
Authors: Wang Z.
, Grosvenor L.
, Ray D.
, Ruczinski I.
, Beaty T.H.
, Volk H.
, Ladd-Acosta C.
, Chatterjee N.
.
Source: Medrxiv : The Preprint Server For Health Sciences, 2024-10-09 00:00:00.0; , .
EPub date: 2024-10-09 00:00:00.0.
PMID: 39417123
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Plasma proteomic comparisons change as coverage expands for SomaLogic and Olink.
Authors: Rooney M.R.
, Chen J.
, Ballantyne C.M.
, Hoogeveen R.C.
, Boerwinkle E.
, Yu B.
, Walker K.A.
, Schlosser P.
, Selvin E.
, Chatterjee N.
, et al.
.
Source: Medrxiv : The Preprint Server For Health Sciences, 2024-07-12 00:00:00.0; , .
EPub date: 2024-07-12 00:00:00.0.
PMID: 39040172
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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.
, García-Closas M.
, Almeida J.S.
.
Source: Jamia Open, 2024 Jul; 7(2), p. ooae055.
EPub date: 2024-06-27 00:00:00.0.
PMID: 38938691
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Joint Modeling of Gene-Environment Correlations and Interactions Using Polygenic Risk Scores in Case-Control Studies.
Authors: Wang Z.
, Shi W.
, Carroll R.J.
, Chatterjee N.
.
Source: American Journal Of Epidemiology, 2024-05-29 00:00:00.0; , .
EPub date: 2024-05-29 00:00:00.0.
PMID: 38806447
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MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.
Authors: Jin J.
, Zhan J.
, Zhang J.
, Zhao R.
, O'Connell J.
, Jiang Y.
, 23andMe Research Team
, Buyske S.
, Gignoux C.
, Haiman C.
, et al.
.
Source: Cell Genomics, 2024-04-10 00:00:00.0; 4(4), p. 100539.
PMID: 38604127
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Genetic Risk, Health-Associated Lifestyle, and Risk of Early-onset Total Cancer and Breast Cancer.
Authors: Zhang Y.
, Lindström S.
, Kraft P.
, Liu Y.
.
Source: Medrxiv : The Preprint Server For Health Sciences, 2024-04-06 00:00:00.0; , .
EPub date: 2024-04-06 00:00:00.0.
PMID: 38633776
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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 00:00:00.0.
PMID: 37620596
Related Citations
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.
, García-Closas M.
, Almeida J.S.
.
Source: Arxiv, 2023-10-13 00:00:00.0; , .
EPub date: 2023-10-13 00:00:00.0.
PMID: 37873020
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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 00:00:00.0; 7(1), p. 39.
EPub date: 2023-04-22 00:00:00.0.
PMID: 37087533
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Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies.
Authors: Wang Z.
, Shi W.
, Carroll R.J.
, Chatterjee N.
.
Source: Biorxiv : The Preprint Server For Biology, 2023-02-15 00:00:00.0; , .
EPub date: 2023-02-15 00:00:00.0.
PMID: 36824704
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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, 2022-11-07 00:00:00.0; , .
EPub date: 2022-11-07 00:00:00.0.
PMID: 36343017
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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 00:00:00.0.
PMID: 32245787
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