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Background <p>Prostate cancer (PCa) is heterogeneous, making risk stratification essential for clinical care. Although polygenic risk scores (PRSs) with main effects of single-nucleotide polymorphisms (SNPs) can help identify individuals at high risk before biological and clinical onset, a PRS for predicting PCa aggressiveness remains underdeveloped. The <i>KLK3</i>, which encodes prostate-specific antigen (PSA), is linked to PCa aggressiveness. Recent findings on <i>KLK3</i> SNP-SNP interactions show promise for predicting PCa aggressiveness. The objective of this study is to develop a PRS (PRS-KLK3int) by examining <i>KLK3</i> SNP-SNP interaction pairs.</p> Methods <p>The PRS-KLK3int was developed based on a discovery set (10,836 PCa patients) and two validation sets with 14,348 and 16,584 patients of European ancestry. A total of 3145 SNP pairs and two published PRSs were evaluated.</p> Results <p>This study developed a PRS-KLK3int with 284 SNPs, combining an existing PRS with 270 SNPs and 12 SNP-SNP interaction pairs with 15 SNPs (one overlapped). All these 12 pairs were involved with at least one SNP from <i>KLK3</i>. The PRS-KLK3int outperformed two existing PRSs in predicting PCa aggressiveness (p-values: 3.5×10<sup>−18</sup>, 9×10<sup>−14</sup>, and 1.7×10<sup>−20</sup> for the three sets). It effectively distinguished high-risk from low-risk groups across all datasets. The top 1% high-risk group had a higher prevalence of PCa aggressiveness than the middle 50% group (45.5% vs. 25.9%, OR = 2.38, p = 2.2×10<sup>−5</sup>) in the discovery set, and similar results were observed in validation sets (OR = 2.56, p = 4.3×10<sup>−6</sup>; OR = 2.07, p = 2.1×10<sup>−5</sup>).</p> Conclusions <p>These findings support PRS-KLK3int as a valuable tool for PCa severity stratification, especially in identifying extremely high-risk PCa patients.</p>

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Polygenic risk score with KLK3 SNP-SNP interaction pairs for predicting prostate cancer aggressiveness

  • 期刊论文

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  • 发表日期：

    2026年5月28日

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  • Hui-Yi Lin,
  • Indrani Sarkar,
  • Harun Mazumder,
  • Po-Yu Huang,
  • Kenneth R. Muir,
  • Johanna Schleutker,
  • Nora Pashayan,
  • Jyotsna Batra,
  • David E. Neal,
  • Henrik Grönberg,
  • Sune F. Nielsen,
  • Børge G. Nordestgaard,
  • Catherine M. Tangen,
  • Robert J. MacInnis,
  • Alicja Wolk,
  • Demetrius Albanes,
  • Ruth C. Travis,
  • Janet L. Stanford,
  • Lorelei A. Mucci,
  • Adam S. Kibel,
  • Olivier Cussenot,
  • Sonja I. Berndt,
  • Stella Koutros,
  • Karina Dalsgaard Sørensen,
  • Cezary Cybulski,
  • Eli Marie Grindedal,
  • Josef Hoegel,
  • Christiane Maier,
  • Robert J. Hamilton,
  • Barry S. Rosenstein,
  • Ana Vega,
  • Manolis Kogevinas,
  • Fredrik Wiklund,
  • Kathryn L. Penney,
  • Manuel R. Teixeira,
  • Hermann Brenner,
  • Esther M. John,
  • Radka Kaneva,
  • Christopher J. Logothetis,
  • Susan L. Neuhausen,
  • Kim De Ruyck,
  • Piet Ost,
  • Marija Gamulin,
  • Nawaid Usmani,
  • Frank Claessens,
  • Jose Esteban Castelao,
  • Paul A. Townsend,
  • Zsofia Kote-Jarai,
  • Christopher A. Haiman,
  • Rosalind A. Eeles,
  • Kim De Ruyck,
  • Jong Y. Park

摘要

Background

Prostate cancer (PCa) is heterogeneous, making risk stratification essential for clinical care. Although polygenic risk scores (PRSs) with main effects of single-nucleotide polymorphisms (SNPs) can help identify individuals at high risk before biological and clinical onset, a PRS for predicting PCa aggressiveness remains underdeveloped. The KLK3, which encodes prostate-specific antigen (PSA), is linked to PCa aggressiveness. Recent findings on KLK3 SNP-SNP interactions show promise for predicting PCa aggressiveness. The objective of this study is to develop a PRS (PRS-KLK3int) by examining KLK3 SNP-SNP interaction pairs.

Methods

The PRS-KLK3int was developed based on a discovery set (10,836 PCa patients) and two validation sets with 14,348 and 16,584 patients of European ancestry. A total of 3145 SNP pairs and two published PRSs were evaluated.

Results

This study developed a PRS-KLK3int with 284 SNPs, combining an existing PRS with 270 SNPs and 12 SNP-SNP interaction pairs with 15 SNPs (one overlapped). All these 12 pairs were involved with at least one SNP from KLK3. The PRS-KLK3int outperformed two existing PRSs in predicting PCa aggressiveness (p-values: 3.5×10−18, 9×10−14, and 1.7×10−20 for the three sets). It effectively distinguished high-risk from low-risk groups across all datasets. The top 1% high-risk group had a higher prevalence of PCa aggressiveness than the middle 50% group (45.5% vs. 25.9%, OR = 2.38, p = 2.2×10−5) in the discovery set, and similar results were observed in validation sets (OR = 2.56, p = 4.3×10−6; OR = 2.07, p = 2.1×10−5).

Conclusions

These findings support PRS-KLK3int as a valuable tool for PCa severity stratification, especially in identifying extremely high-risk PCa patients.