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Grant Details

Grant Number: 1R01CA246592-01A1 Interpret this number
Primary Investigator: Toriola, Adetunji
Organization: Washington University
Project Title: Metabolite Profiles and Mammographic Density in Premenopausal Women
Fiscal Year: 2020


Abstract

ABSTRACT In 2019, approximately 268,600 women in the US will be diagnosed with breast cancer, making it the leading cause of cancer in women. About 25% of these cases occurred in premenopausal women. The annual incidence of breast cancer among women under age 50 has been increasing slowly since the mid-1990s, in contrast to what is observed in women aged over 50 years, where the incidence has remained stable over time. This persistent increase in the incidence among younger women indicates that new approaches to primary prevention in premenopausal women are needed. Mammographic breast density is one of the strongest risk factors for breast cancer, especially in premenopausal women, where an estimated 39% of breast cancer cases is attributable to having dense breasts. A decrease in breast density leads to a reduction in breast cancer incidence. Nevertheless, the molecular basis of mammographic breast density and the mechanisms through which dense breast increases breast cancer risk are poorly understood. A greater understanding of these mechanisms is crucial, and will uncover new biological pathways and actionable biomarkers that can be targeted to prevent breast cancer development. Metabolomics is a promising tool to provide novel insights into disease etiology, biological mechanisms, and pathways. Metabolomics has, however, not been applied to study mammographic breast density. Our pilot analyses show differences in metabolite levels between women with fatty breasts compared to women with dense breasts. Building on these novel findings, we will apply state-of-the art metabolomics platforms to 1) investigate the metabolome of mammographic breast density in premenopausal women; 2) quantify the variation in mammographic breast density explained by the metabolome; 3) determine whether the metabolome of mammographic breast density predicts breast cancer development in premenopausal women. Our overarching hypothesis is that we will leverage metabolomics to uncover the molecular mechanisms, biological pathways, as well as novel actionable biomarkers that are associated with mammographic breast density in premenopausal women. Our study population will consist of premenopausal women recruited during annual screening mammogram at the Joanne Knight Breast Health Center at the Washington University School of Medicine, St. Louis, MO. Mammographic breast density is quantitatively assessed using Volpara. Fasting blood samples are collected on the same day the women have their mammograms. The women also complete a questionnaire with detailed information on breast cancer risk factors and determinants of mammographic density. In conclusion, we will build on our exciting preliminary data to uncover novel, actionable biomarkers associated with mammographic breast density, and also breast cancer development in premenopausal women. These biomarkers could be targeted in breast cancer prevention in future studies.



Publications

Coffee, Tea, and Mammographic Breast Density in Premenopausal Women.
Authors: Odama A.M. , Otti V. , Xu S. , Adebayo O. , Toriola A.T. .
Source: Nutrients, 2021-10-28; 13(11), .
EPub date: 2021-10-28.
PMID: 34836118
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Reply to Hopper, Nguyen, and Li.
Authors: Salazar A.S. , Toriola A.T. .
Source: JNCI cancer spectrum, 2021 Aug; 5(4), p. pkab052.
EPub date: 2021-06-14.
PMID: 34377933
Related Citations

Refining the Focus on Early Life and Adolescent Pathways to Prevent Breast Cancer.
Authors: Colditz G.A. , Toriola A.T. .
Source: Journal of the National Cancer Institute, 2021-06-01; 113(6), p. 658-659.
PMID: 33136152
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Chemoprevention Agents to Reduce Mammographic Breast Density in Premenopausal Women: A Systematic Review of Clinical Trials.
Authors: Salazar A.S. , Rakhmankulova M. , Simon L.E. , Toriola A.T. .
Source: JNCI cancer spectrum, 2021 Feb; 5(1), p. pkaa125.
EPub date: 2021-01-22.
PMID: 33554041
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