Serum Metabolomic Profiling Correlated with ISS and Clinical Outcome for Multiple Myeloma Patients Treated with High Dose Melphalan and Autologous Stem Cell Transplantation

Background. The prognosis in multiple myeloma (MM) has improved significantly in the last two decades. However, the outcome after initial high dose melphalan followed by autologous stem cell infusion (ASCT) is very diverse and prognostic markers, based on techniques such as FISH and next generation sequencing have been proposed. Metabolomics, where low molecular weight molecules are detected in body fluids or tissues, is an emerging technique used for diagnostics and prognosis in different cancers. Indeed, as the metabolome is the final down-stream product of the metabolic processes in the organism, it could be highly representative of a disease phenotype.

In this study we wanted to examine the metabolomic profile in MM patients treated with ASCT, using nuclear magnetic resonance (NMR) spectroscopy, with the primary aim to determine if possible differences could be correlated to clinical outcome.

Patients. MM patients from two Swedish regions, Skåne and Västra Götaland, diagnosed from Jan 2006 to Dec 2016 were identified using the Swedish National Myeloma Registry and ASCT lists from the Sahlgrenska and Lund University Hospitals. Patients with stored serum in the Swedish National Myeloma Biobank, from the time of diagnosis (and pre-treatment), who underwent ASCT as part of the first line therapy were included. A total of 201 patients were included. Clinical data, such as ISS, paraprotein type, high risk cytogenetics, induction therapy, response, progression-free survival (PFS) and overall survival (OS) were obtained from the Swedish National Myeloma Registry or from medical records. Last follow-up was Feb 2019.

Methods. Serum samples were analyzed using a 600 MHz ¹H NMR spectrometer equipped with a 5 mm BBI room temperature probe. After the ¹H NMR data acquisition, peak picking, alignment and integration of the 1D CPMG data was performed in R with an in-house developed script using the speaq-package. The integrated peak data was analyzed using multivariate statistics in SIMCA 14. Principal component analysis was used to check for outliers, followed by discriminant analysis using orthogonal projections to latent structures - discriminant analysis (OPLS-DA) to discriminate between patients with differences in ISS stage and paraprotein type. The validity of the models was judged from R2 and Q2 values compared to permutation tests.

Results. As expected, PFS after ASCT differed significantly between the ISS groups. We found that patients with ISS stage III had a significantly different metabolomic profile than stage I patients (Fig 1). Among the most discriminating metabolites were e.g. the amino acids valine, alanine, glutamine and leucine which were higher in the ISS I group. Interestingly, stage I patients had a totally different lipid profile: they had significantly higher levels of apolipoproteins A1 and A2, the total cholesterol level and in most HDL and LDL main fractions compared to stage III patients. Furthermore, IgA patients had a different metabolomic profile compared with IgG patients (Fig 2), where e.g. the level of N-acetylated amino acids were higher in IgA patients. We found no metabolomic correlation with gender, response or high risk cytogenetics.

Discussion. We found in this study that the serum metabolomic profile at diagnosis differed in ASCT-treated MM patients with low and high ISS stage. We also found different patterns in patients with IgA vs IgG myeloma. A certain metabolomic pattern could reflect a more aggressive disease and also give potential information on different host factors important for the course of the disease. Together, these findings could form a base for future metabolomic studies of the biology of MM.

Figure 1

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Figure 1

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