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<p>Polygenic risk score (PRS) models effectively predict breast cancer (BC) risk in European-ancestry women but have limited accuracy for African-ancestry women, particularly for aggressive subtypes. We developed PRS models for overall BC, estrogen receptor (ER)-positive, ER-negative and triple-negative BC (TNBC) in African-ancestry women using data from the African Ancestry Breast Cancer Genetics consortium (17,391 cases and 18,800 controls). We applied several PRS methods and integrated information across ancestries and BC subtypes. The best models for overall, ER-positive, ER-negative and TNBC showed an area under the receiving operating curve of 0.612, 0.621, 0.611 and 0.639, respectively, and maintained predictive accuracy in external validation studies with area under the receiving operating curves of 0.612, 0.640, 0.605 and 0.652. We further introduce a parsimonious 162-variant PRS for TNBC with comparable accuracy (0.626). These findings demonstrate markedly improved PRS accuracy for BC risk prediction in African-ancestry women. Using these PRS models for screening will help promote more equitable cancer prevention efforts.</p>

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Improved polygenic risk prediction models for breast cancer subtypes in women of African ancestry

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • James L. Li,
  • Haoyu Zhang,
  • Xiaoyu Wang,
  • Guochong Jia,
  • Julian C. McClellan,
  • Wenji Guo,
  • Yijia Sun,
  • Peter N. Fiorica,
  • Stefan Ambs,
  • Mollie E. Barnard,
  • Yu Chen,
  • Montserrat Garcia-Closas,
  • Jian Gu,
  • Jennifer J. Hu,
  • Esther M. John,
  • Katherine L. Nathanson,
  • Barbara Nemesure,
  • Tuya Pal,
  • Xiao-Ou Shu,
  • Michael F. Press,
  • Maureen Sanderson,
  • Dale P. Sandler,
  • Melissa A. Troester,
  • Song Yao,
  • Jirong Long,
  • Thomas U. Ahearn,
  • Abenaa M. Brewster,
  • Adeyinka Falusi,
  • Peter Kraft,
  • Anselm J. M. Hennis,
  • Timothy Makumbi,
  • Berthe S. E. Mapoko,
  • Katie M. O’Brien,
  • Oladosu Ojengbede,
  • Andrew F. Olshan,
  • Sonya Reid,
  • Gary Zirpoli,
  • Qiuyin Cai,
  • Eboneé N. Butler,
  • Maosheng Huang,
  • John Obafunwa,
  • Clarice R. Weinberg,
  • Christine Ambrosone,
  • Jie Ping,
  • Ran Tao,
  • Bingshan Li,
  • Xingyi Guo,
  • Guimin Gao,
  • David V. Conti,
  • Nilanjan Chatterjee,
  • Julie R. Palmer,
  • Olufunmilayo I. Olopade,
  • Wei Zheng,
  • Christopher A. Haiman,
  • Dezheng Huo

摘要

Polygenic risk score (PRS) models effectively predict breast cancer (BC) risk in European-ancestry women but have limited accuracy for African-ancestry women, particularly for aggressive subtypes. We developed PRS models for overall BC, estrogen receptor (ER)-positive, ER-negative and triple-negative BC (TNBC) in African-ancestry women using data from the African Ancestry Breast Cancer Genetics consortium (17,391 cases and 18,800 controls). We applied several PRS methods and integrated information across ancestries and BC subtypes. The best models for overall, ER-positive, ER-negative and TNBC showed an area under the receiving operating curve of 0.612, 0.621, 0.611 and 0.639, respectively, and maintained predictive accuracy in external validation studies with area under the receiving operating curves of 0.612, 0.640, 0.605 and 0.652. We further introduce a parsimonious 162-variant PRS for TNBC with comparable accuracy (0.626). These findings demonstrate markedly improved PRS accuracy for BC risk prediction in African-ancestry women. Using these PRS models for screening will help promote more equitable cancer prevention efforts.