Proteometabolomics of Melphalan Resistance in Multiple Myeloma
Authors: Koomen David C. , Guingab Joy D. , Oliveira Paula S. , Fang Bin , Liu Min , Welsh Eric A. , Meads Mark B. , Nguyen Tuan , Meke Laurel E. , Eschrich Steven A. , Garrett Timothy J. , Koomen John M. , Shain Kenneth H. 
Published: October 2019

Although advancements in therapeutic regimens for treating multiple myeloma (MM) have prolonged patient survival, the disease remains incurable. Several classes of drugs have contributed to these improvements, such as proteasome inhibitors, immunomodulators, deacetylase inhibitors, monoclonal antibodies, and alkylating agents including melphalan. An expanded arsenal of diverse chemotherapy targets has improved patient care significantly, yet we still lack sufficient knowledge of how cellular metabolism and drug processing can contribute to drug resistance. To address this issue, we utilize cell line models to simulate naïve and drug resistant states, which identify drug modifications, endogenous metabolites, proteins, and acute metabolic profile alterations associated with therapeutic escape.

Here, we specifically focus on melphalan; an alkylating agent that forms DNA interstrand crosslinks, inhibits cell division, and leads to cell death through apoptosis (Povirk & Shuker. Mutat. Res. 1994, 318, 205). Melphalan remains a critical component of high dose therapy in the context of stem cell transplant and induction therapy in transplant ineligible patients outside the US. Ineffectiveness of alkylating agents remains a critical problem and serves as an excellent model for investigation of cellular metabolism and its contribution to drug resistance.

Two parental MM cell lines (8226 & U266) were obtained from ATCC and resistant derivatives of each cell line (8226-LR5 & U266-LR6) were selected after chronic drug exposure. To assess mechanisms of melphalan resistance, we use liquid chromatography-mass spectrometry-based metabolomics and proteomics approaches, including studies of drug metabolism, untargeted metabolomics, and activity based protein profiling (ABPP). Drug metabolism monitors the intracellular and extracellular drug modifications over a 24-hour period after acute treatment. Untargeted metabolomics is used to compare the steady state endogenous intracellular metabolites of naïve and drug resistant cells. Differences in endogenous metabolites between naïve and drug resistant cell lines are also examined in the acute treatment dataset. ABPP utilizes desthiobiotinylating probes to enrich for ATP-utilizing enzymes, which are identified and quantified to enable comparison.

We initially compared acute melphalan treatment in drug naive and resistant isogenic cell line pairs. Predictably, melphalan was converted into monohydroxylated and dihydroxylated metabolites more quickly in cells than in media controls. Differences in the formation of these metabolites between the naïve and resistant cell lines were not observed. The untargeted metabolomics data indicated in the 8226-LR5 model, glutathione and xanthine levels are elevated, while guanine is suppressed relative to naive cells. ABPP demonstrated changes in several enzymes related to purine and glutathione metabolism (Figure 1). Interestingly, the U266/U266-LR6 cell line models exhibit higher baseline levels of glutathione when compared with 8226/8226-LR5, indicating heterogeneous means of drug resistance. Alterations in arginine biosynthesis and nicotinate/nicotinamide metabolism are observed in the untargeted metabolomics and ABPP of U266/U266-LR6. Common pathways (e.g. purine biosynthesis) are altered in both models, although the changes involve different molecules.

In examining two models of acquired melphalan resistance, we demonstrate frank differences in metabolic pathways associated with steady state and acute drug response. These data demonstrate the potential heterogeneity in drug resistance mechanisms and the need for more biomarkers to personalize treatment. Ongoing studies involve introduction of enzyme inhibitors in targeted pathways and supplementation of metabolites to validate their role in resistance. Furthermore, we will examine expression of these metabolic pathways associated with ex vivo melphalan resistance in a cohort of over 100 patient samples with paired RNA sequencing. The long term goals are to elucidate mechanisms of therapeutic response, identify biomarkers of metabolism in melphalan resistance, enhance drug efficacy, predict personalized patient treatment, and improve overall MM patient care.

Disclosures

No relevant conflicts of interest to declare.