A Novel Logistic Model Based on Clinicopathological Features Predicts Microsatellite Instability in Colorectal Carcinomas

Abstract

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.

Microsatellite instability of the colorectal carcinoma can be predicted in the conventional pathologic examination. A prospective multicentric study and the statistical analysis of 615 cases consolidate our previously proposed logistic regression model

“…Our previous experience with the logistic regression model [15] capable to predict very accurately MSI instability impels us to assay again this modeling approach with the multicenter data set. Our purpose is to obtain a mathematical expression in order to estimate the probability of a tumor exhibiting MSI-H according to the following equation:…”

“…The global series employed for the improvement of our prediction model included the 265 external cases from our validation series, another 146 from the prospective series evaluated in our institution, plus the 204 cases from our first study [15]. A summary of the histopathological variables of our global series is displayed on Table 1.…”

“…Immunohistochemical analysis was also carried out at each participating center according to their own routine protocols in order to preserve interobserver heterogeneity. Histopathologic features were recorded as previously described [15]. Briefly such features included categorical and numerical variables.…”

“…MSI status was evaluated as described previously [15], using a panel of 11 microsatellites composed by the five microsatellites from the NCI panel (BAT25, BAT26, D5S346, D2S123, and D17S250) [18] in a multiplex PCR reaction, five additional microsatellites originally aimed at detecting the LOH status of chromosome 18q (D18S55, D18S58, D18S61, D18S64, and D18S69) [19] also amplified in a multiplex reaction and a microsatellite at the TP53 locus on 17p (P53CA) [20]. Fluorescent amplicons were analyzed on an automated ABI PRISM® 310 Genetic Analyzer using the GeneScan software (PE Applied Biosystems).…”

“…Also lately another model has been published by Greenson and colleagues [14] which classifies MSI-H tumors with an 85% accuracy looking at pathologic features. The objective of our work was to validate and improve with a multicentric study a logistic model based on clinicopathological features previously designed in our laboratories to predict microsatellite instability [15]. We used a nonselected population of 615 colorectal carcinomas to provide pathologists with an easy to use tool to identify a large subset of carcinomas which do not present a MSI-H status and would need no MIN analysis.…”