Building an outcome predictor model for diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) patients are treated using relatively homogeneous protocols, irrespective of their biological and clinical variability. Here we have developed a protein-expression-based outcome predictor for DLBCL. Using tissue microarrays (TMAs), we have analyzed the expression of 52 selected molecules in a series of 152 DLBCLs. The study yielded relevant information concerning key biological aspects of this tumor, such as cell-cycle control and apoptosis. A biological predictor was built with a training group of 103 patients, and was validated with a blind set of 49 patients. The predictive model with 8 markers can identify the probability of failure for a given patient with 78% accuracy. After stratifying patients according to the predicted response under the logistic model, 92.3% patients below the 25 percentile were accurately predicted by this biological score as "failure-free" while 96.2% of those above the 75 percentile were correctly predicted as belonging to the "fatal or refractory disease" group. Combining this biological score and the International Prognostic Index (IPI) improves the capacity for predicting failure and survival. This predictor was then validated in the independent group. The protein-expression-based score complements the information obtained from the use of the IPI, allowing patients to be assigned to different risk categories.