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Until recently, no prediction models for Lynch syndrome (LS) had been validated for  PMS2  mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for  PMS2  mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for LS according to MMRpredict and PREMM5. The area under the operator receiving characteristic curve (AUC) was compared between MMRpredict and PREMM5 for LS patients in general and for different LS genes specifically. Of 734 index patients, 83 (11%) were diagnosed with LS; 23  MLH1 , 17  MSH2 , 31  MSH6  and 12  PMS2  mutation carriers. Both prediction models performed well for  MLH1  and  MSH2  (AUC 0.80 and 0.83 for PREMM5 and 0.79 for MMRpredict) and fair for  MSH6  mutation carriers (0.69 for PREMM5 and 0.66 for MMRpredict). MMRpredict performed fair for  PMS2  mutation carriers (AUC 0.72), while PREMM5 failed to discriminate  PMS2  mutation carriers from non-mutation carriers (AUC 0.51). The only statistically significant difference between  PMS2  mutation carriers and non-mutation carriers was proximal location of colorectal cancer (77 vs. 28%, p < 0.001). Adding location of colorectal cancer to PREMM5 considerably improved the models performance for  PMS2  mutation carriers (AUC 0.77) and overall (AUC 0.81 vs. 0.72). We validated these results in an external cohort of 376 colorectal cancer patients, including 158 LS patients. MMRpredict and PREMM5 cannot adequately identify  PMS2  mutation carriers. Adding location of colorectal cancer to PREMM5 may improve the performance of this model, which should be validated in larger cohorts.   Electronic supplementary material  The online version of this article (doi:10.1007/s10689-017-0039-1) contains supplementary material, which is available to authorized users.

Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers