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

Liying Zhou; Qing Li; Jingshen Xu; Shuaikang Wang; Zhiqiang Song; Xinyi Chen; Yan Ma; Zhiguang Lin; Bobin Chen; He Huang

doi:10.1093/noajnl/vdac181

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

Neurooncol Adv. 2022 Dec 13;5(1):vdac181. doi: 10.1093/noajnl/vdac181. eCollection 2023 Jan-Dec.

Authors

Liying Zhou¹, Qing Li², Jingshen Xu¹, Shuaikang Wang¹, Zhiqiang Song^{1

3}, Xinyi Chen¹, Yan Ma², Zhiguang Lin², Bobin Chen², He Huang^{1

4}

Affiliations

¹ Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200438, China.
² Department of Hematology, Huashan Hospital, Fudan University, Shanghai, 200438, China.
³ School of Life Sciences, Inner Mongolia University, Hohhot Inner Mongolia, 010021, China.
⁴ Shanghai Qi Zhi Institute, Shanghai, 200030, China.

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.

Keywords: high-dose methotrexate; metabolomics; prediction model; primary CNS lymphoma.