Exploratory profiling of metabolites in cerebrospinal fluid using a commercially available targeted LC-MS based metabolomics kit to discriminate leptomeningeal metastasis.

Abstract

Background: Leptomeningeal metastasis (LM) is a devastating complication of cancer that is difficult to treat. Thus, early diagnosis is essential for LM patients. However, cerebrospinal fluid (CSF) cytology has low sensitivity, and imaging approaches are ineffective. We explored targeted CSF metabolic profiling to discriminate among LM and other conditions affecting the central nervous system (CNS).

Methods: We quantitatively measured amino acids, biogenic amines, hexoses, acylcarnitines (AC), cholesteryl esters (CE), glycerides, phosphatidylcholines (PC), lysophosphatidylcholines (LPC), sphingomyelins (SM), and ceramides (Cer) in 117 CSF samples from various groups of healthy controls (HC, n = 10), patients with LM (LM, n = 47), parenchymal brain tumor (PBT, n = 45), and inflammatory disease (ID, n = 13) with internal standards using the Absolute IDQ- p400^® targeted mass spectrometry kit. Metabolites detected in > 90% of samples or showing a difference in proportional level between groups ≥ 75% were used in logistic regression models when there was no single metabolite with AUC = 1 for the groups of comparison.

Results: PC and SM had higher levels in LM than in PBT or HC, whereas LPC had lower level in PBT than the other groups. Glycerides and Cer levels were higher in PBT and LM than in HC. Long-chain AC level in PBT was lower than in LM or HC. A regression model including Ala, PC (42:7), PC (30:3), PC (37:0), and Tyr achieved complete discrimination (AUC = 1.0) between LM and HC. In comparison of PBT and HC, twenty-six individual metabolites allowed complete discrimination between two groups, and between ID and HC fourty-six individual lipid metabolites allowed complete discrimination. Twenty-one individual metabolites (18 ACs and 3 PCs) allowed complete discrimination between LM and PBT.

Conclusions: Using a commercial targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics kit, we were able to differentiate LM from HC and PBT. Most of the discriminative metabolites among different diseases were lipid metabolites, for which their CNS distribution and quantification in different cell types are largely unknown, whereas amino acids, biogenic amines, and hexoses failed to show significant differences. Future validation studies with larger, controlled cohorts should be performed, and hopefully, the kit may expand its metabolite coverage for unique cancer cell glucose metabolism.