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J Clin Oncol. 2017 Jul 1;35(19):2165-2172. doi: 10.1200/JCO.2016.69.6120. Epub 2017 May 10.

Development and Validation of the PREMM5 Model for Comprehensive Risk Assessment of Lynch Syndrome.

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Fay Kastrinos and Ashley McFarland, Columbia University Medical Center, New York, NY; Hajime Uno, Chinedu Ukaegbu, Matthew B. Yurgelun, Matthew H. Kulke, Deborah Schrag, Jeffrey A. Meyerhardt, Charles S. Fuchs, Robert J. Mayer, Kimmie Ng, and Sapna Syngal, Dana-Farber Cancer Institute; Carmelita Alvero, Harvard T.H. Chan School of Public Health; Matthew B. Yurgelun, Matthew H. Kulke, Deborah Schrag, Jeffrey A. Meyerhardt, Charles S. Fuchs, Robert J. Mayer, Kimmie Ng, and Sapna Syngal, Harvard Medical School; Sapna Syngal, Brigham and Women's Hospital, Boston, MA; and Ewout W. Steyerberg, University Medical Center Rotterdam, Rotterdam, the Netherlands.


Purpose Current Lynch syndrome (LS) prediction models quantify the risk to an individual of carrying a pathogenic germline mutation in three mismatch repair (MMR) genes: MLH1, MSH2, and MSH6. We developed a new prediction model, PREMM5, that incorporates the genes PMS2 and EPCAM to provide comprehensive LS risk assessment. Patients and Methods PREMM5 was developed to predict the likelihood of a mutation in any of the LS genes by using polytomous logistic regression analysis of clinical and germline data from 18,734 individuals who were tested for all five genes. Predictors of mutation status included sex, age at genetic testing, and proband and family cancer histories. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), and clinical impact was determined by decision curve analysis; comparisons were made to the existing PREMM1,2,6 model. External validation of PREMM5 was performed in a clinic-based cohort of 1,058 patients with colorectal cancer. Results Pathogenic mutations were detected in 1,000 (5%) of 18,734 patients in the development cohort; mutations included MLH1 (n = 306), MSH2 (n = 354), MSH6 (n = 177), PMS2 (n = 141), and EPCAM (n = 22). PREMM5 distinguished carriers from noncarriers with an AUC of 0.81 (95% CI, 0.79 to 0.82), and performance was similar in the validation cohort (AUC, 0.83; 95% CI, 0.75 to 0.92). Prediction was more difficult for PMS2 mutations (AUC, 0.64; 95% CI, 0.60 to 0.68) than for other genes. Performance characteristics of PREMM5 exceeded those of PREMM1,2,6. Decision curve analysis supported germline LS testing for PREMM5 scores ≥ 2.5%. Conclusion PREMM5 provides comprehensive risk estimation of all five LS genes and supports LS genetic testing for individuals with scores ≥ 2.5%. At this threshold, PREMM5 provides performance that is superior to the existing PREMM1,2,6 model in the identification of carriers of LS, including those with weaker phenotypes and individuals unaffected by cancer.

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