Validation and extension of the PREMM1,2 model in a population- based cohort of colorectal cancer patients

F Balaguer, J Balmana, S Castellvi-Bel, Ewout Steyerberg, M Andreu, X Llor, R Jover, S Syngal, A Castells

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53 Citations (Scopus)


Background & Aims: Early recognition of patients at risk for Lynch syndrome is critical but often difficult. Recently, a predictive algorithm-the PREMM1,2 model-has been developed to quantify the risk of carrying a germline mutation in the mismatch repair (MMR) genes MLH1 and MSH2. However, the model's performance in an unselected, population-based colorectal cancer population as well as its performance in combination with tumor MMR testing are unknown. Methods: We included all colorectal cancer cases from the EPICOLON study, a prospective, multicenter, population-based cohort (n = 1222). All patients underwent tumor microsatellite instability analysis and immunostaining for MLH1 and MSH2, and those with MMR deficiency (n = 91) underwent tumor BRAF V600E mutation analysis and MLH1/MSH2 germline testing. Results: The PREMM1,2 model with a 5% cut-off had a sensitivity, specificity, and positive predictive value (PPV) of 100%, 68%, and 2%, respectively. The use of a higher PREMM1,2 cut-off provided a higher specificity and PPV, at expense of a lower sensitivity. The combination of a :5% cut-off with tumor MMR testing maintained 100% sensitivity with an increased specificity (97%) and PPV (21%). The PPV of a PREMM1,2 score >= 20% alone (16%) approached the PPV obtained with PREMM1,2 score :5% combined with tumor MMR testing. In addition, a PREMM1,2 score of <5% was associated with a high likelihood of a BRAF V600E mutation. Conclusions: The PREMM1,2 model is useful to identify MLH1/MSH2 mutation carriers among unselected colorectal cancer patients. Quantitative assessment of the genetic risk might be useful to decide on subsequent tumor MMR and germline testing.
Original languageUndefined/Unknown
Pages (from-to)39-46
Number of pages8
Issue number1
Publication statusPublished - 2008

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  • EMC NIHES-02-65-01

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