Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression

Abstract

Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the PREMM1,2,6 and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for PREMM1,2,6. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis.

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Acknowledgments

This work was in part funded though Liga Portuguesa Contra o Cancro-Núcleo Regional do Norte (Research Scholarships to P. P. and M. P.).

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Correspondence to Manuel R. Teixeira.

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Cabreira, V., Pinto, C., Pinheiro, M. et al. Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression. Familial Cancer 16, 73–81 (2017). https://doi.org/10.1007/s10689-016-9926-0

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Keywords

  • Lynch syndrome
  • MLH1
  • Lynch syndrome predictive models
  • Immunohistochemistry