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Fam Cancer. 2018 Jul;17(3):361-370. doi: 10.1007/s10689-017-0039-1.

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

Author information

1
Department of Clinical Genetics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
2
Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
3
Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
4
Department of Pathology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
5
Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.
6
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
7
Department of Clinical Genetics, Erasmus MC, University Medical Center, Rotterdam, The Netherlands. a.wagner@erasmusmc.nl.
8
Department of Clinical Genetics, Erasmus MC, University Medical Center, Room Ee-2018, P. O. Box 2040, 3000 CA, Rotterdam, The Netherlands. a.wagner@erasmusmc.nl.

Abstract

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.

KEYWORDS:

Colorectal cancer; Hereditary cancer; Lynch syndrome; Prediction models

PMID:
28933000
PMCID:
PMC5999171
DOI:
10.1007/s10689-017-0039-1
[Indexed for MEDLINE]
Free PMC Article

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