Metabolomic Profiling of Serum from Myeloma and MGUS Patients - a Novel Strategy to Identify Potential Biomarkers of Myeloma Development and Progression

Introduction: Drivers that underlie the progression of MGUS (Monoclonal gammopathy of undetermined significance) to multiple myeloma are yet largely unknown. Because of the vast number of potential players, these drivers may not necessarily aimed to transform plasma cell or the bone marrow niche, but rather remodel a supporting microenvironment. Metabolic alterations have been linked to cancer development and resistance to chemotherapy. It has been suggested that high rates of glycolysis and glutaminolysis are necessary in malignant cells to allow sensitive and precise control of the pathways that generate metabolic intermediates for macromolecular biosynthesis, hence cancer progression and metastasis. Experimental evidence indicates that glutamine, the most abundant amino acid in the blood and tissues, is the major respiratory fuel for cancer cells, and its conversion to glutamate is the first step in a series of reactions that generate essential metabolic intermediates. Thus, physiologic concentrations of circulating glutamine are required for optimal growth of malignant cells. This study attempts to identify circulating metabolites in myeloma patients which could potentially relate to disease progression.

Methods: We employed hydrophilic interaction liquid chromatography (ZIC-pHILIC)-high resolution mass spectrometry (Q-Exactive) to profile metabolites in cell-free serum samples from 10 MGUS and 10 myeloma patients with written consent. The data were analysed using IDEOM software and its quality was assessed by calculating % RSD (relative standard deviation) of peak median and internal standards within groups, repeated analysis of pooled quality control samples, and inspecting the heatmap for outlier samples. The fold changes of metabolites were measured in the MGUS versus myeloma cohorts using the ratio of the mean peak intensities. Significant features generated after statistical analyses were used for metabolic pathway analysis.

Results: In excess of 600 metabolite features were reproducibly detected, with 76 metabolites confidently identified based on authentic standards and the remainder putatively identified by accurate mass. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis determined that amino acids, carbohydrate and glycerolipid metabolism were affected by myeloma. L-glutamate, L-alanine and L-phenylalanine amino acids showed 0.63-, 0.71- and 0.70-fold decreases, respectively, in myeloma patients compared to MGUS individuals (p<0.05). Consistent with the literature, the circulating level of glutamine was maintained, confirming that glutamine's uptake and release by various organs and skeletal muscle is proportional to its usage by cancer cells for glutaminolysis that supplies them with L-glutamate. With regard to carbohydrate metabolism, D-gluconic acid, pentitol, glycerol and citrate with 0.57-, 0.60-, 0.68- and 0.80-fold decreases, respectively, (p <0.05) were among the compounds with the highest confidence that contributed to the discrimination between MGUS and myeloma. The only markedly decreased lipid-associated metabolite was sn-Glycerol 3-phosphate (p value: 0.043). Exploring the metabolic pathways employed by myeloma cells for energy and biomass generation, Pentose Phosphate Pathway (PPP) appeared to be highly perturbed as D-gluconic acid, D-ribose, deoxyribose and D-glucono-1,5-lactone all showed significantly decreased levels ranging from 0.38 to 0.70 fold. The involvement of the tricarboxylic acid (TCA) cycle, to which both L-glutamate and glucose can contribute, was also evident as suggested by decreases in citrate, malate (0.48-fold), cis-aconitate (0.68-fold) and succinate (0.76-fold).

Conclusions: As proof of concept, our results showed significant differences in a number of circulating metabolites between myeloma and MGUS, which requires further validation through a quantitative metabolomics study. Here, the analysis of non-invasive sampling and easily traceable circulating metabolites exemplifies how detailed metabolic profiling could be potentially utilized to predict progression of MGUS to myeloma and to identify potential targets for treatment interventions.