Frontiers in Oncology · 2019 · 20 citations · 29 references
Life SciencesBiochemistryHematologyMetabolomicsMolecular DiagnosticsStem Cell TransplantationAmino Acid MetabolomicsClinical MetabolomicsMass Spectrometry-based MetabolomicsMetabolite ProfilingTransplant BiomarkersUntargeted MetabolomicsBranched-chain Amino Acid MetabolismBranched-chain Amino AcidsClinical Metabolite ProfilingDisease Metabolite ProfilingGraft-versus-host Disease BiomarkersMetabolomic BiomarkersPredictive BiomarkersSulfur-containing Metabolites
The allogeneic hematopoietic stem cell transplantation procedure-the only curative therapy for many types of hematological cancers-is increasing, and graft vs. host disease (GVHD) is the main cause of morbidity and mortality after transplantation. Currently, GVHD diagnosis is clinically performed. Whereas, biomarker panels have been developed for acute GVHD (aGVHD), there is a lack of information about the chronic form (cGVHD). Using nuclear magnetic resonance (NMR) and gas chromatography coupled to time-of-flight (GC-TOF) mass spectrometry, this study prospectively evaluated the serum metabolome of 18 Brazilian patients who had undergone allogeneic hematopoietic stem cell transplantation (HSCT). We identified and quantified 63 metabolites and performed the metabolomic profile on day -10, day 0, day +10 and day +100, in reference to day of transplantation. Patients did not present aGVHD or cGVHD clinical symptoms at sampling times. From 18 patients analyzed, 6 developed cGVHD. The branched-chain amino acids (BCAAs) leucine and isoleucine were reduced and the sulfur-containing metabolite (cystine) was increased at day +10 and day +100. The area under receiver operating characteristics (ROC) curves was higher than 0.79. BCAA findings were validated by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) in 49 North American patients at day +100; however, cystine findings were not statistically significant in this patient set. Our results highlight the importance of multi-temporal and multivariate biomarker panels for predicting and understanding cGVHD.
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