Brent Mabey , Elisha Hughes , Matthew Kucera , Timothy Simmons , Brooke Hullinger , Holly J. Pederson , Lamis Yehia , Charis Eng , Judy Garber , Monique Gary , Ora Gordon , Jennifer R. Klemp , Semanti Mukherjee , Joseph Vijai , Kenneth Offit , Olufunmilayo I. Olopade , Sandhya Pruthi , Allison Kurian , Mark E. Robson , Pat W. Whitworth , Alexander Gutin
{"title":"将所有血统的多基因评分与传统风险因素相结合的临床乳腺癌风险评估工具的验证。","authors":"Brent Mabey , Elisha Hughes , Matthew Kucera , Timothy Simmons , Brooke Hullinger , Holly J. Pederson , Lamis Yehia , Charis Eng , Judy Garber , Monique Gary , Ora Gordon , Jennifer R. Klemp , Semanti Mukherjee , Joseph Vijai , Kenneth Offit , Olufunmilayo I. Olopade , Sandhya Pruthi , Allison Kurian , Mark E. Robson , Pat W. Whitworth , Alexander Gutin","doi":"10.1016/j.gim.2024.101128","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort.</p></div><div><h3>Methods</h3><p>This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs.</p></div><div><h3>Results</h3><p>Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC.</p></div><div><h3>Conclusion</h3><p>CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.</p></div>","PeriodicalId":12717,"journal":{"name":"Genetics in Medicine","volume":"26 7","pages":"Article 101128"},"PeriodicalIF":6.6000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1098360024000613/pdfft?md5=0062e60be32ca9cd24042faba911d08b&pid=1-s2.0-S1098360024000613-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors\",\"authors\":\"Brent Mabey , Elisha Hughes , Matthew Kucera , Timothy Simmons , Brooke Hullinger , Holly J. Pederson , Lamis Yehia , Charis Eng , Judy Garber , Monique Gary , Ora Gordon , Jennifer R. Klemp , Semanti Mukherjee , Joseph Vijai , Kenneth Offit , Olufunmilayo I. Olopade , Sandhya Pruthi , Allison Kurian , Mark E. Robson , Pat W. Whitworth , Alexander Gutin\",\"doi\":\"10.1016/j.gim.2024.101128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort.</p></div><div><h3>Methods</h3><p>This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs.</p></div><div><h3>Results</h3><p>Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC.</p></div><div><h3>Conclusion</h3><p>CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.</p></div>\",\"PeriodicalId\":12717,\"journal\":{\"name\":\"Genetics in Medicine\",\"volume\":\"26 7\",\"pages\":\"Article 101128\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1098360024000613/pdfft?md5=0062e60be32ca9cd24042faba911d08b&pid=1-s2.0-S1098360024000613-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genetics in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1098360024000613\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genetics in Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1098360024000613","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors
Purpose
We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort.
Methods
This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs.
Results
Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC.
Conclusion
CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.
期刊介绍:
Genetics in Medicine (GIM) is the official journal of the American College of Medical Genetics and Genomics. The journal''s mission is to enhance the knowledge, understanding, and practice of medical genetics and genomics through publications in clinical and laboratory genetics and genomics, including ethical, legal, and social issues as well as public health.
GIM encourages research that combats racism, includes diverse populations and is written by authors from diverse and underrepresented backgrounds.