KMT2D exerts tumour suppressive activities in pancreatic cancer

In order to define the role of KMTs and KDMs in pancreatic carcinogenesis, we initially determined the levels of these enzymes in tumours (n=14) and adjacent controls (n=8) and found different members of the KDM family (KDM2A, KDM4C, KDM5B, KDM8), SETD family (SETDB2, SETD6), and the SUV420H1 and KMT2D to be differentially expressed (online supplementary table S1). As shown in figure 1A and online supplementary figure S1A, statistically significant and consistent deregulation of gene expression was observed only for KMT2D in three independent patient cohorts. We further investigated the differential KMT2D expression based on previously published gene expression array data listed in the Oncomine database. Notably, KMT2D mRNA expression also displays significantly decreased levels based on two independent studies4 5 (online supplementary figure S1B,C). The expression of other H3K4 methyltransferase genes sharing similar structural domains with KMT2D was also measured. As shown in online supplementary figure S1D,E, no significant changes were observed in the mRNA levels of neither KMT2B nor KMT2C in the human pancreatic cancer samples used in the current study.

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Figure 1

Histone methyltransferase KMT2D acts as a tumour suppressor in pancreatic cancer. (A) KMT2D mRNA expression levels in three cohorts of pancreatic cancer and normal tissues, as assessed by RT-qPCR. (B) Transient suppression of KMT2D expression by two different siRNAs in pancreatic cancer cells. Efficiency of KMT2D downregulation, as assessed by RT-qPCR (left panel) and by IB analysis (right panel). Whole cell protein extracts were analysed by IB analysis for total KMT2D or CREB (used as a loading control). Numbers in parentheses denote the average fold change of the KMT2D:CREB total protein ratio of siKMT2D transiently transfected cells compared with siC#1-treated cells (set as default 1) of at least two independent experiments, as assessed by densitometric analysis of the immunoreactive bands. (C) Dynamic monitoring of cellular proliferation on KMT2D transient suppression by using siKMT2D#2, using the xCELLigence RTCA SP system. (D) Assessment of KMT2D expression levels in MIA PaCa-2 cells transfected with #2–21 shRNA against KMT2D that underwent clonal selection resulting in clones a, b and c, by RT-qPCR (left panel) and IB analysis (right panel). Numbers in parentheses denote the average fold change of the KMT2D:CREB total protein ratio of shKMT2D stably transfected cells compared with mock-treated cells (set as default 1), as assessed by densitometric analysis of the immunoreactive bands. (E) Dynamic monitoring of the proliferation of the shKMT2D#2-21a clonal pancreatic cancer cell lines versus mock-transfected cells. (F) Effect of KMT2D stable suppression on soft agar colony formation. (G) Representative images of the excised tumours and (H) tumour volume (mm³) graphs of xenografts bearing KMT2D stably suppressed MIA PaCa-2 and CAPAN-2 cells (n=5 mice per group). For establishing shKMT2D#2-21a xenografts, MIA PaCa-2 or CAPAN-2 cells were injected subcutaneously in the right flank of NOD-SCID mice (five mice/group). (I) Representative IHC images for the proliferation marker Ki-67, corresponding to MIA PaCa-2 (upper two rows) and CAPAN-2 (bottom two rows) xenografts from mice injected with mock or shKMT2D#2-21a cells. Scale bars represent 50 µm. siC#1, cells transfected with a negative control scramble siRNA; siKMT2D#1, cells transfected with siRNA#1 for KMT2D; siKMT2D#2, cells transfected with siRNA#2 for KMT2D; mock, cells transfected with shRNA empty vector; shKMT2D#2–21, cells transfected with #2–21 shRNA for KMT2D; shKMT2D#2-21a, b or c, cells transfected with #2–21 shRNA for KMT2D that underwent clonal selection resulting in clones a, b and c; MX, mouse xenograft. OD, optical density. Statistical analyses were performed using one-way analysis of variance. Asterisks denote statistically significant differences, *p<0.05, **p<0.01, ***p<0.001. IB, immunoblot; IHC, immunohistochemical; KMT2D, histone lysine (K)-specific methyltransferase 2D; RT-qPCR, reverse transcription quantitative PCR; shRNA, short hairpin RNA; siRNA, small interfering RNA.

Based on the above findings, we postulated the hypothesis that KMT2D might display tumour suppressive properties in pancreatic cancer. To address this hypothesis, we performed a series of in vitro and in vivo experiments where KMT2D was genetically inactivated, as evidenced in figure 1B. Transient downregulation of KMT2D by using two different siRNAs promoted cellular proliferative capacity (figure 1C, online supplementary figure S1F,G and online supplementary tables S2–S3). To confirm the biological significance of KMT2D, pancreatic cancer cells stably suppressed of KMT2D expression were established (online supplementary figure S1H) and tested for their proliferative capacity (online supplementary figures S1I–L). The most efficient shRNA-transfected cell population (#2–21) was subjected to clonal selection and used for further studies. Reverse transcription quantitative-PCR (RT-qPCR) and immunoblot (IB) analysis were used to verify the lower levels of KMT2D mRNA and protein in the clonal cell lines (figure 1D). Similar to transient knockdowns, stably decreased KMT2D expression led to increased MIA PaCa-2 proliferation (figure 1E, online supplementary figure S1M,N and supplementary tables S4–S5) and cell anchorage-independent growth (figure 1F and online supplementary figure S10).

To further validate our hypothesis, the effects of KMT2D inhibition of expression were investigated in nude mice bearing human pancreatic cancer xenografts. Significant induction of tumour growth rate and weight was observed (figure 1G,H and online supplementary figure S1P,Q), and lower KMT2D mRNA levels were confirmed by RT-qPCR and immunohistochemical (IHC) analysis (supplementary figure S1R,S) in the excised tumours. Consistent with these observations, histopathological analysis of tumours from different mice at the endpoint revealed high proliferation rates, as evidenced by Ki-67 immunostaining (figure 1I). Taken together, these in vitro and in vivo results point towards a tumour suppressive role of KMT2D in pancreatic cancer.

Site-specific DNA methylation associates with KMT2D transcriptional repression

We next sought to determine the mechanism underlying KMT2D decreased expression in pancreatic cancer. To this end, we designed a complementary tissue-based and cell-based experimental approach for the evaluation of KMT2D transcriptional repression dependency on DNA methylation, as depicted in figure 2A. We investigated DNA methylation patterns of KMTs and KDMs in 20 pancreatic cancer tissues compared with 13 adjacent control tissues by using the Infinium Human Methylation 450 Bead ChIP Array. KMT2D, together with other four of these enzymes, was found to be significantly differentially methylated (at least over 25%) in pancreatic tumours compared with normal tissues (figure 2B and online supplementary table S6). Specifically, methylation of two individual CpG motifs was observed at nucleotides (nt) −29 and +45, relatively to the KMT2D transcription start site (TSS). By integrating the data originating from CpG methylation genome-wide analysis with gene expression profiling in cancerous versus normal pancreas, KMT2D was simultaneously identified to be aberrantly hypermethylated and downregulated. Whole exome sequencing in the pancreatic cancer cell lines used in the current study (online supplemetary table S7), as well as published data from other groups, show that KMT2D harbours deletions and mutations in pancreatic ductal adenocarcinoma (PDAC); however, the actual frequencies of such mutations in PDAC are still debatable.6 7 In addition, the phenotypic consequences of such mutations have not yet been assessed in any disease setting. Given the above, we cannot rule out the possibility that genetic inactivation of KMT2D relies partly on both its mutational and/or CpG methylation status.

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Figure 2

Epigenetic regulation of KMT2D levels through DNA methylation of two CpG sites. (A) Experimental design for the evaluation of KMT2D transcriptional repression-dependency on CpG methylation in pancreatic cancer. (B) Heatmap of methylation beta values for the selected probes (p<0.05, mean difference ≥0.25). Wilcoxon rank-sum tests were conducted to compare methylation array data between patients with pancreatic cancer and healthy controls. (C) Validation of CpG methylation levels via targeted bisulfite sequencing for the selected ROI (chr12: 49448986–49449286). Quantitative methylation measurements at the single CpG site level for untreated or 5-AZA-CdR-treated cells are boxplotted (left panel) or depicted as heatmaps of the methylation ratio (right panel). The colour indicates the level of methylation from higher to lower in yellow > orange > red order. (D) Dose response evaluation of 5-AZA-CdR treatment for 48 hours in KMT2D mRNA levels, as assessed by RT-qPCR. (E) Effect of 5-AZA-CdR treatment for 48 hours on KMT2D protein levels, as assessed by IB analysis. Numbers in parentheses denote the average fold change of the KMT2D:CREB total protein ratio in 5-AZA-CdR-treated cells compared with 0.005% DMSO-treated cells (set as default 1). (F) Schematic depiction of the distinct KMT2D/hRluc constructs used; the engineered hRluc was coupled to the genomic region (positions −179 to +121) of the human KMT2D gene. Tick marks represent the number and location of CpG dinucleotides. (G) The efficiency of CpG methylation was assessed in unmethylated and methylated, linearised and gel-purified constructs by resistance to digestion with HpaII endonuclease and subsequently agarose gel analysis. (H, I) Relative hRluc activity after in vitro methylation of KMT2D constructs using the promoterless pGL4.82 (hRluc/Puro) vector gene system. HRluc mean fluorescence intensity was measured in MIA PaCa-2 cells transfected with either (H) untreated or (I) CpG methyltransferase (MSssl)-treated KMT2D/hRluc constructs. To control for transfection efficiency, cells were cotransfected with a plasmid containing firefly luciferase (Luc) reporter gene and the levels of hRluc fluorescence were averaged over all Luc expressing cells. Statistical analyses were performed using one-way analysis of variance. Asterisks denote statistically significant differences, *p<0.05, **p<0.01, ***p<0.001. 5-AZA-CdR, 5-AZA-2'-deoxycytidine; DMSO, dimethyl sulfoxide; IB,  immunoblot; KMT2D, histone lysine (K)-specific methyltransferase 2D; ROI, region of interest; RT-qPCR, quantitative reverse transcription PCR.

Next, targeted bisulfite sequencing was performed for the specified region comprising nt −179 to +122, in a panel of five pancreatic cancer cell lines treated with the DNA methyltransferase inhibitor 5-AZA-2'-deoxycytidine (5-AZA-CdR, Decitabine). Boxplots of DNA methylation levels indicate that drug-treated cells have an extensive hypomethylated pattern (figure 2C and online supplementary figure S2A). Further evidence on the impact of CpG methylation to repress KMT2D transcriptional activity was obtained from analysis of KMT2D mRNA levels on different exposure times and concentrations of the drug (figure 2D and online supplementary figure S2B). The most robust time-dependent and dose-dependent changes occurred in MIA PaCa-2 and AsPC-1 cell lines, an observation that was further validated by IB analysis (figure 2E). However, since this agent induces genome-wide demethylation, the observed increase in expression after treatment could be indirect. To eliminate this possibility, we designed a luciferase reporter assay for in vitro methylated KMT2D genomic region of interest (ROI) in wild-type or mutated state (figure 2F). Linearised constructs were in vitro methylated with high efficiency, as evidenced by resistance to digestion with HpaII methylation-specific endonuclease (figure 2G). As proof of concept, no significant differences were found among the promoter activity of unmethylated inserts (serving as positive controls), while in the totally unmodified methylated insert Renilla luciferase reporter (hRluc) gene activity was almost absent, thus confirming that CpG methylation controls transcriptional repression of KMT2D. Remarkably, both mutations applied in the ROI seem to derepress the hRluc activity of the in vitro methylated constructs by approximately 22-fold or 12-fold for Mut 1 and Mut 2, respectively (figure 2H,I). Taken together, our results identified that single-site CpGs methylation is critical for the suppression of KMT2D expression levels in pancreatic cancer.

KMT2D depletion promotes a metabolic shift to aerobic glycolysis through regulation of SLC2A3

Next, we investigated the mechanisms downstream of KMT2D underlying its tumour suppressor functions. KMT2D belongs to the complex of proteins associated with Set1 (COMPASS) family of H3K4 methyltransferases8; thus, we first examined the effects of KMT2D on global H3K4 methylation marks in pancreatic cancer cells. Using IB we showed that H3K4 monomethylated and dimethylated were slightly downregulated in the siKMT2D-treated cells, while the trimethylated form of H3K4 (H3K4me3) was significantly reduced (figure 3A). Furthermore, we tested the effect of 5-AZA-CdR treatment, as a means to overexpress KMT2D, on the H3K4me3 levels of pancreatic cancer cells. Consistent with the notion that H3K4 methylation is enriched on 5-Aza-dCdR treatment, potentially as a secondary event following the promoter demethylation and gene re-expression,9 10 we observed a remarkable enrichment of H3K4me3 methylation upon agent treatment. Interestingly, silencing of KMT2D in 5-Aza-dCdR-treated cells did not decrease H3K4me3 methylation levels as efficiently as in untreated cells, most possibly due to higher KMT2D levels (online supplementary figure S3A,B), while non-significant alterations in H3K4me1/2 levels were observed (online supplementary figure S3C). To complement these initial findings, ChIP-seq analysis for H3K4me3 and microarray gene expression profiling were performed in MIA PaCa-2 cells. ChIP-seq analysis showed significant decrease in H3K4me3 signals in KMT2D-silenced versus non-targeting control cells (figure 3B). The H3K4me3 peaks significantly overlap between the two different siRNAs used, as shown by the Venn diagram (figure 3C). H3K4me3 peaks were associated mostly with intergenic and intronic regions and were located around the TSS site (figure 3D,E). Marked loss of H3K4me3 peaks also occurs in loci essential for gene transcription as in the case of the general transcription factor IIA subunit 1 (GTF2A1), a component of the transcription machinery of RNA polymerase II that plays an important role in transcriptional activation (online supplementary figure S4). Gene ontology (GO) analysis of the H3K4me3 peaks found to be diminished on KMT2D silencing identified association with transcription-related pathways, with the highest enrichment score being observed for ‘transcription from RNA polymerase II promoter’ and ‘metabolic processes’  (figure 3F). Serine/Threonine kinase 11 (STK11) represents an example of KMT2D-mediated regulation of metabolic gene through reduction of H3K4me3 occupancy that was further experimentally validated as a direct regulatory target of KMT2D (online supplementary figure S5). Gene expression studies demonstrated 1035 genes to be downregulated, while a smaller subset of genes were upregulated on KMT2D suppression (figure 3G). We mapped the differentially regulated genes to well-established (‘canonical’) pathways by using the Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, http://www.ingenuity.com/). Energy-converting biochemical processes, primarily related to glucose and fatty acid (FA) metabolism, were distinguished as KMT2D-mediated cellular functions (figure 3H). Collectively, both the ChIP-seq and microarray studies point to the association of KMT2D expression with metabolism-related pathways.

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Figure 3

KMT2D-regulated pancreatic cancer cells’ transcriptome and epigenome. (A) Effects of KMT2D silencing on the global levels of histone H3K4 monomethylation, dimethylation  and trimethylation using IB analysis. Numbers in parentheses denote the average fold change of the H3K4me1, H3K4me2 or H3K4me3:H3 total protein ratio in KMT2D-silenced cells versus siC#1-treated cells (set as default 1). (B) Enrichment scores comparing the read density conditions for each set of peak regions are shown in the jittered boxplots. The enrichment values were tested with the Mann-Whitney U test (p<0.001). (C) Overlap of H3K4me3 peaks on KMT2D suppression using two different siRNAs. (D) Effect of KMT2D silencing on the genomic distribution of H3K4me3 peaks in MIA PaCa-2 cells. Pie chart indicates the enriched H3K4me3 peaks in negative siRNA versus siKMT2D#2-treated cells. (E) Distribution of H3K4 trimethylation mark around TSS. (F) GO terms associated with H3K4me3 binding sites were determined as follows: ChIP-seq peaks found in siC#1-treated cells versus peaks found in cells treated with siKMT2D#2 were associated with the nearest ENSEMBL transcript and processed using the DAVID (V.6.7) tool. The data presented are log-transformed p value of GO terms found to be enriched in the tested group of genes. (G) Heatmap showing the differentially expressed genes in MIA PaCa-2 cells on KMT2D suppression. Data were filtered using a p value cut-off of 0.05 and a fold change cut-off of 2.0. Clustering dendrograms show the relative expression values according to the following colouring scheme: red: high; black: moderate; green: low. (H) List of metabolism-associated canonical pathways derived from the IPA GO algorithms for the KMT2D-regulated genes (from figure 3G). −log (p value) is measured by the bar length, while R refers to the number of molecules from the data set that map to the pathway listed divided by the total number of molecules that map to the pathway from within the IPA knowledge base. (I) OCR/ECAR curves for KMT2D stably transfected pancreatic cancer cells (Seahorse Technology). R, ratio; oligo, oligomycin; FCCP, carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone; RM, rotenone/myxothiazol. ChIP-seq, chromatin immunoprecipitation-sequencing; ECAR, extracellular acidification rate; GO, Gene Ontology; IB, immunoblot; IPA, Ingenuity Pathway Analysis; KMT2D, histone lysine (K)-specific methyltransferase 2D; OCR, oxygen consumption rate;  siC#1, cells transfected with a negative control scramble siRNA; siKMT2D#1, cells transfected  with siRNA#1 for KMT2D; siKMT2D#2, cells transfected with siRNA#2 for KMT2D;  siRNA, small interfering RNA; TSS, transcription start site. 

To further explore the functional consequences of the metabolism-associated KMT2D target(s), we measured changes of nicotinamide adenine dinucleotide phosphate (NADPH), which represents a fundamental common mediator of various biological processes including energy metabolism.11 We found that cells harbouring low KMT2D levels accumulate higher NADPH levels in comparison with control cells (online supplementary figure S6A). Given this observation, we reasoned to assay cultured cells in real time using an XF24-3 Analyzer to query changes in the bioenergetic status of KMT2D-suppressed versus control pancreatic cancer cells. Simultaneous assessment of both oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in basal state showed enhanced relative contribution of glycolysis on KMT2D suppression (figure 3I and online supplementary figure S6B,C). In particular, significant increase in ECAR values was observed, while OCR seems to remain unaffected, thus augmenting cellular preference for aerobic glycolysis. By quantifying the activity of the two major energy-yielding pathways in the cell, mitochondrial respiration and glycolysis, we provide evidence of a clear discrimination in the metabolic profiles driven by loss of KMT2D expression. Furthermore, lactate production as well as glucose uptake were found to be significantly elevated on KMT2D silencing (online supplementary figure S6D–G).

In light of the above results, we hypothesised that KMT2D could influence glucose metabolism by controlling key glycolytic genes. Expression of the SLC2A1 (glucose transporter-1 (GLUT-1)) that typically correlates with the rate of cellular glucose metabolism was found to be merely upregulated (+1.25-fold change) in KMT2D-silenced cells (figure 4A) and was not significantly affected in the tumours originating from xenografts bearing KMT2D stably suppressed pancreatic cancer cells (online supplementary figure S7). Of note, solute carrier family 2 member 3 (SLC2A3) (also known as GLUT3) was distinguished as the top differentially expressed glycolysis-related gene on KMT2D downregulation (figure 4A–C and online supplementary figure S8). To rule out secondary effects related to the genetic status of SLC2A3 in the two different pancreatic cancer cell lines, we demonstrate that mostly common single nucleotide changes of SLC2A3 gene are observed, all of them ensuing synonymous substitutions (online supplementary table S8). Mapped DNA sequence reads from the ChIP-seq experiments mentioned above do not support the role of SLC2A3 as a direct KMT2D transcriptional target. Hence, we assessed whether KMT2D transcriptionally regulates SLC2A3 through an indirect effect.

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Figure 4

KMT2D regulates pancreatic cancer cell growth and metabolism by affecting SLC2A3 glucose transporter. (A) Heatmap summarising the differentially expressed glycolysis-related genes in MIA PaCa-2 cells on KMT2D suppression. Data were filtered using a p value cut-off of 0.05 and a fold change cut-off of 1.25. (B) SLC2A3 mRNA expression in pancreatic cancer cells on KMT2D silencing. (C) SLC2A3 expression in MIA PaCa-2 xenografts from mice injected with mock or shKMT2D#2-21a cells, as assessed by RT-qPCR. (D) Representative IB images for the activated and total (D) mTOR and (E) NF-κB p65 on KMT2D silencing. Numbers in parentheses denote the average fold change of the phosphorylated:total protein ratio of KMT2D-silenced cells versus siC#1-treated cells (set as default 1) of at least two independent experiments. (F) Representative IB images for SLC2A3, mTOR, Rictor and CREB protein levels on treatment of MIA PacA-2 cells harbouring differential KMT2D levels with siRNAs against mTOR, Rictor or the respective scramble control. Numbers in parentheses denote the average fold change of the SLC2A3:CREB total protein ratio of siRNA-treated cells versus siC#1-treated cells (set as default 1). (G) Treatment of MIA PaCa-2 cells with inhibitors of NF-κB activation for 24 hours reverses the KMT2D-mediated increase in SLC2A3 mRNA levels. Effects of SLC2A3 silencing on the (H) bioenergetic status, (I) proliferation and (J) colony formation ability of KMT2D-suppressed cells. Statistical analyses were performed using one-way analysis of variance. Asterisks denote statistically significant differences, *p<0.05, **p<0.01, ***p<0.001. DMSO, dimethyl sulfoxide; ECAR, extracellular acidification rate; FCCP, carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone; IB, immunoblot; KMT2D, histone lysine (K)-specific methyltransferase 2D; mechanistic target of rapamycin (mTOR);NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; OCR, oxygen consumption rate; OD, optical density; oligo, oligomycin; RM, rotenone/myxothiazol; RT-qPCR, reverse transcription quantitative PCR;siRNA; small interfering RNA; siC#1, cells transfected with a negative control scramble siRNA; siKMT2D#1, cells transfected with siRNA#1 for KMT2D; siKMT2D#2, cells transfected with siRNA#2 for KMT2D; siSLC2A3, cells transfected with siRNA for SLC2A3.

Tuberous sclerosis 1 (TSC 1), which acts as upstream negative regulator of mechanistic target of rapamycin (mTOR) activation,12 was found to be significantly downregulated based on the gene expression array employed in KMT2D-supressed cells (figure 3G). In addition, by pharmacological or genetic strategies, it has been demonstrated that rapamycin-sensitive mTOR complex 1, consisting of mTOR itself and the regulatory-associated protein of mTOR (Raptor), is involved in the regulation of SLC2A3 expression. Specifically, loss of the TSC1 induced mTOR hyperactivation and SLC2A3 overexpression through the activation of the IκB kinase/nuclear factor kappa-light-chain-enhancer of activated B cells (IKK/NF-κB) pathway.13 In another report, stimulation of NF-κB activity has also been shown to directly upregulate SLC2A3 gene transcription in mouse and human cells.14 Indeed, IB analysis shows that reduction of KMT2D expression leads to increased phosphorylation and thus activation of mTOR (figure 4D) and increased phosphorylation of NF-κB p65 in Ser 536 (figure 4E). On the other hand, the expression of REL-associated protein (p65) involved in NF-κB heterodimer formation, nuclear translocation and activation is not influenced on KMT2D genetic manipulation (online supplementary figure S9). Silencing of mTOR, but not Rictor (a specific component of mTORC2), as well as rapamycin treatment, reverse the SLC2A3 upregulation that is induced by low KMT2D levels (figure 4F and online supplementary figure S10). Furthermore, the use of inhibitors of NF-κB activation resulted in the partial reversal of SLC2A3 mRNA levels induction caused by KMT2D silencing (figure 4G). These data suggest that KMT2D suppression promotes SLC2A3 expression via activation of the mTOR/NF-kB axis. The immunostaining patterns of activated NF-κB p65 and SLC2A3 in tumours from xenografts bearing KMT2D stably suppressed or mock-transfected MIA PaCa-2 cells (online supplementary figure S11) validated our in vitro findings. Most importantly, treatment of pancreatic cancer cells with siRNA for SLC2A3 rescues the metabolic (figure 4H), proliferative (figure 4I and online supplementary tables S9–S10) and anchorage-independent growth phenotypes (figure 4J) that are associated with KMT2D suppression. Collectively, the functional features displayed by SLC2A3 reduction in KMT2D low-expressing cells highlight its role as a mediator of KMT2D-induced effects.

Polyunsaturated fatty acids are increased in KMT2D-suppressed cells and promote cell growth time-dependently

It is now considered that cancer cells support their increased growth rate by increasing either the uptake of exogenous lipids or their endogenous synthesis.15 Functional enrichment analysis (figure 5A), as well as bioinformatics prediction of the top-rated pathways and networks (figure 3G and online supplementary table S11) of differentially expressed genes in KMT2D-silenced cells, suggest that KMT2D expression has an impact on lipid metabolic processes. Genes found in the particular functional cluster are illustrated in figure 5B, and among those, fatty acid synthase (FASN), which encodes a multienzyme protein catalysing FA synthesis, is one of the top differentially regulated genes on KMT2D downregulation (online supplementary figure S12). Interestingly, we noticed that MIA PaCa-2 and CAPAN-2 bearing xenografts exhibit a clear trend of increased body weight, mainly at prolonged stages of tumour growth (figure 5C and online supplementary figure S13), even though these mice were possibly expected to be of poorer health given their increased tumour burden. However, the increase in the overall body weight of the mice most possibly relates to the increased weight of the tumours.

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Figure 5

KMT2D inhibition alters the lipid composition and cholesterol content in pancreatic cancer. (A) GO-based annotation was used to perform functional enrichment analysis using the DAVID (V.6.7) tool. Fold enrichment of genes (associated with lipid metabolic processes) regulated by KMT2D levels is measured by the bar length while p value represents the significance of the enrichment. (B) Heatmap summarising the differentially expressed lipid metabolism-related genes in MIA PaCa-2 cells on KMT2D suppression. Data were filtered using a p value cut-off of 0.05 and a fold change cut-off of 1.5. (C) Body weight graphs of mouse xenografts bearing MIA PaCa-2 or CAPAN-2 shKMT2D#2-21a clonal cell lines and mock-transfected cells (n=5 mice per group). (D) Column graph illustrating quantitative changes of the top 3 FAs regulated by KMT2D. (E) Time-dependent and dose-dependent effects of exogenously added FAs on cell proliferation, as assessed by CellTiter Glo Luminescence Cell Viability Assay. (F) Detection of cholesterol uptake within MIA PaCa-2 cultured cells, as assessed by fluorescence microscopy. Scale bars represent 50 µm. (G, H) Time-dependent effect of LDLR or SLC2A3 silencing, SC 26196 and SC 204990 inhibitors on cell proliferation in high and low KMT2D-expressing cells, as assessed by CellTiter Glo Luminescence Cell Viability Assay. Statistical analyses were performed using one-way analysis of variance. Asterisks denote statistically significant differences, *p<0.05, **p<0.01, ***p<0.001. FA, fatty acid; FBS, fetal bovine serum; GO, Gene Ontology; KMT2D, histone lysine (K)-specific methyltransferase 2D; LDLR, low-density lipoprotein receptor.

As a next step, we used mass spectrometry-based technology to assess quantitative changes in FA composition and cholesterol content as a response to KMT2D silencing. Specifically, the total number of lipid species identified accounts for 28 FAs, all of which displayed increased levels relative to control cells (table 1), with the most prominent differences being observed for the polyunsaturated fatty acids (PUFAs) docosadienoic, docosatrienoic and docosatetraenoic (table 1 and figure 5D).

Interestingly, addition of the aforementioned lipids in serum-free conditions resulted in increased MIA PaCa-2 cell growth in a time-dependent and dose-dependent manner (figure 5E) but did not affect pancreatic cancer cell’s invasive capacity (online supplementary figure S14). Furthermore, elevated cholesterol uptake was detected in MIA PaCa-2 cultured cells transiently transfected with an siRNA against KMT2D, as assessed by fluorescence microscopy (figure 5F). Subsequently, we explored whether cells harbouring low or high KMT2D levels differentially respond to perturbations in lipid homeostasis by (1) silencing the low-density lipoprotein receptor (LDLR), the main selective route of cholesterol-rich lipoprotein entrance into cancer cells; (2) blocking the ATP citrate lyase, a critical enzyme for de novo synthesis of a wide range of complex cellular lipids such as cholesterol and long-chain FAs using the SB 204990 inhibitor; and (c) blocking the Δ6 desaturase, the rate-limiting enzyme that initiates the metabolism of the n-6 and n-3 PUFAs, linoleic acid and α-linolenic acid, respectively, into their downstream long-chain FA conversion products using the selective SC 26196 inhibitor. We found that reduction in MIA PaCa-2 cell proliferation rate caused by lipid synthesis/uptake blockage is more pronounced in cells lacking KMT2D expression, with the maximum effect among the compounds used being observed in the case of the selective Δ6 desaturase inhibitor SC 26196 (figure 5G). The latter result substantiates the functional importance of the top KMT2D-regulated lipids in pancreatic cancer, since the SC 26196 compound inhibits the rate-limiting step in arachidonic acid synthesis pathway, in which both docosadienoic and docosatetraenoic acids are implicated. Overall, these results revealed that the cellular lipid profiles depend on KMT2D expression. Furthermore, we are the first ones to show the potential of docosadienoic, docosatrienoic and docosatetraenoic acids to promote growth of pancreatic cancer cells, as evidenced by time-dependent studies.

Synergistic role of glycolytic and lipidomic effectors in KMT2D-related phenotype

Lipogenic phenotype (characterised by increased FA synthesis, lipids’ uptake and metabolism) has been functionally and temporally linked to the glycolytic metabolism in primary and metastatic cancers as essential interaction regulating the malignant phenotype.16 Thus, we explored whether KMT2D-mediated impact on cancer cell proliferation is interdependent on both glucose/lipid metabolism-related downstream effectors. Notably, combined treatment of cells with an siRNA for SLC2A3 and the SC 26196 inhibitor completely reversed the effect of KMT2D depletion in MIA PaCa-2 cell proliferation (figure 5H), thus indicating the synergistic role of both SLC2A3 expression and maintenance of proper lipid composition as mediators of KMT2D-induced proliferative phenotype.

Clinical relevance of the KMT2D expression in pancreatic cancer

To define the clinical relevance of our findings, KMT2D expression was examined by IHC and quantitative automated image analysis in matched adjacent control-cancerous tissues from 26 patients with pancreatic cancer (cohort IV). KMT2D staining patterns showed that its nuclear localisation is almost absent in tumour samples, while highly expressed in normal tissues (figure 6A and online supplementary figure S15A). Further analysis of KMT2D expression in subgroups reflecting stage I or II of patients with pancreatic cancer (cohort IV) shows that the decrease in KMT2D levels may be significant in more advanced stages of the disease (online supplementary figure S15B). However, whether KMT2D levels relate to disease progression needs to be further explored in extended numbers of human biopsies and genetically engineered mouse models of pancreatic cancer. Kaplan-Meier analysis performed in 158 cases (cohort V) and in 22 cases (cohort III) revealed that patients with pancreatic cancer harbouring low KMT2D expression levels had worse prognosis in comparison with patients harbouring high KMT2D levels (figure 6B and online supplementary figure S16). Sample power calculations conducted for the larger cohort V suggest that an extended sample size (over 2800 cases) is needed to reach statistical significance (online supplementary table S12). It should be highlighted that the samples derived from cohort V reflect mostly (90%) stage II cases, thus rendering it very difficult to detect survival differences with statistical significance. On the other hand, 86% of the tumours derived from cohort III reflect patients with stage III pancreatic cancer. Subsequently, we conducted correlation analysis for the levels of KMT2D with its downstream effectors. Expectedly, the Pearson coefficients computed reflect a negative correlation between KMT2D and SLC2A3 mRNA levels (figure 6C,D), based on publicly available gene expression array data and our experimental RT-qPCR validation studies. Significantly, IHC analysis of activated NF-κB p65 and SLC2A3 in matched adjacent control-cancerous tissues shows increased immunostaining of both in the tumour-derived samples where KMT2D protein levels were found to be very low compared with normal samples. These results further validate our in vitro mechanistic findings that suggest KMT2D-dependent NF-kB activation and thus SLC2A3 upregulation in pancreatic cancer.

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Figure 6

Correlation of KMT2D levels between its targets and disease aggressiveness. (A) (i) Representative images (10× magnification) of KMT2D expression, as assessed by IHC analysis, in matched cancer and normal tissues from patients with pancreatic cancer (cohort IV). (ii) Automated image analysis using the Spectrum Software V.11.1.2.752 (Aperio) was performed for nuclear staining quantification. (B) Correlation of KMT2D expression with overall patient survival. Kaplan-Meier survival curves of patients harbouring below median (<3.917) and above median (>3.917) KMT2D levels derived from cohort V. (C, D) Correlation of KMT2D levels with SLC2A3 expression in patients with pancreatic cancer based on the study by Badea et al 4 and as assessed by RT-qPCR (cohort III). (E) Representative images (10× magnification) of phospho-NF-kB p65 (Ser 536) and SLC2A3 expression, as assessed by IHC analysis, in matched cancer and normal tissues from patients with pancreatic cancer (cohort IV). Scale bars represent 50 µm. r, Pearson correlation coefficient; Kaplan-Meier test was used for univariate survival analysis. Cox proportional hazard model was used for multivariate analysis and for determining the 95% CI. Statistical analyses were performed using one-way analysis of variance or Pearson correlation. Asterisks denote statistically significant differences, **p<0.01. BMI, body mass index; IHC, immunohistochemical; KMT2D, histone lysine (K)-specific methyltransferase 2D; NF-kB, nuclear factor kappa-light-chain-enhancer of activated B cells; RT-qPCR, reverse transcription quantitative PCR.

Correlation of KMT2D expression with various clinical parameters is illustrated in table 2 and online supplementary table S13.

No significant association was observed between KMT2D expression and age (p=0.4976), sex (p=0.2999) or race (p=0.3702). Interestingly, patients with high KMT2D levels display dramatic weight loss and higher BMI values with high statistical significance. Out of 106 cases with above median (>3.917) KMT2D expression, 84 (79.2%) cases lost an average of 9.366 kg (20.65 pounds). On the other hand, a significant correlation of KMT2D expression was further observed in 95 cases with increased body fat, as assessed by BMI measurements. In our attempt to address whether biopsies derived from patients harbouring low KMT2D levels exert quantitative changes in FA composition and cholesterol content relatively to control tissues, we performed lipidomics analysis in samples derived from cohort II. Remarkably, we found that among the top lipids being robustly upregulated with statistical significance in human biopsies are docosadienoic, docosatrienoic and docosatetraenoic acids, thus validating our in vitro results showing dramatic increase of these PUFAs on genetic inactivation of KMT2D in pancreatic cancer cells (table 3).

Overall, these findings support the relevance of KMT2D for human pancreatic carcinogenesis. Figure 7 shows a diagram illustrating the upstream and downstream effectors of KMT2D expression and activity in pancreatic cancer.

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Figure 7

Schematic depiction of KMT2D transcriptional regulation and downstream mechanistic targets and pathways in pancreatic cancer. Methylation of two single CpG sites transcriptionally represses KMT2D histone methyltransferase expression. Suppression of KMT2D induces aerobic glycolysis and lipid levels in pancreatic cancer. SLC2A3 consists a key mediator of the cellular growth and metabolic effects triggered by KMT2D downregulation. Docosadienoic, docosatrienoic and docosatetraenoic acid represent the top KMT2D-regulated fatty acids and harbour oncogenic properties in pancreatic cancer cells. KMT2D, histone lysine (K)-specific methyltransferase 2D.