Protein profiling in pathology: Analysis and evaluation of 239 frozen tissue biopsies for diagnosis of B‐cell lymphomas

Abstract

We determined the potential value of protein profiling of tissue samples by assessing how precise this approach enables discrimination of B-cell lymphoma from reactive lymph nodes, and how well the profiles can be used for lymphoma classification. Protein lysates from lymph nodes (n=239) from patients with the diagnosis of reactive hyperplasia (n=44), follicular lymphoma (n=63), diffuse large B-cell lymphoma (n=43), mantle cell lymphoma (n=47), and chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma (n=42) were analysed by SELDI-TOF MS. Data analysis was performed by (i) classification and regression tree-based analysis and (ii) binary and polytomous logistic regression analysis. After internal validation by the leave-one-out principle, both the classification and regression tree and logistic regression classification correctly identified the majority of the malignant (87 and 96%, respectively) and benign cases (73 and 75%, respectively). Classification was less successful since approximately one-third of the cases of each group were misclassified according to the histological classification. However, an additional mantle cell lymphoma case that was misclassified as chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma initially was identified based on the protein profile. SELDI-TOF MS protein profiling allows for reliable identification of the majority of malignant lymphoma cases; however, further validation and testing robustness in a diagnostic setting is needed.