Multi-marker algorithms based on CXCL13, IL-10, sIL-2 receptor, and beta2-microglobulin in cerebrospinal fluid to diagnose CNS lymphoma.

Tumor biopsy is essential for the definitive diagnosis of central nervous system (CNS) lymphoma. However, the biopsy procedure carries the risk of complications such as bleeding, convulsions, and infection. Cerebrospinal fluid (CSF) beta2-microglobulin (beta2-MG), soluble IL-2 receptor (sIL-2R), and interleukin-10 (IL-10) are known to be useful diagnostic biomarkers for CNS lymphoma. The C-X-C motif chemokine ligand 13 (CXCL13) was recently reported to be another useful biomarker for CNS lymphoma. The purpose of this study is to establish a diagnostic algorithm that can avoid biopsy by combining these diagnostic biomarkers. In the first, we conducted a case-control study (n = 248) demonstrating that the CSF CXCL13 concentration was significantly increased in CNS lymphoma patients compared with various other brain diseases (AUC = 0.981). We established a multi-marker diagnostic model using CSF CXCL13, IL-10, beta2-MG, and sIL-2R from the results of the case-control study and then applied the model to a prospective study (n = 104) to evaluate its utility. The multi-marker diagnostic algorithms had excellent diagnostic performance: the sensitivity, specificity, positive predictive value, and negative predictive value were 97%, 97%, 94%, and 99%, respectively. In addition, CSF CXCL13 was a prognostic biomarker for CNS lymphoma patients. Our study suggests that multi-marker algorithms are important diagnostic tools for patients with CNS lymphoma.