Multiple myeloma cells undergo metabolic reprogramming in response to the hypoxic and nutrient-deprived bone marrow microenvironment. Primary oncogenes in recurrent translocations might be able to drive metabolic heterogeneity to survive the microenvironment that can present new vulnerabilities for therapeutic targeting. t(4;14) translocation leads to the universal overexpression of histone methyltransferase NSD2 that promotes plasma cell transformation through a global increase in H3K36me2. Here, we identified PKCα as an epigenetic target that contributes to the oncogenic potential of NSD2. RNA sequencing of t(4;14) multiple myeloma cell lines revealed a significant enrichment in the regulation of metabolic processes by PKCα, and the glycolytic gene, hexokinase 2 (HK2), was transcriptionally regulated by PKCα in a PI3K/Akt-dependent manner. Loss of PKCα displaced mitochondria-bound HK2 and reversed sensitivity to the glycolytic inhibitor 3-bromopyruvate. In addition, the perturbation of glycolytic flux led to a metabolic shift to a less energetic state and decreased ATP production. Metabolomics analysis indicated lactate as a differential metabolite associated with PKCα. As a result, PKCα conferred resistance to the immunomodulatory drugs (IMiD) lenalidomide in a cereblon-independent manner and could be phenocopied by either overexpression of HK2 or direct supplementation of lactate. Clinically, t(4;14) patients had elevated plasma lactate levels and did not benefit from lenalidomide-based regimens. Altogether, this study provides insights into the epigenetic-metabolism cross-talk in multiple myeloma and highlights the opportunity for therapeutic intervention that leverages the distinct metabolic program in t(4;14) myeloma.

Significance:

Aberrant glycolysis driven by NSD2-mediated upregulation of PKCα can be therapeutically exploited using metabolic inhibitors with lactate as a biomarker to identify high-risk patients who exhibit poor response towards IMiD-based regimens.

Metabolic shifts are a hallmark of cancer (1). Cancer cells have increased bioenergetics and anabolism as compared with normal cells due to adaptations to increased proliferation rate, hypoxic microenvironment and therapy evasion (2–4). This metabolic distinction has important consequences as the addiction of myeloma cells to a metabolic pathway or increased requirements for specific metabolites could reveal new dependencies that can be targeted through nutrient starvation or pharmacological inhibitors (5). Moreover, targeting metabolome of cancer cells is downstream of DNA, RNA, and protein changes, giving an accurate reflection of the cancer cell phenotype. This is especially critical to overcome undruggable oncogenes that are known to drive cancer phenotypes.

Multiple myeloma is a plasma cell malignancy with extensive molecular and cytogenetic heterogeneity, leading to varying clinical outcomes. Multiple myeloma is the second most common hematologic malignancy, after non-Hodgkin lymphoma (6). Till date, metabolomics comparison has been performed for healthy controls versus patients with different stages of multiple myeloma, and revealed significant alterations in energy metabolism (7). Glucose and glutamine metabolism were the most frequently studied pathways in multiple myeloma, whereas serine, folate, pentose phosphate pathway, and fatty acid oxidation have all been implicated in myelomagenesis (4, 8–11). Despite this, the study of metabolic reprogramming in multiple myeloma is obscure as compared with solid tumors, and whether recurrent translocations define certain metabolic features is unknown. Importantly, each primary translocation drives the overexpression of a specific gene, such as the transcription factor c-MAF in t(14;16)-translocated cells or the histone methyltransferase NSD2 in t(4;14)-translocated cells, making multiple myeloma an ideal disease to study how metabolic networks are influenced by genetic or epigenetic factors.

Overexpression of NSD2 occurred in 15% to 20% of nonhyperdiploid multiple myeloma cases and catalyzes the active histone mark H3K36me2 via its evolutionarily conserved SET domain (12, 13). Despite numerous studies characterizing the chromatin regulatory activities of NSD2, it has been challenging to develop small molecule inhibitors against NSD2 that can achieve tangible clinical endpoints. With the introduction of novel agents such as proteasome inhibitors (PI) and immunomodulatory drugs (IMiD), patients had significantly benefited from an improved overall outcome, but this disease remains incurable, especially in elderly patients. Some of the therapeutic challenges in multiple myeloma treatment are patients with high-risk cytogenetics who progresses rapidly, or patients who are either intrinsically refractory or subsequently developed resistance to these novel agents (14, 15).

Accumulating evidence implicates altered energy metabolism as a mechanism that myeloma cells acquired resistance against multiple myeloma drugs (16–18), and elevated aerobic glycolysis was observed in bortezomib, dexamethasone, and melphalan resistance (19). It is also evident that metabolism and the epigenome are intricately linked as epigenetic enzymes rely on the availability of metabolites such as S-adenosylmethionine (SAM) from the methionine cycle to provide the starting substrates for DNA and histone methylation (20–22). In addition, multiple tricarboxylic acid (TCA) cycle intermediates such as alpha-ketoglutarate, succinate, and fumarate regulate DNA and histone methylation, while acetyl-CoA controls the level of histone acetylation (23). In a bidirectional manner, chromatin-modifying enzymes can drive the metabolic plasticity of tumor cells by permissible switch between different metabolic networks. As a consequence, this study aims to identify the metabolic reprogramming of NSD2 to broaden the range of targets amendable to therapy and yield a critical understanding on the compensatory metabolic networks that have adapted to the perturbed driver oncogene.

Cell culture and inhibitors

Multiple myeloma cell lines KMS11, KMS18, KMS28BM, KMS34, H929, OPM2, UTMC2, KMS12BM, MM1S, RPMI8226, and U266 were purchased from ATCC and cultured in RPMI1640 medium supplemented with 10% FBS. Cell lines were authenticated (Centre for Translational Research and Diagnostics, National University of Singapore, Singapore) and regularly tested for Mycoplasma (Lonza) before experimental use. HEK293T cells were cultured in DMEM supplemented with 10% FBS. All of the mentioned cells above were maintained at 37°C and 5% CO2 incubator. PI3K inhibitor LY294002, ERK1/2 inhibitor SCH772984, and 5-azacytidine were purchased from Santa Cruz Biotechnology. Lenalidomide was purchased from Sigma-Aldrich, bortezomib was from Santa Cruz Biotechnology, daratumumab was from Janssen Biotech, Inc. Primary plasma samples were collected with written informed consent at National University Cancer Institute (Singapore) with the approval from Institutional Review Board (IRB; number 2017/00196) in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines.

Hexokinase 2 activity assay and glucose-6-phosphate levels

Hexokinase activity was measured according to manufacturer's protocol (Abcam). KMS18 and KMS28BM cells were treated with 1 μmol/L of DMSO, phorbol 12-myristate 13-acetate (PMA), phorbol 12,13-dihexanoate (PDH) or 4α-PDD for 24 hours. Forty micrograms of protein lysate was added to 96-well plate and reaction mix and incubated for 35 minutes before absorption was measured at 450 nm using a microplate reader (Tecan). Glucose-6-phosphate levels were quantified according to manufacturer's protocol (Abcam). Briefly, 5 × 106 cells were homogenized with PBS and added to reaction mix to a final volume of 50 μL, incubated for 30 minutes before absorption measurement at 450 nm using a microplate reader (Tecan).

Measurement of bioenergetics using Seahorse assay

Seahorse XF24 (Agilent Technologies) was used for measurement of basal respiration, real-time ATP assay, oxygen consumption rate (OCR), and extracellular acidification rate (ECAR). Seahorse XF24 cell culture microplates (Agilent Technologies) were precoated with Cell-Tak (Corning). Human myeloma cell lines (HMCL) were resuspended and seeded at 2 × 105 cells per well in Seahorse assay media (RPMI, pH 7.4). Measurement of cellular bioenergetics was performed with addition of mitochondrial and glycolytic specific activators and inhibitors. Basal respiration was measured at baseline before injection of inhibitors. Glucose (7.5 mmol/L), oligomycin (1.5 μmol/L) and 2-deoxy-d-glucose (2-DG; 50 mmol/L) were used for glycolysis stress assay. ECAR was measured from glycolysis stress test and normalized to nonglycolytic ECARs. OCR was measured from mitochondrial stress test and normalized to basal respiration rate. Various parameters were measured such as mitochondrial basal respiration, proton leak, reserve capacity, and maximal respiration, after correction of nonmitochondrial respiration. Glycolysis, glycolytic capacity and glycolytic reserve were measured for glycolytic stress test. The XF Mito Stress Test report generator, XF Glycolysis Stress Test report generator, and Agilent Seahorse analytics were used to tabulate parameters from Wave data that were exported to GraphPad Prism.

Lactate measurement

Sodium L-lactate was purchased from Sigma-Aldrich and dissolved in water. Increasing doses of L-lactate (0, 5, 10, 15, or 30 mmol/L) were used as a medium supplement and the uptake by cells were determined using Lactate Glo Assay (Promega) after 1 hour. The lactate detection reagent was added to cell lysates at a 1:1 ratio and the intracellular lactate levels were determined by luminescent signal after 1-hour incubation. Bone marrow or peripheral blood samples were collected from patients with multiple myeloma and processed by centrifugation at 3,000 rpm for 10 minutes, and top layer plasma fraction was collected at stored in −80°C until use. The plasma samples were diluted 10 to 100× before measurement of lactate with Lactate Glo Assay (Promega).

In vivo mouse xenograft

All experiments involving animals were approved and in accordance to Institutional Animal Care and Use Committee protocol (IACUC protocol; National University of Singapore). KMS11 cells were stably transduced with shLUC control or shPKCα and inoculated subcutaneously at 5 × 106 cells per flank into the left and right flanks of 6-week-old female NOD/SCID mice respectively (n = 8, in vivo). At the end of the experiment (day 31), the mice were sacrificed and tumors were excised. Tumor volumes were determined using the formula [calculated as length × width (2)/2] and photographed.

Metabolomics

Targeted quantitative analysis was performed on KMS28BM scrambled control and PKCα shRNA in biological duplicates using capillary electrophoresis mass spectrometry (CE-TOFMS and CE-QqQMS) in the cation and anion analysis modes for analyzing 54 cationic and 62 anionic metabolites, respectively. Peaks detected in CE-TOFMS analysis were extracted using automatic integration software (MasterHands ver.2.19.0.2 developed at Keio University) and those in CE-QqQMS analysis were extracted using automatic integration software (MassHunter Quantitative Analysis B.06.00 Agilent Technologies) in order to obtain peak information, which includes m/z, migration time (MT), and peak area. The peaks were annotated based on the Human Metabolome Technologies, Inc. (HMT) metabolite database. In addition, absolute quantification was calculated by normalizing the peak area of each metabolite with respect to the area of the internal standard and by using standard curves, which were obtained by three-point calibrations.

Bioinformatics

RNA sequencing (RNA-seq) was performed in biological triplicates and Metabolomics in biologic duplicates. Measurements from real-time PCR assays were obtained from three biologic repeats with technical duplicates; apoptosis and cell cycle assays were performed in biological triplicates; CTG viability assays were performed in three biological repeats with technical octuplicates; chromatin immunoprecipitation (ChIP) was performed in biologic duplicates then technical triplicates for qPCR analysis. All results are plotted as mean ± SD. Statistical analysis was performed with Excel or GraphPad Prism 9 using Student t test for pairwise comparison between each treatment group with its corresponding control, and adjusted using Benjamini–Hochberg correction method (FDR = 0.05) for multiple comparisons. Western Blot analyses are representative images from two independent experiments, and CFU are representative fields from three biological independent experiments. Correlation analyses were performed between NSD2 (longest protein-coding transcript, ENST00000382891) and PKCα (ENST00000413366) in CoMMpass dataset using Pearson correlation analysis stratified by TC classes. The levels of significance were labelled as follows: *, P < 0.05; **, P < 0.01; ***, P < 0.001 (nonsignificant values were unlabeled unless indicated otherwise).

Data availability

RNA-seq data are deposited in Gene Expression Omnibus (GEO) under accession number GSE208739. Absolute concentrations of metabolites from metabolomics were provided in Supplementary Table S1. All other raw data are available upon request from the corresponding author. The data analyzed in this study were obtained from MMRF CoMMpass Study at https://research.themmrf.org/.

PKCα is aberrantly overexpressed in t(4;14) multiple myeloma cells through NSD2-dependent H3K36me2 histone methylation

Previously, we used RNA-seq to characterize the transcriptome of NSD2 in KMS11, a t(4;14)-translocated myeloma cell line, as an approach to identify therapeutically actionable targets of NSD2 (13). We noticed that the oncogenic serine-threonine kinase, PKCα, is downregulated upon NSD2 knockdown. As protein kinases represent a major class of drug targets that can be pharmacologically inhibited, we sought to further elucidate the role of PKCα in t(4;14) biology. First, we examine the association between NSD2 and PKCα by checking their expression in a panel of human myeloma cell lines. We found that PKCα is primarily expressed at higher levels in t(4;14)-translocated as compared with nontranslocated cells, in a similar manner to NSD2, but independent of FGFR3 (Fig. 1AC). Next, we performed immunofluorescence (IF) staining to examine the subcellular localization of PKCα, which could be detected in the cytoplasm of KMS28BM but was absent from a t(4;14)-negative U266 cell line (Fig. 1D). To verify that NSD2 is an upstream regulator of PKCα, we conducted loss-of-function studies of NSD2 using three shRNAs targeting the 3′, 3′ untranslated region (UTR) and CDS regions of the gene in KMS28BM and KMS18 cells. We achieved varying levels of NSD2 knockdown and a downregulation of the expression of PKCα at both mRNA and protein levels (Fig. 1E). In addition, PKCα is directly regulated by NSD2 in an epigenetic manner as NSD2 depletion reduced the occupancy of H3K36me2 on the promoter of PKCα (Fig. 1F). Analysis of the publicly available CoMMpass dataset showed that NSD2 and PKCα were positively correlated in the t(4;14) TC class (Fig. 1G). Finally, enforced expression of NSD2 in U266 cells was sufficient to induce the expression of PKCα (Fig. 1H). Altogether, our data implicate a novel involvement of PKCα in t(4;14) myeloma.

Figure 1.

Correlation between NSD2 and PKCα in t(4;14) myeloma. A, qRT-PCR analysis of NSD2 and PKCα in a panel of t(4;14) translocated and nontranslocated human myeloma cell lines. GAPDH was used as for normalization. n  =  3 biologically independent replicates with technical duplicates. B, Scatter plot showing the correlation between transcript levels of NSD2 and PKCα in eleven HMCLs. Linear regression was performed using GraphPad Prism, and the Pearson r value and P value are indicated in the graph. C, Western blotting to determine the endogenous expression of NSD2, FGFR3, and PKCα in the panel of HMCLs. β-Actin was used as loading control. n  =  2 independent replicates. D, IF staining for PKCα with Alexa Fluor 488 secondary antibody (green) and 4',6-diamidino-2-phenylindole (DAPI) was used to stain nucleus (blue). IgG antibody was used as negative stain. KMS28BM, t(4;14) cell line; U266, non-t(4;14) cell line. n  =  3 independent replicates. E, Knockdown of NSD2 using CDS-targeting (shCDS), 3′ region-targeting (sh3’), or 3′UTR-targeting (sh3’ UTR) shRNA in KMS18 (left) and KMS28BM (right) cells. PKCα levels were determined and presented as fold change (FC) relative to shscr. n  =  3 independent replicates with technical duplicates. Statistical significance was determined by pairwise Student t test corrected for multiple comparisons by the Benjamini–Hochberg method. F, Schematic diagram representing genomic DNA region covering PKCα TSS region. Black bars indicate locations of ChIP–PCR primers pairs. ChIP-PCR assays with H3K36me2 antibodies at the PKCα core promoter region in control versus PKCα knockdown cells in KMS18 and KMS28BM. ChIP and input DNA were analyzed by qPCR. Negative primers span a gene desert region. Mouse IgG was used as negative control. n  =  2 biologically independent replicates with technical triplicates. G, Correlation between NSD2 and PKCα expression was determined in the t(4;14) group of the CoMMpass dataset and presented as a scatter plot. H, NSD2 plasmid was transiently transfected into U266 cells and qRT-PCR was performed at 24 hours (n  =  3 biologically independent replicates with technical duplicates). Immunoblot was done at 48 hours to determine PKCα expression (n  =  2 independent replicates). *, P < 0.05; **, P < 0.01; Student t test.

Figure 1.

Correlation between NSD2 and PKCα in t(4;14) myeloma. A, qRT-PCR analysis of NSD2 and PKCα in a panel of t(4;14) translocated and nontranslocated human myeloma cell lines. GAPDH was used as for normalization. n  =  3 biologically independent replicates with technical duplicates. B, Scatter plot showing the correlation between transcript levels of NSD2 and PKCα in eleven HMCLs. Linear regression was performed using GraphPad Prism, and the Pearson r value and P value are indicated in the graph. C, Western blotting to determine the endogenous expression of NSD2, FGFR3, and PKCα in the panel of HMCLs. β-Actin was used as loading control. n  =  2 independent replicates. D, IF staining for PKCα with Alexa Fluor 488 secondary antibody (green) and 4',6-diamidino-2-phenylindole (DAPI) was used to stain nucleus (blue). IgG antibody was used as negative stain. KMS28BM, t(4;14) cell line; U266, non-t(4;14) cell line. n  =  3 independent replicates. E, Knockdown of NSD2 using CDS-targeting (shCDS), 3′ region-targeting (sh3’), or 3′UTR-targeting (sh3’ UTR) shRNA in KMS18 (left) and KMS28BM (right) cells. PKCα levels were determined and presented as fold change (FC) relative to shscr. n  =  3 independent replicates with technical duplicates. Statistical significance was determined by pairwise Student t test corrected for multiple comparisons by the Benjamini–Hochberg method. F, Schematic diagram representing genomic DNA region covering PKCα TSS region. Black bars indicate locations of ChIP–PCR primers pairs. ChIP-PCR assays with H3K36me2 antibodies at the PKCα core promoter region in control versus PKCα knockdown cells in KMS18 and KMS28BM. ChIP and input DNA were analyzed by qPCR. Negative primers span a gene desert region. Mouse IgG was used as negative control. n  =  2 biologically independent replicates with technical triplicates. G, Correlation between NSD2 and PKCα expression was determined in the t(4;14) group of the CoMMpass dataset and presented as a scatter plot. H, NSD2 plasmid was transiently transfected into U266 cells and qRT-PCR was performed at 24 hours (n  =  3 biologically independent replicates with technical duplicates). Immunoblot was done at 48 hours to determine PKCα expression (n  =  2 independent replicates). *, P < 0.05; **, P < 0.01; Student t test.

Close modal

PKCα is essential for t(4;14) multiple myeloma growth and survival in vitro and in vivo

Next, we sought to define the functional role of PKCα as a downstream target of NSD2. We measured the effects of PKCα loss-of-function on cell viability in three t(4;14) cell lines, which portrayed significant growth impairment upon PKCα depletion (Fig. 2A). This was also evident in the soft agar colony formation assay, where a significantly lesser number of seeded cells transfected with PKCα shRNA were able to form colonies (Fig. 2B). Further analysis on cellular processes such as cell-cycle progression indicated that abrogation of PKCα concomitantly increased sub-G1 phase and decreased S-phase cell populations (Fig. 2C). Detection of cell death using Annexin V/PI double staining identified an increase population of cells in late apoptosis upon PKCα knockdown (Fig. 2D). Strikingly, there was a complete abrogation of the tumor-forming ability of PKCα shRNA-bearing myeloma cells (Fig. 2E). Importantly, these cellular phenotypes of PKCα depletion were not observed in U266 cell line that lacks NSD2 protein expression (Fig. 2B and C) and neither did loss of PKCα affected viability in three other non-t(4;14) cell lines (Supplementary Fig. S1A). These results suggested that PKCα is an important oncogene for t(4;14) cells where myeloma cell function is sensitive to PKCα depletion. Conversely, we showed that overexpression of PKCα is sufficient to rescue the effects of NSD2 knockdown (Fig. 2F). We next asked whether pharmacologic agents could reproduce the inhibitory effects of shRNA-mediated genetic knockdown of PKCα. However, viability assays interrogating four different PKC chemical compounds did not demonstrate superior efficacy toward t(4;14) as compared with non-t(4;14) cells (Supplementary Fig. S1B). This is likely due to the high frequency of off-target effects on other kinases, further highlighting the need to understand the downstream mechanisms of PKCα in t(4;14) myeloma.

Figure 2.

PKCα expression is required for t(4;14) myelomagenesis. A, KMS11, KMS18, and KMS28BM cells were stably transfected with PKCα-targeting shRNA and knockdown efficiency was assessed using Western blotting. β-actin was used as loading control (n  =  2 independent replicates). The viability of the knockdown cells was determined at 48 hours posttransfection (three biological repeats with technical octuplicates) and plotted as fold change to control (shLuc).B, Colony-forming assay was performed to determine the long-term viability of control versus PKCα knockdown cells over two weeks and the number of colonies was plotted. Representative images are shown (n = 3 biological replicates). C, Cell-cycle analysis of control and PKCα knockdown cells were measured using flow cytometry analysis and the percentage of cells in G1, S, or G2–M is indicated. Experiment was performed in biological triplicates. D, Control and PKCα knockdown cells were subjected to Annexin V-PI flow cytometry analysis at 48 hours to determine apoptosis. Representative FACS plots are shown and percentage of apoptosis indicates the double-positive fraction of Annexin V and PI from average mean of three independent experiment. E, Xenografts of KMS11 (5 million cells) with stable expression of nontargeting control (shLuc) or shPKCα were allowed to grow on the right and left flank of the mice (n = 8 biological replicates). Tumor volume was measured over time. Tumors were harvested after 31 days and imaged. Significant differences from pairwise comparison between means of control and treatment group using Student t test. **, P < 0.01. F, KMS28BM cells were transfected with scrambled shRNA (shscr), shRNA targeting NSD2 (using sh3’ UTR from Fig. 1E) with/without overexpression of PKCα. Right, expression of PKCα is validated with qPCR. Cells were counted and 1 × 103 cells were seeded into CFU media. Images were taken at 10 days postseeding. Representative field is shown and biological triplicates were used to plot the graph (middle). Significant differences determined by Student t test. **, P < 0.01; n.s., no significance.

Figure 2.

PKCα expression is required for t(4;14) myelomagenesis. A, KMS11, KMS18, and KMS28BM cells were stably transfected with PKCα-targeting shRNA and knockdown efficiency was assessed using Western blotting. β-actin was used as loading control (n  =  2 independent replicates). The viability of the knockdown cells was determined at 48 hours posttransfection (three biological repeats with technical octuplicates) and plotted as fold change to control (shLuc).B, Colony-forming assay was performed to determine the long-term viability of control versus PKCα knockdown cells over two weeks and the number of colonies was plotted. Representative images are shown (n = 3 biological replicates). C, Cell-cycle analysis of control and PKCα knockdown cells were measured using flow cytometry analysis and the percentage of cells in G1, S, or G2–M is indicated. Experiment was performed in biological triplicates. D, Control and PKCα knockdown cells were subjected to Annexin V-PI flow cytometry analysis at 48 hours to determine apoptosis. Representative FACS plots are shown and percentage of apoptosis indicates the double-positive fraction of Annexin V and PI from average mean of three independent experiment. E, Xenografts of KMS11 (5 million cells) with stable expression of nontargeting control (shLuc) or shPKCα were allowed to grow on the right and left flank of the mice (n = 8 biological replicates). Tumor volume was measured over time. Tumors were harvested after 31 days and imaged. Significant differences from pairwise comparison between means of control and treatment group using Student t test. **, P < 0.01. F, KMS28BM cells were transfected with scrambled shRNA (shscr), shRNA targeting NSD2 (using sh3’ UTR from Fig. 1E) with/without overexpression of PKCα. Right, expression of PKCα is validated with qPCR. Cells were counted and 1 × 103 cells were seeded into CFU media. Images were taken at 10 days postseeding. Representative field is shown and biological triplicates were used to plot the graph (middle). Significant differences determined by Student t test. **, P < 0.01; n.s., no significance.

Close modal

RNA-seq profiling revealed a significant number of PKCα-regulated genes in metabolism

PKCα expression and kinase activity triggers a plethora of tumor-promoting downstream signaling events that varied with cell type (24, 25). To better understand the molecular mechanism of PKCα in the context of t(4;14) multiple myeloma, we performed lentiviral-based stable knockdown of PKCα on KMS28BM and KMS18 for RNA-seq analysis. These cells were chosen based on their t(4;14) status and highest endogenous levels of PKCα. On the basis of the expression profiles, we found a similar number of differentially regulated genes (DEG) between the two cell lines, where over 500 genes were upregulated and 200 genes were downregulated (Fig. 3A). To narrow down, we overlapped the dataset to identify genes that were regulated in the same direction in both cell lines. This condensed the list to 115 upregulated and 76 downregulated high-confidence targets (Fig. 3B; Supplementary Table S2). Gene ontology and KEGG pathway analysis were conducted to prioritize enriched biological processes regulated by the DEGs. 44.9% genes were involved in cellular process and 28.9% of the genes were involved in metabolic processes (Fig. 3C and D; Supplementary Fig. S2A–S2C). As metabolic shift represent an important hallmark of malignancy that is underexplored in t(4;14) myeloma, we sought to validate a subset of these differentially regulated metabolic genes by qRT-PCR. In this experiment, we performed a transient transfection of PKCα shRNA into KMS18 and KMS28BM, and confirmed that these are immediate and direct targets of PKCα, instead of secondary targets arising from adaption toward the loss of PKCα (Fig. 3E). Altogether, our results suggested a novel, noncanonical role of NSD2 by participating in cellular metabolism through PKCα.

Figure 3.

Gene expression signature of PKCα in t(4;14) myeloma. A, KMS18 and KMS28BM with stable knockdown of PKCα (n = 3 biological replicates) were submitted for RNA-seq (BGI). The number of DEG were plotted for KMS18 and KMS28BM, respectively. B, Venn diagram showing the number of overlapping genes that were upregulated (115 genes; red) and downregulated (75 genes; blue). C, Heat map depicting the differentially regulated genes, upregulated (red) and downregulated (green) genes. PKCα was in the downregulated gene list. Clustering and heat map were generated with cluster v3.0 and Java TreeView. Adjusted P values are indicated in the figure. D, Gene ontology biological process analysis indicated that 44.9% genes were involved in cellular process and 28.9% genes were involved in metabolic processes among common DEGs. The breakdown of DEGs in the metabolic pathways is shown. E, The DEGs were independently validated using qRT-PCR. KMS18 and KMS28BM cells were transiently transfected using the Neon System with PKCα shRNA and mRNA were harvested 24 hours after transfection. Data is presented as fold change against control shscr, three biological repeats with technical duplicates. *, P < 0.05; **, P < 0.01; Student t test.

Figure 3.

Gene expression signature of PKCα in t(4;14) myeloma. A, KMS18 and KMS28BM with stable knockdown of PKCα (n = 3 biological replicates) were submitted for RNA-seq (BGI). The number of DEG were plotted for KMS18 and KMS28BM, respectively. B, Venn diagram showing the number of overlapping genes that were upregulated (115 genes; red) and downregulated (75 genes; blue). C, Heat map depicting the differentially regulated genes, upregulated (red) and downregulated (green) genes. PKCα was in the downregulated gene list. Clustering and heat map were generated with cluster v3.0 and Java TreeView. Adjusted P values are indicated in the figure. D, Gene ontology biological process analysis indicated that 44.9% genes were involved in cellular process and 28.9% genes were involved in metabolic processes among common DEGs. The breakdown of DEGs in the metabolic pathways is shown. E, The DEGs were independently validated using qRT-PCR. KMS18 and KMS28BM cells were transiently transfected using the Neon System with PKCα shRNA and mRNA were harvested 24 hours after transfection. Data is presented as fold change against control shscr, three biological repeats with technical duplicates. *, P < 0.05; **, P < 0.01; Student t test.

Close modal

PKCα promotes hexokinase 2 activity and mitochondrial localization in t(4;14) cells through PI3K-Akt pathway

Amongst the metabolic genes that were identified through RNA-seq, we decided to focus on hexokinase 2 (HK2) as cancer cells often present with a marked increase in glycolysis, or the “Warburg effect” (26–28). HK2 catalyze the conversion of glucose to glucose-6-phosphate (G6P), and this phosphorylation step commits the glucose molecule in the cell as a precursor for four metabolic pathways: glycolysis, pentose phosphate pathway, hexosamine biosynthesis and glycogenesis. We verified that knockdown of PKCα in KMS18 and KMS28BM downregulated HK2 expression and activity (Fig. 4A; Supplementary Fig. S3A). Importantly, this was downstream of NSD2, because knockdown of PKCα could reverse NSD2-driven expression of HK2 (Fig. 4B). Conversely, treatment with PKCα agonists PMA and PDH, but not their inactive analogue 4αPDD, led to an increase in HK2 mRNA expression (Fig. 4C). The increase in transcript levels translated into elevated HK2 activity and production of G6P (Fig. 4D), which could be reversed by NSD2 knockdown and rescued with PKCα overexpression (Fig. 4E and F). The regulation on HK2 was not observed in non-t(4;14) cells, which harbored low levels of PKCα (Supplementary Fig. S3B). There was also no evidence of direct NSD2 binding or H3K36me2 histone methylation on HK2 promoter (Supplementary Fig. S3C).

Figure 4.

PKCα regulates hexokinase 2 expression and activity in t(4;14) cells. A, PKCα knockdown was performed and qRT-PCR (24 hours) and Western blot analysis (48 hours) was used to determine HK2 levels. β-actin was used as loading control. Analysis from three biological repeats for qRT-PCR and representative images from two biological repeats for Western blot analysis. B, Overexpression of NSD2 in the presence and absence of PKCα shRNA was performed to determine HK2 levels using Western blot analysis at 48 hours posttransfection (n = 2 biological repeats). C, KMS18 and KMS28BM were treated with PKCα agonists PMA and PDH for 24 hours and the mRNA levels of HK2 was determined using qRT-PCR (three biological repeats with technical duplicates). 4α-Phorbol 12,13-didecanoate (4αPDD) is an inactive compound with similar structure to PMA and PDH served as a negative control. Paired Student t test was used to determine significance and corrected for multiple comparisons by the Benjamini–Hochberg method. *, P < 0.05. D, Hexokinase activity and glucose-6-phosphate levels were measured using colorimetric method (Abcam) at 450 nm using a microplate reader in PKCα agonist–treated cells (n = 3 biological repeats). Treatment was compared with DMSO control and significance was determined by Student t test, *, P < 0.05. E and F, Overexpression of PKCα could rescue hexokinase activity (E) and glucose-6-phosphate (F) levels in NSD2 knockdown cells (three biological repeats). Pair-wise comparison between cells transfected with shNSD2 and/or PKCα overexpression plasmid with KMS18 control using Student t test, *, P < 0.05; **, P < 0.01, and corrected for multiple comparisons by the Benjamini–Hochberg method. G, Cells were treated with increasing doses of PI3K inhibitor, LY294002 (IC50 ∼10 μmol/L), for 24 hours and HK2 levels were determined using qRT-PCR (three biological repeats with technical duplicates) and Western blot analysis (n = 2 biological repeats). iPI3K-treated samples were compared with DMSO using Student t test, *, P < 0.05; **, P < 0.01, and corrected for multiple comparisons by the Benjamini–Hochberg method. pAKT and total AKT were probed to measure the drug effect. β-Actin was used as loading control. H, Mitochondria and total levels of HK2 were determined by Western blot analysis (n = 2 biological repeats) in PKCα or NSD2 knockdown KMS18 and KMS28BM cells, with VDAC as mitochondria fraction marker and GAPDH as cytoplasmic fraction marker. The ratio of total HK2 to cytoplasmic HK2 was determined using ImageJ and is indicated in the blot. I, KMS28BM Control shscr or shPKCα cells were treated with increasing concentrations of 3BP, a hexokinase inhibitor. Twenty-four hours after transfection with shRNA, the cells were reseeded and treated with increasing concentrations of 3BP (DMSO, 5, 10, 15, 25 μmol/L) and viability was determined at 8 hours after treatment using CTG assay (three biological repeats with technical octuplicates). Significant differences determined by Student t test. **, P < 0.01.

Figure 4.

PKCα regulates hexokinase 2 expression and activity in t(4;14) cells. A, PKCα knockdown was performed and qRT-PCR (24 hours) and Western blot analysis (48 hours) was used to determine HK2 levels. β-actin was used as loading control. Analysis from three biological repeats for qRT-PCR and representative images from two biological repeats for Western blot analysis. B, Overexpression of NSD2 in the presence and absence of PKCα shRNA was performed to determine HK2 levels using Western blot analysis at 48 hours posttransfection (n = 2 biological repeats). C, KMS18 and KMS28BM were treated with PKCα agonists PMA and PDH for 24 hours and the mRNA levels of HK2 was determined using qRT-PCR (three biological repeats with technical duplicates). 4α-Phorbol 12,13-didecanoate (4αPDD) is an inactive compound with similar structure to PMA and PDH served as a negative control. Paired Student t test was used to determine significance and corrected for multiple comparisons by the Benjamini–Hochberg method. *, P < 0.05. D, Hexokinase activity and glucose-6-phosphate levels were measured using colorimetric method (Abcam) at 450 nm using a microplate reader in PKCα agonist–treated cells (n = 3 biological repeats). Treatment was compared with DMSO control and significance was determined by Student t test, *, P < 0.05. E and F, Overexpression of PKCα could rescue hexokinase activity (E) and glucose-6-phosphate (F) levels in NSD2 knockdown cells (three biological repeats). Pair-wise comparison between cells transfected with shNSD2 and/or PKCα overexpression plasmid with KMS18 control using Student t test, *, P < 0.05; **, P < 0.01, and corrected for multiple comparisons by the Benjamini–Hochberg method. G, Cells were treated with increasing doses of PI3K inhibitor, LY294002 (IC50 ∼10 μmol/L), for 24 hours and HK2 levels were determined using qRT-PCR (three biological repeats with technical duplicates) and Western blot analysis (n = 2 biological repeats). iPI3K-treated samples were compared with DMSO using Student t test, *, P < 0.05; **, P < 0.01, and corrected for multiple comparisons by the Benjamini–Hochberg method. pAKT and total AKT were probed to measure the drug effect. β-Actin was used as loading control. H, Mitochondria and total levels of HK2 were determined by Western blot analysis (n = 2 biological repeats) in PKCα or NSD2 knockdown KMS18 and KMS28BM cells, with VDAC as mitochondria fraction marker and GAPDH as cytoplasmic fraction marker. The ratio of total HK2 to cytoplasmic HK2 was determined using ImageJ and is indicated in the blot. I, KMS28BM Control shscr or shPKCα cells were treated with increasing concentrations of 3BP, a hexokinase inhibitor. Twenty-four hours after transfection with shRNA, the cells were reseeded and treated with increasing concentrations of 3BP (DMSO, 5, 10, 15, 25 μmol/L) and viability was determined at 8 hours after treatment using CTG assay (three biological repeats with technical octuplicates). Significant differences determined by Student t test. **, P < 0.01.

Close modal

PKCα stimulates the proliferative pathways MAPK and PI3K to drive oncogenesis (24). To determine which of these pathways is the underlying mechanism for HK2 regulation, we treated the cells with LY294002 (iPI3K) or SCH772984 (iERK), and only PI3K inhibition resulted in a blockade of HK2 expression (Fig. 4G; Supplementary Fig. S3D). Consistent with this, PMA-mediated activation of PI3K could rescue the expression of HK2 during the loss of NSD2 (Supplementary Fig. S3E). Despite previous reports of HK2 promoter methylation in certain cancer cell types (26), treatment with 5-azacytidine indicated that HK2 was not regulated by DNA methylation in t(4;14) cells (Supplementary Fig. S3F).

Akt is the downstream substrate of PI3K and was reported to directly phosphorylate and stabilize HK2 to the outer mitochondrial membrane (27). This binding of HK2 promotes the access to mitochondrially generated ATP and plays an important function of integrating glycolysis with oxidative phosphorylation (28). Hence, this prompted us to isolate the mitochondrial fraction for determination of mitoHK2 levels, and we observed that knockdown of either PKCα or NSD2 impaired the mitochondrial localization of HK2 (Fig. 4H). PKCα-mediated regulation of HK2 also constituted a metabolic dependency where cells with higher PKCα or t(4;14) status were more susceptible to 3-bromopyruvate (3-BP; refs. 29, 30) treatment (Fig. 4I; Supplementary Fig. S3G). Altogether, these data confirms that NSD2 regulates HK2 by activating PI3K/Akt signaling through PKCα in t(4;14) cells.

A global reduction in cellular bioenergetic pathways were observed in PKCα-deficient cells

To determine whether PKCα expression is linked to the bioenergetic requirements of t(4;14) myeloma cells, we measured the basal metabolic phenotypes and the cellular steady-state ATP generation using Seahorse Assay. Cells with stable knockdown of PKCα exhibited a reduction in basal extracellular acidification rate (ECAR) and OCR (Fig. 5A), coupled with an approximately 40% decrease in both glycolysis and mitochondrial-derived ATP (Fig. 5B). To further understand how energy production was perturbed by loss of PKCα, we utilized the glycolysis stress test. The lowered glycolytic capacity was exemplified by a minimal increase in ECAR when respiration was blocked with the addition of oligomycin (Fig. 5C). Similar suppression on glycolysis was observed with the depletion of NSD2 (Supplementary Fig. S4A and S4B). To comprehensively characterize the metabolic shifts mediated by PKCα, we utilized a metabolomics-based approach that allowed us to analyze the levels of 116 metabolites (Supplementary Table S1). Significant reduction in metabolites such as the glycolytic end-product lactate, TCA cycle metabolites citrate, cis-aconitate, and isocitrate, as well as reduction in amino acid (aa) synthesis (including ketogenic, branched-chain amino acids and aromatic amino acids) were seen with PKCα knockdown (Fig. 5DG). In addition, metabolic parameters that indicate the energy or redox status demonstrated a lowered NADH/NAD+ ratio and Malate/Asp shuttle, confirming that loss of PKCα affects the central carbon metabolism (Supplementary Fig. S5A and S5B). The changes in glycolysis-associated metabolites were consistent with an effect mediated by HK2, and identified lactate as a major metabolite mediated by PKCα (Fig. 5F).

Figure 5.

PKCα knockdown affects glycolysis in t(4;14) cells. A, The basal OCR (pmol/minute/150,000 cells) and ECAR (mpH/minute/150,000 cells) rates were used to plot an energy map of KMS28BM shscr control versus PKCα with equal seeding cell number (n = 2 biological repeats). B, Glycolytic- and mitochondria-derived ATP levels were measured in KMS28BM control versus PKCα using inhibitors oligomyin and Rot/AA in the Seahorse assay and plotted as the total rate of ATP production (pmol/minute). Two independent repeats, Student t test, *, P < 0.05. C, Seahorse assay was used to measure ECAR in real time to determine glycolytic function in KMS28BM PKCα stable knockdown cells using a Seahorse XFe24 metabolic analyzer (n = 2 biological repeats). Student t test *, P < 0.05; n.s., no significance. The metabolic inhibitors used are labeled in the graph. Values were normalized for 150,000 cell number with cell counting per well before the metabolic stress tests. D, Metabolome profile of KMS28BM shscr and PKCα knockdown by CE-TOFMS analysis that measures the absolute abundances of 116 metabolites and is annotated on the basis of the HMT metabolite database (n = 2 biological repeats). E, The significant metabolites were used to plot pathway enrichment using Metaboanalyst 5.0. The top pathways are highlighted. P value for glycolysis/gluconeogenesis pathway is 7.2511E-4. F, The absolute concentration of metabolites from the glycolysis pathway was plotted using Graphpad Prism. Paired t test was performed to compare the means between isogenic groups and Benjamini–Hochberg correction method was applied for comparisons of multiple metabolites. The significance of lactate was also reconfirmed with Mann–Whitney U test; P = 0.0017. G, The absolute metabolite levels involved in pathways identified in E are individually plotted. *, P < 0.05; **, P < 0.01; Student t test.

Figure 5.

PKCα knockdown affects glycolysis in t(4;14) cells. A, The basal OCR (pmol/minute/150,000 cells) and ECAR (mpH/minute/150,000 cells) rates were used to plot an energy map of KMS28BM shscr control versus PKCα with equal seeding cell number (n = 2 biological repeats). B, Glycolytic- and mitochondria-derived ATP levels were measured in KMS28BM control versus PKCα using inhibitors oligomyin and Rot/AA in the Seahorse assay and plotted as the total rate of ATP production (pmol/minute). Two independent repeats, Student t test, *, P < 0.05. C, Seahorse assay was used to measure ECAR in real time to determine glycolytic function in KMS28BM PKCα stable knockdown cells using a Seahorse XFe24 metabolic analyzer (n = 2 biological repeats). Student t test *, P < 0.05; n.s., no significance. The metabolic inhibitors used are labeled in the graph. Values were normalized for 150,000 cell number with cell counting per well before the metabolic stress tests. D, Metabolome profile of KMS28BM shscr and PKCα knockdown by CE-TOFMS analysis that measures the absolute abundances of 116 metabolites and is annotated on the basis of the HMT metabolite database (n = 2 biological repeats). E, The significant metabolites were used to plot pathway enrichment using Metaboanalyst 5.0. The top pathways are highlighted. P value for glycolysis/gluconeogenesis pathway is 7.2511E-4. F, The absolute concentration of metabolites from the glycolysis pathway was plotted using Graphpad Prism. Paired t test was performed to compare the means between isogenic groups and Benjamini–Hochberg correction method was applied for comparisons of multiple metabolites. The significance of lactate was also reconfirmed with Mann–Whitney U test; P = 0.0017. G, The absolute metabolite levels involved in pathways identified in E are individually plotted. *, P < 0.05; **, P < 0.01; Student t test.

Close modal

Modulation of lactate by PKCα conferred resistance to lenalidomide in a cereblon-independent manner

Metabolism-associated mechanisms of drug resistance have been increasingly identified in multiple myeloma (16–18). To extend this observation, we wondered whether metabolic adaptations driven by PKCα can modulate therapeutic efficacy toward the three classes of novel agents: proteasome inhibitor (bortezomib), immunomodulatory drug (lenalidomide), and anti-CD38 antibody (daratumumab). We found that PKCα expression is a determinant of the differential response toward lenalidomide, but not bortezomib or daratumumab (Fig. 6A; Supplementary Fig. S6A and S6B). This modulation of lenalidomide sensitivity can be phenocopied with either NSD2 or HK2 abrogation (Supplementary Fig. S6C). As cereblon abnormalities are a well-established mechanism of lenalidomide resistance (31–33), we next examine whether PKCα operated via a cereblon-dependent pathway. We did not observe any changes in the expression of cereblon, including its downstream targets B-cell transcription factor Ikaros and cell survival factor IRF4 (Fig. 6B), which led us to pursue whether PKCα might contribute to lenalidomide resistance through a HK2-mediated glycolytic flux. To test this hypothesis, we performed a rescue experiment where we overexpressed HK2 in PKCα knockdown cells, which restored lenalidomide insensitivity in t(4;14) cells, demonstrating the involvement of HK2 in lenalidomide resistance (Fig. 6C). Consistent with this, we found that knockdown of HK2 activates apoptotic cell death, indicating that HK2 partly supports PKCα-mediated myeloma cell viability (Supplementary Fig. S7A–S7H).

Figure 6.

PKCα-overexpressing cells are resistant to lenalidomide through high lactate levels. A, Control or shPKCα cells were treated with increasing concentrations (6.25, 12.5, 25, 50, or 100 μmol/L) of lenalidomide, and viability was measured using CTG assay at 144 hours (three biological repeats with technical octuplicates). IC50 values were generated using Graphpad Prism and Student t test indicates significance at 100 μmol/L between control and shPKCα cells. *, P < 0.05. B, Control shscr or shPKCα cells were treated with 10 or 50 μmol/L of lenalidomide for 48 hours and probed with the indicated antibodies (n = 2 biological repeats). C, PKCα stable knockdown cells were transiently transfected with HK2 overexpression plasmid and treated with 50 or 100 μmol/L of lenalidomide for 144 hours in KMS18 and KMS28BM cells to determine viability. Three biological repeats with technical octuplicates. *, P < 0.05; Student t test. D, Intracellular lactate was determined using Lactate-Glo assay (Promega) and luminescence readings were taken in KMS18 and KMS28BM stable PKCα knockdown cells. Three biological repeats. *, P < 0.05; Student t test. E, KMS18 cells were transiently transfected with PKCα overexpression plasmid for 48 hours, followed by measurement of intracellular lactate levels (n = 3 biological repeats) and Western blot analysis (n = 2 biological repeats). *, P < 0.05; Student t test. F, KMS18 and KMS28BM cells were cultured without or with the addition of 10 mmol/L L-lactate (+Lac) and the effect of 50 or 100 μmol/L lenalidomide was determined by viability at 144 hours. Three biological repeats with technical octuplicates. Student t test; *, P < 0.05; **, P < 0.01. G, The IC50 of lenalidomide was plotted against intracellular lactate readings and Pearson correlation was used to determine the linear relationship between the two variables in ten myeloma cell lines RPMI8226, U266, H929, KMS34, KMS12BM, OPM2, XG6, MM1.S, KMS28BM, and KMS18. H, Nine t(4;14) patient plasma samples and eleven non-t(4;14) samples were measured for lactate levels (10×dilution) and unpaired t test was used to determine significant difference between the two groups. I, Three patient-derived plasma samples that were on lenalidomide-based treatment were thawed and centrifuged on 8,000 to 10,000 × g, 5 to 10 minutes before the supernatant was 10 to 100× diluted for lactate measurement with Lactate Glo Assay (Promega). P001 and P002 are normal karyotypes, while P003 is t(4;14) cytogenetic.

Figure 6.

PKCα-overexpressing cells are resistant to lenalidomide through high lactate levels. A, Control or shPKCα cells were treated with increasing concentrations (6.25, 12.5, 25, 50, or 100 μmol/L) of lenalidomide, and viability was measured using CTG assay at 144 hours (three biological repeats with technical octuplicates). IC50 values were generated using Graphpad Prism and Student t test indicates significance at 100 μmol/L between control and shPKCα cells. *, P < 0.05. B, Control shscr or shPKCα cells were treated with 10 or 50 μmol/L of lenalidomide for 48 hours and probed with the indicated antibodies (n = 2 biological repeats). C, PKCα stable knockdown cells were transiently transfected with HK2 overexpression plasmid and treated with 50 or 100 μmol/L of lenalidomide for 144 hours in KMS18 and KMS28BM cells to determine viability. Three biological repeats with technical octuplicates. *, P < 0.05; Student t test. D, Intracellular lactate was determined using Lactate-Glo assay (Promega) and luminescence readings were taken in KMS18 and KMS28BM stable PKCα knockdown cells. Three biological repeats. *, P < 0.05; Student t test. E, KMS18 cells were transiently transfected with PKCα overexpression plasmid for 48 hours, followed by measurement of intracellular lactate levels (n = 3 biological repeats) and Western blot analysis (n = 2 biological repeats). *, P < 0.05; Student t test. F, KMS18 and KMS28BM cells were cultured without or with the addition of 10 mmol/L L-lactate (+Lac) and the effect of 50 or 100 μmol/L lenalidomide was determined by viability at 144 hours. Three biological repeats with technical octuplicates. Student t test; *, P < 0.05; **, P < 0.01. G, The IC50 of lenalidomide was plotted against intracellular lactate readings and Pearson correlation was used to determine the linear relationship between the two variables in ten myeloma cell lines RPMI8226, U266, H929, KMS34, KMS12BM, OPM2, XG6, MM1.S, KMS28BM, and KMS18. H, Nine t(4;14) patient plasma samples and eleven non-t(4;14) samples were measured for lactate levels (10×dilution) and unpaired t test was used to determine significant difference between the two groups. I, Three patient-derived plasma samples that were on lenalidomide-based treatment were thawed and centrifuged on 8,000 to 10,000 × g, 5 to 10 minutes before the supernatant was 10 to 100× diluted for lactate measurement with Lactate Glo Assay (Promega). P001 and P002 are normal karyotypes, while P003 is t(4;14) cytogenetic.

Close modal

Intracellular lactate exploits prosurvival and antiapoptotic pathways such as NF-κB and Bcl2 (34–36), while secretion of lactate promotes an immunosuppressive microenvironment that is antitumor immunity and unfavorable for normal cells (37–39). As an upstream regulator of HK2, we sought to determine the effects of PKCα on both intracellular and extracellular lactate levels. Our data demonstrates that silencing of PKCα primarily decreases intracellular lactate therefore affecting the efflux of extracellular lactate, and this reaction can be reversed in ectopically expressed PKCα (Fig. 6D and E). To mimic the effect of lactate on lenalidomide resistance, we cultured KMS18 and KMS28BM cells in medium supplemented with sodium l-lactate, which led to a corresponding increase in cytosolic lactate detected after one hour of incubation (Supplementary Fig. S8). Lactate supplementation partly rescues the inhibitory effects of lenalidomide, resulting in survival adaptation of myeloma cells (Fig. 6F). This trend was confirmed on a broader scale where we correlated intracellular lactate levels with IC50 of lenalidomide in a panel of myeloma cell lines. We discovered that cell lines with high lactate production were less vulnerable to lenalidomide, although this can be independent of subtype (Fig. 6G). To demonstrate the clinical relevance of our findings, we measured lactate levels from t(4;14) versus non-t(4;14) patient-derived plasma samples, which was significantly elevated in the t(4;14) group (Fig. 6H). Among them, a t(4;14) patient had high levels of lactate and demonstrated disease progression on lenalidomide-based treatment, while two patients with normal karyotype had low levels of lactate and achieved partial response with lenalidomide (Fig. 6I; Supplementary Fig. S9). Overall, we demonstrated a novel mechanism of lenalidomide resistance associated with metabolic alterations and not due to moderation of the cereblon-Ikaros-IRF4 pathway.

Recurrent translocation-driven metabolic heterogeneity is important for the understanding of how cancer cells with specific cytogenetic abnormalities gain growth and survival advantage using metabolically-derived ATP and biosynthetic precursors (40). Such heterogeneous metabolic preferences often contribute to the acquisition of resistance against antimyeloma therapies. In this aspect, elucidating single-driver molecular events causing altered metabolism specific to translocated multiple myeloma is lacking. Furthermore, altered epigenetic landscape is known to contribute to metabolic plasticity, and this rationale led us to metabolically characterize NSD2-driven t(4;14) multiple myeloma. Here, we documented that elevated aerobic glycolysis is a feature of t(4;14)-translocated cells driven by NSD2-mediated metabolic rewiring that relies upon its epigenetic activation of PKCα. We integrated a multiomics approach using metabolome profiling to characterize the metabolic phenotype and RNA-seq to identify key metabolic genes as a means to uncover novel associations involved in the distinct reprogramming of t(4;14) metabolism. Mechanistically, we demonstrated a functional downstream PKCα–HK2–lactate pathway that serves to promote myeloma cell viability and lactate-driven IMiDs resistance, offering new insights into the epigenetic and metabolism cross-talk in myeloma.

It is now increasingly recognized that myeloma cells harbored different metabolic liabilities, and multiple proteins implicated in glycolysis or oxidative phosphorylation have successively became potential therapeutic targets amenable for clinical application (10, 19, 41–47). Although genetic mutations remain an attributing factor to cancer metabolism, many metabolic enzymes are reported to be under epigenetic control through histone modifications such as DNA and histone methylation (48). A notable example will be the facilitated expression of HK2 by promoter demethylation during tumor progression, which can be further enhanced by stimuli from HIF1α and glucose (27, 49). Fructose 1,6-bisphosphatase 1 (FBP1) is a negative regulator of the glycolytic pathway and is silenced by promoter hypermethylation in carcinomas (50). Histone methyltransferase G9A epigenetically activates the serine-glycine biosynthetic pathway through H3K9 monomethylation (51). Hence, dysregulation of histone methylation activities has an important role in mediating cellular metabolome through transcription of metabolic enzymes by affecting chromatin accessibility.

NSD2 is essential for transcriptional activation by catalyzing the transfer of dimethyl groups to lysine 36 residue on histone H3 proteins (H3K36me2), an epigenetic mark that is associated with active chromatin. There are evidence implicating NSD2 as an initiating factor in various aspects of metabolism. In human primary fibroblast cells, knockdown of NSD2 sees an increase in mitochondria oxidative phosphorylation (52), which is a frequent compensatory mechanism triggered by a reduction in glycolysis. In tamoxifen-resistant breast cancer, NSD2 is overexpressed and directly binds to multiple gene loci involved in glucose metabolism, such as HK2 and G6PD, directly promoting their expression via its methylase activity (53). However, we did not find evidence of direct NSD2 activity on HK2 promoter, rather, the regulation was through another substrate, PKCα. This is not surprising as many cancer cells that exhibit persistently high glycolysis were linked to aberrant signaling of oncogenes, such as c-Myc and Ras (54, 55). We found that PKCα sustained the expression of HK2 through its downstream PI3K–AKT signaling axis, which not only acts to upregulate HK2 expression, but also promotes AKT-mediated mitochondrial translocation of HK2 (26, 56). This localization is advantageous to cancer cells as HK2 can access mitochondrially derived ATP as well as limit negative autoregulation feedback. In addition to the major role of HK2 in glycolytic overload and unscheduled glycolysis (57), HK2 has an equally important function in antiapoptosis through inhibition of cytochrome c release by competing with Bax for binding to mitochondria (58, 59). Here, we similarly found that silencing of HK2 could impede cell viability paralleled with apoptotic cell death in t(4;14) myeloma. Essentially, the activation of HK2 can be considered a metabolic convergence in multiple myeloma as cyclin D1 dysregulation in t(11;14) subtype was also found to increase HK2 levels (60).

A key feature of increased metabolic flux through glycolysis is the production of abnormally high levels of lactate. Lactate was initially thought to be a metabolic waste product, but has now emerged as an important player in malignancy and adaptive multidrug resistance (35, 61, 62). Furthermore, increased lactate production is likely to couple with lactate release, fueling a “reverse Warburg effect” in the microenvironment-residing cancer-associated fibroblasts as a concerted manner to further acquire genetic resistance mechanisms (63, 64). Among the novel agents used clinically in multiple myeloma regimen, the efficacy of IMiDs partly relies on the recruitment of antitumor immune cells. Understandably, accumulation of lactate sets an acidic microenvironment that counteracts the action of mechanism of this class of drugs and warrant further studies. Although cereblon abnormalities is the predominant mechanism of lenalidomide resistance (31–33, 65), others have reported the involvement of HIF1α and MCT1 to hint at the importance of metabolic adaptations in the establishment of IMiDs resistance (66–68), in agreement with our current work. Although we demonstrated that HK2 contributed to the dysregulation of lactate, we do not overlook the possibility that aberrations in other glycolytic enzymes such as lactate dehydrogenase (LDH) and monocarboxylate transporters (MCT) can also lead to lactate-associated drug resistance (61). This was indeed reflected in our myeloma cell line correlation where non-t(4;14) cell lines with elevated intracellular lactate could also display IMiDs insensitivity.

Metabolomics hold immense potential for bench to bedside applications in therapeutics and diagnostics (68). Metabolic intervention for myeloma treatment can be achieved through either dietary restrictions that aims to reduce the blood concentration of the metabolite driving tumor growth or pharmacologic approach as a more specific method to target a particular metabolic gene or pathway. In addition, healthy individuals and patients harbor significantly different metabolite signatures, making it possible for metabolite concentrations in the plasma or serum to act as biomarkers for prognosis or drug response prediction, with the advantage of generating less invasive diagnostic tests (69). Overall, our metabolic characterization of t(4;14) myeloma highlights the importance toward identifying metabolic dependency and helps to establish a metabolomic framework that can be easily extended to characterize the metabolic profiles of other genetically high-risk multiple myeloma subtypes.

No disclosures were reported.

P. Chong: Conceptualization, data curation, software, formal analysis, supervision, funding acquisition, validation, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. J. Chooi: Validation, investigation, methodology. J.S. Lim: Resources, investigation, methodology. A.C. Leow: Investigation, methodology. S.H. Toh: Investigation, methodology. I. Azaman: Investigation, methodology. M. Koh: Investigation, methodology. P. Teoh: Investigation, methodology. T. Tan: Data curation, software, visualization. T. Chung: Data curation, software, formal analysis, visualization. W.J. Chng: Conceptualization, supervision, funding acquisition, project administration, writing–review and editing.

The authors thank Dr. Yap Lai Lai from NUS Biochemistry for Seahorse machine, reagents, and analysis assistance. The authors thank Dr. Xie Zhigang for his involvement in the conceptualization of the project. W.J. Chng is supported by NMRC Singapore Translational Research (STaR) Investigatorship. This research is partly supported by the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centers of Excellence initiative as well as the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education's Tier 3 grants, grant number MOE2014-T3–1-006. This research is also funded by the National Medical Research Council (NMRC) Clinician Scientist-Individual Research Grant (CS-IRG), CIRG20nov-0019 (to W.J. Chng).

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

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