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. 2025 Nov 15;157(10):2071-2080.
doi: 10.1002/ijc.70041. Epub 2025 Jul 14.

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

Affiliations

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

Guochong Jia et al. Int J Cancer. .

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 × 10-4). 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.

Keywords: biomarker; breast cancer; multi‐ancestry; plasma protein.

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Conflict of interest statement

Olufunmilayo I. Olopade (O.I.O) has potential personal conflict of interests involving affiliations or financial relationships with CancerIQ and TempusAI. Atara Ntekim (A.N) received research support from Roche. Other authors declare no competing interests related to this study. Michael F. Press (M.F.P) has potential personal conflict of interests involving affiliations or financial relationships with AstraZeneca, Inc., Eli Lilly & Company, Novartis Pharmaceuticals Corp., Zymeworks, Inc., and TORL Biotherapeutics.

Figures

Figure 1.
Figure 1.
Study design overview for protein-coding gene expression prediction and association analysis in breast cancer

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