Grant Details
Grant Number: |
7U01CA249866-04 Interpret this number |
Primary Investigator: |
Chatterjee, Nilanjan |
Organization: |
Johns Hopkins University |
Project Title: |
Multifactoral Breast Cancer Risk Prediction Accounting for Ethnic and Tumor Diversity |
Fiscal Year: |
2023 |
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
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
Related Citations
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
Related Citations
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
Related Citations
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
Related Citations