Efficient and reproducible identification of mismatch repair deficient colon cancer: validation of the MMR index and comparison with other predictive models
1 Department of Pathology and Cytology, Helsingborg General Hospital, Helsingborg, Södra Vallgatan 5 SE-251 87, Helsingborg, Sweden
2 Department of Pathology, Skane University Hospital, Clinical Science, Lund University, SE-221 85 Lund, Sweden
3 Department of Oncology, Institute of Clinical Sciences, Lund University, SE-221 85 Lund, Sweden
4 Aleris Medilab, Nytorpsvägen 28-32, SE-183 53 Täby, Sweden
5 Clinical Research Centre, Hvidovre University Hospital, Copenhagen University, Kettegaards Allé 30, DK-2650 Hvidovre, Denmark
BMC Clinical Pathology 2013, 13:33 doi:10.1186/1472-6890-13-33Published: 17 December 2013
The identification of mismatch-repair (MMR) defective colon cancer is clinically relevant for diagnostic, prognostic and potentially also for treatment predictive purposes. Preselection of tumors for MMR analysis can be obtained with predictive models, which need to demonstrate ease of application and favorable reproducibility.
We validated the MMR index for the identification of prognostically favorable MMR deficient colon cancers and compared performance to 5 other prediction models. In total, 474 colon cancers diagnosed ≥ age 50 were evaluated with correlation between clinicopathologic variables and immunohistochemical MMR protein expression.
Female sex, age ≥60 years, proximal tumor location, expanding growth pattern, lack of dirty necrosis, mucinous differentiation and presence of tumor-infiltrating lymphocytes significantly correlated with MMR deficiency. Presence of at least 4 of the MMR index factors identified MMR deficient tumors with 93% sensitivity and 76% specificity and showed favorable reproducibility with a kappa value of 0.88. The MMR index also performed favorably when compared to 5 other predictive models.
The MMR index is easy to apply and efficiently identifies MMR defective colon cancers with high sensitivity and specificity. The model shows stable performance with low inter-observer variability and favorable performance when compared to other MMR predictive models.