Pathologic predictors of microsatellite instability in colorectal cancer
- PMID: 18830122
- PMCID: PMC3500028
- DOI: 10.1097/PAS.0b013e31817ec2b1
Pathologic predictors of microsatellite instability in colorectal cancer
Abstract
Identification of microsatellite unstable (MSI-H) colorectal cancers (CRCs) is important not only for the identification of hereditary nonpolyposis colorectal cancer syndrome but also because MSI-H CRCs have a better prognosis and may respond differently to 5-fluorouracil-based chemotherapy. We present 2 nearly equivalent logistic regression models for clinical use that predict microsatellite instability based on the review of 1649 CRCs from patients of all ages collected in a population-based case control study in northern Israel. One hundred ninety-eight of these 1649 tumors demonstrated a high degree of microsatellite instability (12%). Multivariate analysis found that >2 tumor-infiltrating lymphocyte (TIL) cells per high-powered field, the lack of dirty necrosis, the presence of a Crohn-like reaction, right-sided location, any mucinous differentiation (mucinous or focally mucinous) and well or poor differentiation, and age less than 50 were all independent predictors of MSI-H. We developed 2 logistic regression models that differ only by the statistical approach used to analyze the number of TIL cells per high-powered field, where the slightly more accurate (and complex) model uses the log of the total number of TIL cells. The simpler clinical model uses a cut-off of 2>TIL cells per high-powered field. The accuracy of both models is high, with an 85.4% versus 85.0% probability of correctly classifying tumors as MSI-H. By employing the simpler model, pathologists can predict the likelihood of microsatellite instability by compiling the MSI probability score (Table 4 and Fig. 1) from simple histologic and clinical data available during sign-out. Our model shows that approximately 43% of CRCs have a MSI probability score of 1 or less and hence have little likelihood (<3%) of being MSI-H. Although this model is not perfect in predicting microsatellite instability, its use could improve the efficiency of expensive diagnostic testing.
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