ALBERT

Description: 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.

Description: Introduction The use of proteasome inhibitors (PIs), such as bortezomib (BTZ), in multiple myeloma (MM) has markedly increased the survival of newly diagnosed patients. Although advancements in therapeutic regimens in the past decade have improved prognosis, we lack knowledge of the mechanisms that lead to drug resistance. To assess the contributors to BTZ-resistance, we integrated steady-state metabolomics, proteomics and gene expression from two naïve and BTZ-resistant cell line models. In addition, gene expression associated with ex vivo PI resistance has been analyzed. Potential predictive biomarkers of PI-resistance and novel targets for combination therapy will be investigated. Methods Parental cell lines, RPMI 8226 and U266, were acquired from ATCC. 8226-B25 and U266-PSR (kind gift from Dr. S. Grant) BTZ-resistant derivatives were selected from their respective parental naïve cell lines by chronic drug exposure. Untargeted metabolomics, activity-based protein profiling (ABPP), and expression proteomics data were acquired using liquid chromatography-mass spectrometry. Gene expression profiles of both cell lines and ex vivo patient specimens were derived from RNAseq. Metabolomics and proteomics data were normalized with iterative rank order normalization. Significantly different genes, proteins, and metabolites were integrated for pathway mapping and identification of biomarkers for PI resistance. Results Consistent with previous findings, kynurenine, a product of tryptophan catabolism, is significantly altered in both of our cell line models. In the 8226 and 8226-B25 pair, PI resistance was associated with increased kynurenine and positively correlated with TDO2 and IDO1 overexpression consistent with published literature (Li et al. Nature Medicine, 2019, 25, 850-60). As expected, PSMB2, a subunit of the proteasome, is overexpressed and has a higher activity in both 8226-B25 and U266-PSR in the ABPP and expression proteomics, and higher expression in 8226-B25 RNAseq data. PSMB2 is also overexpressed and significant in the RNAseq patient data, increasingly from newly diagnosed/pre-treatment to early relapse (p-value 2E-4) and late relapse (p-value 0.0052). In addition, CD38 is an enzyme responsible for conversion of NAD+ to nicotinamide and ADP-ribose. It has increased expression in MM cells and is significantly downregulated in ABPP (log2 ratio -4.25, p-value 2E-13), expression proteomics (log2 ratio -2.5), and RNAseq (log2 ratio -2.6, p-value 5E-6) in the 8226-B25 BTZ-resistant cells. In the steady-state metabolomics of the 8226-B25 cells, ADP-ribose (log2 ratio 4.11, p-value 2E-5) is the most upregulated known metabolite. This change suggests a downstream result of resistance within this interaction and a potential biomarker of PI resistance. However, gene expression of CD38 in patient samples was relatively unchanged. CD38 was not detected in the U266-PSR proteomics or RNAseq data and ADP-ribose (log2 ratio -0.63, p-value 0.06) was not significantly altered, suggesting a different mechanism of resistance in this cell line. Conclusions Though common mechanisms of PI resistance were identified, our data clearly show that BTZ-resistance arises by heterogeneous means in the two cell line models, promoting the need for biomarkers that can determine resistance and predict response in individual patients (or cohorts). Decreased expression of CD38 in 8226-B25 could elucidate mechanisms of PI resistance and immune response evasion strategies of MM cells. Further investigation of CD38 expression as a BTZ-resistance biomarker could lead to improving combination therapies with monoclonal antibodies, such as daratumumab, and PIs in newly diagnosed MM patients by predicting response prior to treatment. Further examination of ADP-ribose metabolism may lead to the mechanism of synergy between PARP inhibitors and proteasome inhibitors. Ultimately, we plan to integrate and utilize these multi-omics approaches in patient specimens and improve MM patient care by identifying PI resistance biomarkers to predict patient response. Disclosures Shain: Adaptive Biotechnologies: Consultancy; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees.