Cerebrospinal fluid metabolic markers predict prognosis behavior of primary central nervous system lymphoma with high-dose methotrexate-based chemotherapeutic treatment

Background Primary central nervous system lymphoma (PCNSL) is a highly aggressive non-Hodgkin's B-cell lymphoma which normally treated by high-dose methotrexate (HD-MTX)-based chemotherapy. However, such treatment cannot always guarantee a good prognosis (GP) outcome while suffering several side effects. Thus, biomarkers or biomarker-based models that can predict PCNSL patient prognosis would be beneficial. Methods We first collected 48 patients with PCNSL and applied HPLC-MS/MS-based metabolomic analysis on such retrospective PCNSL patient samples. We then selected the highly dysregulated metabolites to build a logical regression model that can distinguish the survival time length by a scoring standard. Finally, we validated the logical regression model on a 33-patient prospective PCNSL cohort. Results Six metabolic features were selected from the cerebrospinal fluid (CSF) that can form a logical regression model to distinguish the patients with relatively GP (Z score <= 0.06) from the discovery cohort. We applied the metabolic marker-based model to a prospective recruited PCNSL patient cohort for further validation, and the model preformed nicely on such a validation cohort (AUC = 0.745). Conclusions We developed a logical regression model based on metabolic markers in CSF that can effectively predict PCNSL patient prognosis before the HD-MTX-based chemotherapy treatments.

Automated summary

In this study, a logical regression model based on metabolic markers in cerebrospinal fluid (CSF) was developed to effectively predict the prognosis of PCNSL patients before chemotherapy treatments. The model performed well in a validation cohort of PCNSL patients.