Abstract 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.

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

Primary central nervous system lymphoma (PCNSL) is an aggressive and rare malignancy with poor prognosis. However, there are no reliable prognostic biomarkers for PCNSL in clinical practice. Here, we aimed to identify a reliable prognostic biomarker for predicting the survival of PCNSL patients. In this study, multiplex immunofluorescence and digital imaging analysis were used to characterize tumor-associated macrophages (TAMs) immunophenotypes and the expression of programmed cell death ligand 1 on TAMs, with regard to prognosis from diagnostic tumor tissue samples of 59 PCNSL patients. We found that the M2-to-M1 ratio was a more reliable prognostic biomarker for PCNSL than M1-like or M2-like macrophage infiltration. In addition, the combination of programmed death-ligand 1 (PD-L1) expression on TAMs and the M2-to-M1 ratio in PCNSL demonstrated improved performance in prognostic discrimination than PD-L1-positive TAMs or M2-to-M1 ratio. To validate the prognostic significance of the combined TAMs associated biomarkers, they were integrated into the International Extranodal Lymphoma Study Group (IELSG) index and termed as IELSG-M index. Kaplan-Meier plots showed that the IELSG-M index could discriminate patients into low-, intermediate- or high-risk subgroups, better than IELSG, in terms of prognosis. The areas under the receiver operating characteristic curves of IELSG-M was 0.844 for overall survival; superior to the IELSG model (0.580). Taken together, this study's findings showed that the combination of PD-L1 on TAMs and the M2-to-M1 ratio could be strong prognostic predictive biomarkers for PCNSL and the IELSG-M index had improved prognostic significance than the IELSG index.

Xianqiu Wu

Papers

Combined tumor-associated macrophages biomarker predicting extremely poor outcome of patients with primary central nervous system lymphoma.