Integrating multi‐ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci

Genome-wide association studies (GWAS) have identified more than 200 risk loci for breast cancer. However, target genes and their encoded proteins in these loci remain largely unknown. In this study, we utilized genetic prediction models for 1349 circulating proteins derived from individuals of African (n = 1871) and European (n = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (n = 40,138), Asian (n = 137,677), and European (n = 247,173) ancestry. We identified 51 blood protein biomarkers associated with breast cancer risk, overall or by subtypes, at a false discovery rate (FDR) < 0.05, including 27 proteins encoded by genes located at least 1 Mb away from any of the known risk loci identified in GWAS. Of them, 32 proteins showed significant associations with breast cancer risk at the Bonferroni-corrected significance level (p < 2.45 × 10<sup>-4</sup>). Of the 24 proteins located at GWAS-identified risk loci, associations for 14 proteins were significantly attenuated after adjustment for the index risk variant of each respective locus, suggesting that these proteins may be target proteins for the risk loci. Encoding gene expression levels in normal breast tissue could be genetically predicted for 23 of the 51 identified proteins, and 13 encoding genes were associated with breast cancer risk in the same direction (p < .05). Our study identified potential protein targets of GWAS risk loci and biomarkers for breast cancer risk and provided additional insights into breast cancer genetics and etiology.

Authors

Institutions

Publication Details

Journal
International Journal of Cancer
Published
2025-07-14
DOI
https://doi.org/10.1002/ijc.70041
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating multi‐ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci

Maureen Sanderson, Sun‐Seog Kweon, Heather M. Munro, Atara Ntekim et al.
International Journal of Cancer
Genetic Associations and Epidemiology
article

Integrating multi‐ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci

Maureen Sanderson, Sun‐Seog Kweon, Heather M. Munro, Atara Ntekim, Dezheng Huo, Abenaa M. Brewster, Siew‐Kee Low, Motoki Iwasaki, Eboneé N. Butler, Stefan Ambs, Bingshan Li, Koichi Matsuda, Song Yao, Andrew F. Olshan, Sonya Reid, Christine B. Ambrosone, Tuya Pal, Hidemi Ito, Jie Ping, Thomas U. Ahearn, Paul Ndom, Shuai Xu, Taiki Yamaji, Esther M. John, Jian Gu, Jirong Long, Daehee Kang, Zheng Ying, Gary Zirpoli, Olufunmilayo I. Olopade, Katie M. O’Brien, Michael F. Press, Dale P. Sandler, Anselm Hennis, Barbara Nemesure, Christopher A. Haiman, Melissa A. Troester, Mollie E. Barnard, Prisca Olabisi Adejumo, Keitaro Matsuo, Timothy Makumbi, Boyoung Park, Jennifer J. Hu, Ji‐Yeob Choi, Katherine L. Nathanson, Mojisola Morenike Oluwasanu, Montserrat García‐Closas, Yu‐Tang Gao, Xiao‐Ou Shu, Xingyi Guo, Dong‐Young Noh, Sue K. Park, Guochong Jia, Lili Liu, Ran Tao, Wei Zheng, Yu Chen, Min‐Ho Park, Eun Sook Lee, Maosheng Huang, Julie R. Palmer, Michiaki Kubo, Clarice Ring Weinberg
article en

Abstract

Genome-wide association studies (GWAS) have identified more than 200 risk loci for breast cancer. However, target genes and their encoded proteins in these loci remain largely unknown. In this study, we utilized genetic prediction models for 1349 circulating proteins derived from individuals of African (n = 1871) and European (n = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (n = 40,138), Asian (n = 137,677), and European (n = 247,173) ancestry. We identified 51 blood protein biomarkers associated with breast cancer risk, overall or by subtypes, at a false discovery rate (FDR) < 0.05, including 27 proteins encoded by genes located at least 1 Mb away from any of the known risk loci identified in GWAS. Of them, 32 proteins showed significant associations with breast cancer risk at the Bonferroni-corrected significance level (p < 2.45 × 10<sup>-4</sup>). Of the 24 proteins located at GWAS-identified risk loci, associations for 14 proteins were significantly attenuated after adjustment for the index risk variant of each respective locus, suggesting that these proteins may be target proteins for the risk loci. Encoding gene expression levels in normal breast tissue could be genetically predicted for 23 of the 51 identified proteins, and 13 encoding genes were associated with breast cancer risk in the same direction (p < .05). Our study identified potential protein targets of GWAS risk loci and biomarkers for breast cancer risk and provided additional insights into breast cancer genetics and etiology.

International Journal of CancerVol. 157(10)
Boston University (US), Chonnam National University (KR), University of North Carolina at Chapel Hill (US), Meharry Medical College (US), Roswell Park Comprehensive Cancer Center (US), The University of Texas MD Anderson Cancer Center (US), Seoul National University (KR), University of Miami (US), Cancer Research Institute (US), Chicago Department of Public Health (US), University of Ibadan (NG), Vanderbilt University (US), Keck Hospital of USC (US), University College Hospital, Ibadan (NG), Mulago Hospital (UG), Seoul National University Hospital (KR), University of Chicago (US), Stony Brook School (US), National Cancer Center (US), Chonnam National University Hospital (KR), National Institute of Environmental Health Sciences (US), Stanford Health Care (US), USC Norris Comprehensive Cancer Center (US), National Cancer Center (KR), Aichi Cancer Center (JP), Stanford Medicine (US), State Key Laboratory of Oncogene and Related Genes (CN), National Cancer Institute (US), Chonnam National University Hwasun Hospital (KR), RIKEN Center for Integrative Medical Sciences (JP), Yaoundé General Hospital (CM), University of the West Indies (BB), Shanghai Municipal Center For Disease Control Prevention (CN), Division of Cancer Epidemiology and Genetics, Hanyang University (KR), New York University (US), Stony Brook University (US), Nagoya University (JP), The University of Tokyo (JP), University of Pennsylvania (US), Vanderbilt University Medical Center (US), Stanford University (US)
National Cancer Institute
Good health and well-being
Openalex Percentile: Top 80%
Genetic Associations and Epidemiology
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.