Metabolic Changes Are Associated with Melphalan Resistance in Multiple Myeloma

Abstract

Multiple myeloma is an incurable hematological malignancy that impacts tens of thousands of people every year in the United States. Treatment for eligible patients involves induction, consolidation with stem cell rescue, and maintenance. High-dose therapy with a DNA alkylating agent, melphalan, remains the primary drug for consolidation therapy in conjunction with autologous stem-cell transplantation; as such, melphalan resistance remains a relevant clinical challenge. Here, we describe a proteometabolomic approach to examine mechanisms of acquired melphalan resistance in two cell line models. Drug metabolism, steady-state metabolomics, activity-based protein profiling (ABPP, data available at PRIDE: PXD019725), acutetreatment metabolomics, and western blot analyses have allowed us to further elucidate metabolic processes associated with melphalan resistance. Proteometabolomic data indicate that drug-resistant cells have higher levels of pentose phosphate pathway metabolites. Purine, pyrimidine, and glutathione metabolisms were commonly altered, and cell-line-specific changes in metabolite levels were observed, which could be linked to the differences in steady-state metabolism of nai ̈ve cells. Inhibition of selected enzymes in purine synthesis and pentose phosphate pathways was evaluated to determine their potential to improve melphalan's efficacy. The clinical relevance of these proteometabolomic leads was confirmed by comparison of tumor cell transcriptomes from newly diagnosed MM patients and patients with relapsed disease after treatment with high-dose melphalan and autologous stem-cell transplantation. The observation of common and cell-line-specific changes in metabolite levels suggests that omic approaches will be needed to fully examine melphalan resistance in patient specimens and define personalized strategies to optimize the use of high-dose melphalan.

Metabolic Disorders in Multiple Myeloma

“…It has to be noted that the patterns of metabolic landscape in myeloma cells are dynamic during the disease course. Metabolic changes have been associated with acquired resistance to backbone treatment agents in myeloma such as bortezomib and melphalan [ 17 , 18 , 19 ]. Interestingly, a heterogenous metabolic pattern in baseline positron emission tomography/computed tomography (PET/CT) has been recently associated with adverse prognosis in patients with MM [ 20 ].…”

Proteome coverage after simultaneous proteo-metabolome liquid-liquid extraction

“…We first determined the total protein amount using a colorimetric bicinchoninic acid assay. The average number of cells subjected to SPM-LLE was 4.3 × 10 6 cells. Solubilization with SDS and urea exhibited little variation while solubilization with SDC was more variable.…”

“…C) Number of proteins reproducibly identified in all independent experiments (n = 5, biological replicates) with the indicated buffer systems. Average number of cells: 4.3 × 10 6 cells.…”

“…One reason for this discrepancy may be that sample preparation techniques used in metabolomics typically involve deproteinization steps to precipitate proteins by acid and/or organic solvents. Consequently, multiomics studies for which both the proteome and the metabolome need to be analyzed are often not done on the same sample, but rather two comparable samples are prepared independently, one for proteome and one for metabolome analysis 3 4 5 6 . In recent years, methods for simultaneous proteo-metabolome extraction have been developed based on liquid-liquid extraction using methanol/chloroform 7 8 9 or methanol/methyl- tert -butyl-ether 10 .…”

Drugging the undruggable: activity-based protein profiling offers opportunities for targeting the KLK activome

“…These preclinical proteomics studies already have applications in the study of melphalan resistance in multiple myeloma, target discovery for dasatinib in gastric cancer, and curcumin resistance screening in colon cancer among many functional ABPP-driven investigations. 2–4 From a clinical perspective, these assays may be better equipped at predicting off target effects well before entering clinical trials as protein interactions are often synergistic across multiple biological pathways. 5 Chemical proteomics has aided in the characterization of enzyme activity profiles across multiple enzyme families such as proteases, phosphatases, glycosidases, oxidoreductases, and kinases further demonstrating the abundance of targeting potential from a drug development standpoint.…”