A novel logistic model based on clinicopathological features predicts microsatellite instability in colorectal carcinomas

Anna Colomer1, Nadina Erill1, August Vidal2, Miquel Calvo3, Ruth Román1, Montse Verdú2, Carlos Cordon-Cardo4, Xavier Puig 1,2.

1BIOPAT, Grup Assistència, Barcelona; 2HISTOPAT Laboratoris, Barcelona; 3Statistics Department of the Universitat de Barcelona, Barcelona and 4Division of Molecular Pathology, Memorial Sloan-Kettering Cancer Center, New York.


High-frequency microsatellite instability has been reported to be associated with good prognosis in colorectal adenocarcinoma. However, methods to assess microsatellite instability (MIN) are based on genetic assays, and are not ideally suited to most histopathology laboratories. The aim of the present study was to develop a model for prediction of MIN status in colorectal cancer based on phenotypic characteristics. Clinicopathological features of a cohort of 204 patients with primary colon cancer were retrospectively reviewed following predetermined criteria. Genetic assessment of MIN status was performed on DNA extracted from sections of formalin-fixed, paraffin-embedded specimens by testing a panel of 11 microsatellite markers. Logistic regression analysis generated a mathematical tool capable of identifying colorectal tumors displaying MIN status with a sensitivity of 77.8% and a specificity of 96.8%. Features associated with instability included the proximal location of the lesions, occurrence of solid and/or mucinous differentiation, absence of cribriform structures, presence of peritumoral Crohn-like reaction, expansive growth pattern, high Ki67 proliferative index, and p53 negative phenotype. This approach predicts microsatellite instability in colorectal carcinoma with an overall assigned accuracy of 95.1% and a negative predictive value of 97.8%. Implementation of this tool to routine histopathological studies could improve the management of patients with colorectal cancer, especially those presenting with stage II and III of the disease. It will also assist in identifying a subset of patients likely to benefit from adjuvant chemotherapy.

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Diagn Mol Pathol 2005; 14: 213-223