Abstract
Pancreatic neuroendocrine tumours (PNETs) are the second most common pancreatic tumour. However, relatively little is known about their tumourigenic drivers, other than mutations involving the multiple endocrine neoplasia 1 (MEN1), ATRX chromatin remodeler, and death domain-associated protein genes, which are found in ~40% of sporadic PNETs. PNETs have a low mutational burden, thereby suggesting that other factors likely contribute to their development, including epigenetic regulators. One such epigenetic process, DNA methylation, silences gene transcription via 5’methylcytosine (5mC), and this is usually facilitated by DNA methyltransferase enzymes at CpG-rich areas around gene promoters. However, 5’hydroxymethylcytosine, which is the first epigenetic mark during cytosine demethylation, and opposes the function of 5mC, is associated with gene transcription, although the significance of this remains unknown, as it is indistinguishable from 5mC when conventional bisulfite conversion techniques are solely used. Advances in array-based technologies have facilitated the investigation of PNET methylomes and enabled PNETs to be clustered by methylome signatures, which has assisted in prognosis and discovery of new aberrantly regulated genes contributing to tumourigenesis. This review will discuss the biology of DNA methylation, its role in PNET development, and impact on prognostication and discovery of epigenome-targeted therapies.
Introduction
Pancreatic neuroendocrine tumours (PNETs) account for approximately 10% of all pancreatic tumours and occur with an incidence of approximately 1 per 100,000 (Sonbol et al. 2022). The incidence of PNETs is rising, partly due to increased detection rates and improved histopathological diagnosis. Compared to other malignancies, PNETs tend to be well-differentiated indolent tumours, with >15% caused by germline mutations in the multiple endocrine neoplasia 1 (MEN1), Von Hippel–Lindau tumour-suppressor (VHL), TSC complex (TSC), neurofibromin 1(NF1), MutY DNA glycosylase, BRCA2 DNA repair-associated, cyclin-dependent kinase inhibitor 1B (CDKN1B), and checkpoint kinase 2 genes (Crona & Skogseid 2016, Scarpa et al. 2017). Although germline mutations in these genes have been found in patients with clinically sporadic PNETs, these germline mutations are not always associated with a PNET phenotype. PNETs associated with hereditary tumour syndromes occur most commonly in patients with MEN1 syndrome (~80%), followed by VHL (5–17%) and TSC (4%) (de Laat et al. 2016, Romanet et al. 2019, Ahmad et al. 2021, Evans et al. 2022b ). Separate from PNETs are poorly differentiated highly aggressive pancreatic neuroendocrine carcinomas (PNECs). Although these pancreatic cancers retain and/or express neuroendocrine features, they are biologically and clinically distinct from their indolent PNET counterparts. Four distinct pathways have been implicated in PNET pathogenesis, including chromatin remodelling, DNA damage repair, mammalian target of rapamycin (mTOR) pathway activation, and telomere maintenance (Scarpa et al. 2017). In sporadic (i.e. non-familial) PNETs, inactivating mutations in MEN1, alpha thalassemia/mental retardation syndrome x-linked (ATRX) chromatin remodeller (ATRX), and death domain-associated protein (DAXX) are the most common, occurring in up to 40% of PNETs, and are involved in epigenetic regulation (Jiao et al. 2011, Thakker 2014, Chan et al. 2018) and inhibition of proliferative pathways (Jiao et al. 2011, Chamberlain et al. 2014). For example, menin (encoded by MEN1) is a ubiquitously expressed protein which forms complexes with proteins involved in gene transcription and repression mainly via histone modifications, including histone 3, lysine 4 (H3K4) and H3K9 methylation, and histone deacetylation (HDAC) (Yang et al. 2013, Thakker 2014). In addition to these genetic mutations, the tumour-suppressor protein RASSF1A is silenced in >80% of PNETs due to increased DNA methylation at its promoter. Given the low mutational burden of PNETs and that genes most commonly mutated in PNETs are involved in epigenetic regulation, recent studies have focused on the investigation of the epigenome, including both histone modifications and DNA methylation. Histone modification and DNA methylation mechanisms are intrinsically linked; however, recently new technological and scientific developments have advanced our understanding of DNA methylation. This review will focus on the biology of DNA methylation, its role in human PNET development, and its likely impact on current therapeutics and future research.
Epigenetics overview
Epigenetics refers to processes that alter gene activity without changing the DNA sequence and result in modifications that may be transmitted to daughter cells. These epigenetic processes, which include methylation, acetylation, phosphorylation, ubiquitination, and sumoylation, have important roles in ensuring cell-specific transcription regulating the accessibility of DNA, as follows. DNA, whose total length in a human cell is >2 m, is packaged within the nucleus, whose diameter is ~10–20 μm, by being tightly wrapped around histone proteins to form nucleosomes that are the building blocks for chromatin (Fig. 1) (Annunziato 2008). Chromatin may occur in a less tightly compacted form, referred to as open or euchromatin, which is closely associated with RNA polymerases and actively transcribed genes, while more condensed chromatin, referred to as closed or heterochromatin, is associated with structural proteins and regions containing inactive genes. DNA methylation and histone modifications, both of which determine chromatin state, are dynamic processes, and these may also change depending on the microenvironment and nutrient availability (Tobi et al. 2009).
Relationship between histone and DNA methylation with chromatin state. In chromosomes, DNA is usually tightly wrapped and packaged around histone proteins when not being actively transcribed. DNA methylation is catalysed by DNMT enzymes which ensure that cytosines at CpG sites remain methylated and this prevents transcriptional machinery from binding to these sections of DNA. Histone and DNA methylation work together to either allow or prevent DNA transcription. Thus, sections of DNA are ‘marked’ for transcription with both histone and DNA modifications to determine which parts of DNA are unwound from histone proteins to enable transcriptional machinery to access DNA. TET enzymes ensure that DNA remains unmethylated, thereby allowing transcription factors to bind to DNA, whereas the methylation mark H3K27 tri-methylation (H3K27me3), catalysed by EZH2, is associated with heterochromatin and keeps DNA wound tightly around histone proteins. Menin catalyses the addition of a methyl group by MLL1/2 (KMT2A/B) to form the active histone methylation mark H3K4 tri-methylation (H3K4me3), which unwinds DNA.
Citation: Endocrine Oncology 3, 1; 10.1530/EO-23-0003
The histone mark that is added to specific amino acids on histone tails determines the chromatin structure. For example, the tumour-suppressor protein menin forms complexes with mixed-lineage leukemia gene 1/2 (MLL1/2)/lysine methyltransferase 2A/B (KMT2A/B), which adds a methyl group to lysine 4 of histone protein H3 (Fig. 1), forming the active histone mark H3K4 tri-methylation (H3K4me3) and open chromatin. Whereas tri-methylation of lysine 27 of histone 3 (H3K27me3), catalysed by enhancer of zeste homolg 2 (EZH2), leads to a closed chromatin (heterochromatin) state and subsequent transcriptional repression. Both MLL1/2 (KMT2A/B) and EZH2 are known are ‘writers’ as they are responsible for adding these marks, whereas lysine demethylase 5B (KDM5B) and lysine demethylase 6A/B (KDM6A/B) are demethylases, which remove these methyl groups, and are termed ‘erasers’ (Table 1).
Examples of methylation-associated histone H3 and DNA epigenetic ‘writers’, ‘readers’, and ‘erasers’.
| Epigenetic mark | Transcription | Writers | Readers | Erasers |
|---|---|---|---|---|
| Histones | ||||
| H3K4 methylation | Active | KMT2A/B (MLL1/2) |
TAF3 Sgf29 |
KDM5A/B (JARID1A/B) |
| SETD1A/B (KMT2F/E) | CHD1 BPTF |
KDM1A/B (LSD1/2) | ||
| H3K27 methylation | Repressive | EZH1/2 (KMT6A/B) | CBX7 BAHD1 |
KDM6A/B |
| DNA | ||||
| 5mC | Repressive | DNMT1, DNMT3A, DNMT3B | MeCP2 MBD1–6 Kaiso family SRA family |
TET1-3 |
5mc, 5’methylcytosine; BAHD1, bromo adjacent homology domain containing 1; BPTF, bromodomain finger transcription factor; CDX7, chromobox 7; CHD1, chromodomain helicase DNA-binding protein 1; DNMT1/3A/3B, DNA methyltransferase 1/3A/3B; EZH1/2, enhancer of zeste 1/2 polycomb repressive complex 2 subunit; JARID1A/B, Jumonji, AT-rich interactive domain 1A/B; KDM1A/B, lysine demethylase 1A/B; KDM5A/B, lysine demethylase 5A/B; KDM6A/B, lysine demethylase 6A/B; KMT2A/B, lysine methyltransferase 2A/B; KMT2E/F, lysine N-methyltransferase 2E/F; KMT6A/B, hstone–lysine N-methyltransferase EZH1/2; LSD1/2, lysine-specific histone demethylase 1A/B; MBD1–6, methyl–CpG-binding domain protein 1–6; MeCP2, methyl–CpG-binding protein 2; MLL1/2, myeloid/lympoid or mixed-lineage leukaemia; Sgf29, SAGA complex-associated factor 29; SRA, SET and ring-finger-associated; SETD1A/B, SET domain containing 1A/B, histone lysine methyltransferase; TAF3, TATA-box-binding protein-associated factor 3; TET1–3, tet methylcytosine dioxygenase 1–3.
‘Readers’ are proteins which decode these histone marks and determine the recruitment of other machinery to assist in changing DNA conformation to either allow or inhibit transcription. There are >75 different ‘writers’, ‘erasers’, and ‘readers’ involved in methylation maintenance of histone H3, and examples of these are provided in Table 1 (Hyun et al. 2017, Beacon et al. 2021). Separate to these histone marks are DNA modifications, with DNA methylation being the most common and characterised mark. Long-term gene silencing may occur via DNA methylation, with the modified DNA base 5’methylcytosine (5mC) responsible for recruiting transcriptional repressors to DNA protomers, and/or inhibiting transcriptional factor binding, ultimately silencing gene expression (Kohli & Zhang 2013, Moore et al. 2013). Cytosine modifications are recognised by different transcription factors, which show a preference for specific cytosine modifications, including the methyl-binding domain (MBD), Kaiso, and SET- and ring-finger-associated (SRA) domain family (Ren et al. 2018). There are several ‘reader’ proteins which can interact with 5mC, and these predominantly contain an MBD domain, including MBD1–6 and methyl–CpG-binding protein 2 (MeCP2), SET domain bifurcated histone lysine methyltransferase 1/2, and bromodomain adjacent to zinc finger domain 2A/B. By interacting with 5mC, these proteins predominantly cause transcriptional repression, either by recruitment of other transcriptional repressive proteins or by direct interaction with histone modifications (e.g. MeCP2 which binds to histone ‘writers’ and ‘erasers’, which are involved in HDAC and histone methylation, respectively) that subsequently alter nucleosome and chromatin structure to a closed state (i.e. heterochromatin) (Du et al. 2015). 5mC is formed by DNA methyltransferase (DNMT) enzymes transferring a methyl group from S-adenosyl-methionine to the 5’ position of cytosine (Fig. 1) (Moore et al. 2013). There are three human DNMTs: DNMT3A and DNMT3B are responsible for de novo methylation and DNMT1 is responsible for methylation maintenance during replication. The function of these DNMTs is directly opposed by the recently discovered ten-eleven-translocase (TET) family of enzymes, TET1, TET2 and TET3, which can actively demethylate 5mC via consecutive reactions from 5mC back to an unmodified/hypomethylated cytosine (Tahiliani et al. 2009, Ito et al. 2010, Kohli & Zhang 2013). The DNA methylome is therefore a dynamic process that is also closely intertwined with the citric acid cycle, with TET and a subset of lysine demethylases (KDMs) dependent on alpha-ketoglutarate, including KDM5B which demethylates H3K4me3, H3K4 di-methylation (H3K4me2), and H3K4 mono-methylation (H3K4me1) histone marks (Fig. 2). 5’hydroxymethylcytosine (5hmC; the first intermediate mark during 5mC oxidation) is a stable epigenetic mark, protecting CpG sites against DNMTs forming 5mC and promoting gene transcription (Kohli & Zhang 2013, Skvortsova et al. 2019). However, 5hmC and 5mC are indistinguishable when using conventional bisulfite conversion techniques, and this may explain the reported inconsistencies between apparently methylated promoters and protein expression in PNETs (e.g. MEN1 andO-6-methylguanine-DNA methyltransferase (MGMT)) and in PNECs (SRY-box transcription factor 2 (SOX2)) (Arnold et al. 2007, Walter et al. 2015, Ban et al. 2022, Yachida et al. 2022). For example, SOX2 overexpression was reported in PNECs to be associated with promoter methylation, and a paradoxically open chromatin structure at the SOX2 gene was observed using assay for transposase-accessible chromatin with high-throughput sequencing (Yachida et al. 2022). 5mC and 5hmC occur almost exclusively at sites where cytosine is followed by a guanine (CpG) on cis-DNA. Either CpGs are heavily methylated and scattered at lower than expected frequency throughout the human genome which is likely due to 5mC undergoing spontaneous deamination to thiamine (Bird 1986) or they are found in clusters of hypomethylated CpGs in sections of DNA 0.5–2kB in length, termed CpG islands (CGIs). There are approximately 30,000 CGIs which are commonly found in proximal promoters and specifically those of housekeeper genes (Bird 1986, Jones & Baylin 2002). CGIs are flanked on either side by shores (within 2kB of the CGI), shelves (within 4 kB), and the open sea (>4 kB) (Fig. 3). When genes are transcribed, CpG sites within the CGI are hypomethylated and flanked on either side by 5hmC at shores (which protect against 5mC), with 5mC marks scattered throughout the open sea (Fig. 3A). 5hmC marks are also present at the ‘rim’ of expressed genes, with the amount of 5hmC positively correlating with both the peak in H3K4me3 histone marks and with gene expression (Li et al. 2018). In cancer cells, when 5hmC is lost (Fig. 3B), DNMTs methylate these previously protected unmethylated cytosines in CGIs, leading to 5mC formation and transcriptional silencing. Aberrant DNA hypermethylation tends to occur at hypomethylated CGIs including tumour-suppressor genes (TSGs) (Skvortsova et al. 2019), whereas overall DNA hypomethylation commonly occurs outside of CGIs at highly repetitive DNA sequences, mainly at short or long interspersed nuclear elements (SINEs or LINEs) which comprise up to 50% of the human genome, and is associated with chromosome instability (Eden et al. 2003, Ehrlich 2009).
The dynamic DNA methylome cycle. In the dynamic DNA methylome, 5’methylcytosine (5mC) undergoes consecutive oxidative steps to form 5’hydroxymethylcytosine (5hmC), 5’formylcytosine (5fC) and 5’carboxylcytosine (5caC) and then back to an unmodified cytosine (C), which can re-enter the cycle following re-methylation by DNA Methyltransferase (DNMT) enzymes to 5mC. The DNA methylome is linked with the tricarboxylic acid (TCA) cycle, which is also known as the Krebs or citric acid cycle. The TCA cycle provides alpha-ketoglutarate which is required for active demethylation by ten-eleven-translocase (TET) and by histone lysine demethylase (KDM) enzymes including KDM5B which demethylates H3K4me3, H3K4 dimethylation (H3K4me2), and H3K4 mono-methylation (H3K4me1). Loss of menin leads to increased DNMT1 and subsequent DNA methylation, as well as a loss of the active histone mark H3K4me3, which also protects against DNA methylation.
Citation: Endocrine Oncology 3, 1; 10.1530/EO-23-0003
DNA methylation in normal (panel A) and cancer (panel B) states. (A) A typical strand of DNA with a CpG island I (CGI) in normal tissue. CGIs are flanked on either side by shores (within 2 kB of the CGI), shelves (within 4 kB), and the open sea (>4kB). CpG sites occur more frequently in CpG islands when compared to the rest of the genome and are usually hypomethylated (blue circles), whilst 5’hydroxymethylcytosine (5hmC) marks (green circles) tend to be present at the shores of CGI and protect against DNA methyltransferases (DNMTs), and subsequent 5’methylcytosine (5mC) marks (red circles) are found less frequently outside of CGIs. H3K4 tri-methylation (H3K4me3) marks are associated with regions of DNA hypomethylation and H3K4 mono-methylation (H3K4me1) marks are associated with regions enriched in 5hmC. CpG sites in the open sea (i.e. >4 kB away from a CGI) tend to be methylated. (B) In cancer, aberrant DNA methylation occurs with a loss of 5hmC marks (green circles) that results in an inability to protect against DNMTs, which leads to the usually hypomethylated cytosines (blue circles) in CGI becoming methylated (red circles) by DNMTs that in turn leads to transcriptional silencing. Scattered CpGs outside the CGI (shelves and open sea) become progressively hypomethylated in malignancy.
Citation: Endocrine Oncology 3, 1; 10.1530/EO-23-0003
DNA methylation in PNETs
There are many methods to investigate DNA methylation, and these include bisulfite-based, restriction enzyme-based, and affinity-based strategies (Olkhov-Mitsel & Bapat 2012). There have been 32 studies reporting DNA methylation in human PNETs, and one study which has looked at global 5hmC (Tables 2 and 3). The PNET methylome has been profiled in 24 studies in a gene-specific manner (Muscarella et al. 1998, Bartsch et al. 2000, Serrano et al. 2000, Chan et al. 2003, Dammann et al. 2003, House et al. 2003, Wild et al. 2003, Liu et al. 2005, Arnold et al. 2007, Choi et al. 2007, Dejeux et al. 2009, Malpeli et al. 2011, Stricker et al. 2012, Ohki et al. 2014, Schmitt et al. 2014, Stefanoli et al. 2014, Liu et al. 2014, Cros et al. 2016, Ushiku et al. 2016, Campana et al. 2018, Conemans et al. 2018, Zhang et al. 2020, Ban et al. 2022, Evans et al. 2022a ) and subsequently by both global methylation (Marinoni et al. 2017) and hydroxymethylation (Sharma et al. 2022) and by specific CpG site assessment with array-based technologies (Chan et al. 2003, Tirosh et al. 2019, Boons et al. 2020, Di Domenico et al. 2020, Lakis et al. 2021, Simon et al. 2022, Yachida et al. 2022). Out of the 24 studies looking at a specific subset of genes, 58% (14/24) have used methylation-specific polymerase chain reaction (MSP) to investigate the percentage of methylation present at specific gene promoters. However, the criteria used to classify whether a gene is methylated were not defined in most studies. Definitions for methylated genes, if included, were reported as increased gene methylation as an mCG/CG ratio of >7%, >8, or >20% (Malpeli et al. 2011, Cros et al. 2016, Campana et al. 2018, Li et al. 2018). Other techniques include combined bisulfite restriction analysis (COBRA), methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA), denaturing HPLC, pyrosequencing, and array-based techniques including Illumina Infinium Human450K and MethylationEPIC arrays. All studies examining DNA methylation have used bisulfite-only methods to investigate the PNET methylome. However, bisulfite converts only unmodified cytosines to uracil (subsequently to thiamine), and it is important to note that both 5mC and 5hmC marks will remain unchanged and will therefore be indistinguishable.
Gene-specific DNA methylation studies of human PNETs.
| Study | PNETs | Investigation | Genes investigated (proportion methylated (%)) | Other features |
|---|---|---|---|---|
| Gene-specific methylation assessment grouped by method | ||||
| Arnold et al. (2007) | 46 PNETs: 26 INS, 3 GLU, 4 GAS, 2 VIP, 11 NF | MSP, IHC, LOH, MSI | APC (48%), E-cadherin (2%), HIC-1 (93%), hMLH1 (0%), MEN1 (19%), MGMT (17%), p16 (0%), PTEN (0%), RASSF1A (80%), RUNX3 (7%), and TIMP3 (0%) | CIMP-positive phenotype associated with a higher Ki67 and poorer overall survival. APC, p16, and menin expression for each tumour was reported using IHC |
| Bartsch et al. (2000) | 17 PNETs: 17 INS | MSP | CDKN2A (17%) | |
| Campana et al. (2018) | 43 PNETs | MSP or PSQ | MGMT | On multivariate analysis, poorer overall survival was associated with unmethylated MGMT, higher Ki67, and previous chemotherapy |
| Chan et al. (2003) | 11 PNETs: 1 INS, 2 GAS (2 MEN1), 8 NF (1 MEN1) | MSP and COBRA | CACNA1G (0%), COX2 (9%), ER (64%), MEN1 (0%), MGMT (0%), APBA1/MINT1 (18%), APBA2/MINT2 (0%), MINT25 (0%), MINT27 (9%), MINT31 (18%), P14 (9%), P16 (9%), RARß (0%), THBS1 (9%) | Percentage of methylated genes in adjacent normal tissue: COX2 (9%), ER (27%), MINT1 (27%), MINT27 (18%), MINT31 (9%) |
| Dammann et al. (2003) | 12 PNETs | MSP | CDKN2A (17%), RASSF1A (83%) | CDKN2A and RASSF1A methylation was associated with metastatic disease |
| House et al. (2003) | 48 PNETs: 1 GLU, 2 GAS, 45 NF | MSP | APC (21%), CDKN2A (40%), E-cadherin (23%), GST (0%), hMLH1 (23%), MGMT (40%), P14 (0%), P73 (17%), RARß (25%), RASSF1A (75%), TIMP3 (0%) | CDKN2A methylation was associated with mortality (multivariate analysis) |
| Liu et al. (2005) | 16 PNETs | MSP | MGMT (13%), P14 (44%), P16 (19%), RASSF1A (63%) | Percentage of methylated genes in adjacent normal tissue: RASSF1A (27%) |
| Malpeli et al. (2001) | 20 PNETs: 3 INS, 2 GAS, 15 NF | MSP, qMSP, mRNA | RASSF1A (80%), RASSF1A (55%) | RASSF1A expression was increased after treatment of BON-1, QGP-1, and CM cells |
| Muscarella et al. (1998) | 4 PNETs: 3 NF, 1 liver metastasis | MSP | CDKN2A (50%) | 50% homozygous loss of CDKN2A. No genetic mutations in CDKN2A were identified |
| Ohki et al. (2014) | 50 PNETs: 2 INS, 48 NF | MSP, LOH, mRNA | PHLDA3 (82%) – analysed in 11 patient PNET samples only | PHLDA3 methylation was associated with poorer outcomes (ns). A99 cell lines treated with 5-aza-C decreased methylation of PHLDA3 |
| Schmitt et al. (2014) | 52 PNETs: 28 INS, 3 GLU, 5 GAS, 4 VIP, 12 NF | MSP, IHC | MGMT (56%) | MGMT loss of expression correlated with an overall poorer survival. 51% of samples had discordant promoter methylation and protein expression |
| Serrano et al. (2000) | 9 PNETS: 9 GAS | MSP and semi-Q-MSP | CDKN2A/P16 (67%) | No genetic mutations or homozygous deletions. No association with clinical characteristics |
| Ushiku et al. (2016) | 36 PNETs: 6 INS, 2 GAS, 1 VIP, 1 SOM, 26 NF | Q-MSP, IHC | HOPX (14%) | HOPX reduced expression associated with poorer overall survival |
| Wild et al. (2003) | 21 PNETs: 5 INS, 5 GAS, 1 VIP, 1 REN, 9 NF | MSP, RNA, and IHC | TIMP3 (44%) | TIMP3 methylation was associated with metastasis. Strong TIMP3 expression was seen in normal islets, with 55% of PNETs showing loss of expression |
| Ban et al. (2022) | 115 PNETs: 44 NF and 71 functional | MS-MLPA (17 PNETs and 7 normal pancreata) and IHC | MLH1, MSH2, MSH6, PMS2 (23.5% had promoter methylation in at least 1 of the MMR genes), MGMT (47%) | All samples expressed MMR proteins. Multivariate analysis, low MGMT expression was associated with shorter progression-free survival |
| Conemans et al. (2018) | 95 PNETs: NF (61 MEN1, 34 sporadic) | SALSA MS-MLPA | APAF1, APC, BCL2, CASP8, CD44, CDH13, CKDN2B, DNAJC15, ESR1, GATA4, GATA5, GSTP1, KLLN, MGMT, MSH6, MUS81, NF1, NTRK1, PAX5, PCCA, RARRES1, RASSF1, SFRP1, TERT, THBS1, TP73, TWIST1 a | Using a cutoff level of 15% to define promoter hypermethylation, MSH6, APAF1, RASSF1, TWIST1, and KLLN were hypermethylated in >80% of MEN1 tumours. Of note, RASSF1 and CASP8 had high levels of promoter methylation in menin-negative tumours compared to menin-positive tumours |
| Stefanoli et al. (2014) | 56 PNETs: 23 INS, 2 GLU, 2 GAS, 2 VIP, 1 CUS, 26 NF | MS-MLPA and CNA | ATM, APC, BRCA1, BRCA2, CADM1, CASP8, CDH13, CD44, CDKN1B, CDKN2A(p14 and p16), CHFR, DAPK1, ESR1, FHIT, GATA5, GSTP1, HIC1, LINE-1, MGMT, MLH1, PAX5, PAX6, PYCARD, RARß, RASSF1, RB1, STK11, THBS1, TIMP3, TP53, TP73, VHL,WT1 | Unsupervised hierarchical clustering of the 33 methylated genes clustered PNETs into three groups, with increased numbers of TSG gene methylation associated with poorer prognosis. LINE-1 hypomethylation was also associated with poorer overall survival |
| Liu et al. (2014) | 350 PNETs: 140 INS, 144 NF, 39 functional NOS | DHPLC | INA (56%) – analysed in 25 patient PNET samples only. | Cell lines treated with decitabine to look for protein re-expression |
| Choi et al. (2007) | 11 PNETs with matched controls | PSQ | LINE-1, Alu hypomethylation | Relative hypomethylation of LINE-1 associated with metastasis and RASSF1A methylation. Alu hypomethylation correlated with MGMT methylation |
| Cros et al. (2016) | 43 PNETs: (5 MEN1) | PSQ, IHC, and MSI | MGMT | Low MGMT expression was associated with longer progression-free survival and response to temozolomide therapy |
| Dejeux et al. (2009) | 32 PNETs: 12 INS, 20 NF | PSQ, mRNA, and IHC | IGF2-H19 locus | Increased expression (IHC and mRNA) of IGF2 in insulinomas and increased methylation of the DMR2 locus. Decreased DMR methylation associated with increasing tumour grade. |
| Evans et al. (2022) | 27 PNETs | PSQ | SSTR2 | Promoter methylation of SSTR2 is higher in PNETs compared to non-NET tissue and is inversely correlated with IHC SSTR2 staining. Guadecitabine increases SSTR2 expression in NET cell lines (and xenograft mouse model) |
| Stricker et al. (2012a) | 15 PNETs with matched controls | PSQ | LINE-1 hypomethylation | LINE-1 hypomethylation was associated with lymph node metastasis and grade 1 vs 2 PNETs |
| Zhang et al. (2020) | 14 PNETs: 7 ACTH, 7 NF | PSQ and IHC | POMC | The POMC promoter was hypomethylated in ACTH-PNETs compared to NF-PNETs and normal pancreas. |
aAll genes had hypermethylation in >10% of MEN1-related tumours analysed.
450K, Infinium HumanMethylation450 BeadChip; 5-aza-C, azacitadine; A-D-M, ATRX-DAXX-MEN1; APAF1, apoptotic peptidase-activating factor 1; APC, APC regulator of WNT signalling pathway; ATAC-seq, assay for transposase-accessible chromatin with high-throughput sequencing; ATM, ATM serine/threonine Kinase; ATRX, ATRX chromatin remodeler; BCL2, BCL2 apoptosis regulator; BRCA1, BRCA1 DNA repair associated; BRCA2, BRCA2 DNA repair associated, CACNA1G, T-type calcium channel; CADM1, cell adhesion molecule 1; CASP8, caspase 8; CD44, CD44 molecule (Indian Blood Group); CDH1, E-cadherin; CDH13, cadherin 13; CDKN1B, cyclin-dependent kinase inhibitor 1B; CDKN2A(p14, p16), cyclin-dependent kinase inhibitor 2A; CDKN2B, cyclin-dependent kinase inhibitor 2B; CHFR, checkpoint with forkhead and ring-finger domains; CNA, copy number alterations; COBRA, combined bisulfite restriction analysis; COX2, cyclooxygenase 2; DAPK1, death-associated protein kinase 1; DAXX, death domain-associated protein; DHPLC, denaturing high-performance liquid chromatography; DMR, differentially methylated region; DNAJC15, DnaJ heat-shock protein family (Hsp40) member C15; EPIC, Infinium MethylationEPIC BeadChip; ER, oestrogen receptor; ESR1, oestrogen receptor 1; FHIT, fragile histidine triad diadenosine triphosphatase; GAS, gastrinoma; GATA4, GATA-binding protein 4; GATA5, GATA-binding protein 5; GLU, glucagonoma; GSTP1, glutathione S-transferase Pi 1; H19, H19 imprinted maternally expressed transcript; HIC-1, HIC ZBTB transcriptional repressor 1; hMLH1, human MutL homolog 1; 5hmC, 5’ hydroxymethylcytosine; HOPX, HOP homeobox; IGF2, insulin-like growth factor 2; IHC, immunohistochemistry; INA, internexin neuronal intermediate filament protein alpha/alpha-internexin; INS, insulinoma; Killin, KLLN, p53-regulated DNA replication inhibitor; LINE-1, long interspersed nuclear element 1; LOH, loss of heterozygosity; MEN1, multiple endocrine neoplasia type-1; MGMT, O6-methyl-guanine methyltransferase; MINT1/ABPBA1, amyloid beta precursor protein-binding family A member 1; MINT2/ABPBA2, amyloid beta precursor protein-binding family A member 2; MINT25, MINT27, and MINT31, methylated In tumour; mRNA, messenger ribonucleic acid; MS-MLPA, methylated-specific multiplex ligation-dependent probe amplification; MSH6, MutS homolog 6; MSI, microsatellite instability; MSP, methylation-specific-PCR; MUS81, MUS81 structure-specific endonuclease subunit; NF, non-functioning; NF1, neurofibromin 1; NOS, not otherwise specified; NS, not significant; NTRK1, neurotrophic receptor tyrosine kinase 1; PAX5, paired box 5; PAX6, paired box 6; PCCA, propionyl-CoA carboxylase subunit alpha; PDX1, pancreatic and duodenal homeobox 1; PNEC, pancreatic neuroendocrine carcinoma; PNEN, pancreatic neuroendocrine neoplasm; PNET, pancreatic neuroendocrine tumour; POMC, proopiomelanocortin; PSQ, pyrosequencing; PYCARD, PYD and CARD domain containing; Q-MSP, quantitative-MSP; RB1, RB transcriptional corepressor 1; RARb, retinoic acid receptor beta 2; RARRES1, retinoic acid receptor responder 1; RASSF1A, Ras association domain family member 1; REN, renin producing; SFRP1, secreted frizzled related protein 1; SOM, somatostatinoma; SSTR2, STK11, somatostatin receptor 2; serine/threonine kinase 11; TERT, telomerase reverse transcriptase; THBS1, thrombospondin 1; TIMP3, TIMP metallopeptidase inhibitor 3; TP53, tumour protein P53; TP73, tumour protein P73, TSG, tumour-suppressor gene; TWIST1, twist family BHLH transcription factor 1; VHL, Von Hippel–Lindau tumour suppressor; VIP, VIPoma; WES, whole-exome sequencing; WGS, whole-genome sequencing; WTI, WT1 transcription factor.
Global DNA methylation and hydroxymethylation studies of human PNETs.
| Study | PNETs | Investigation | Genes investigated (proportion methylated (%)) | Other features |
|---|---|---|---|---|
| Global methylation assessment | ||||
| Marinoni et al. (2017) | 167 PNETs | Episeeker quantification, IHC, MSP and PSQ | Global and LINE-1 methylation | DAXX/ATRX-negative tumours and patients with MEN1 mutations were not associated with LINE-1 hypomethylation |
| Tirosh et al. (2019) | 33 PNETs (9 sporadic, 10 MEN1, and 10 VHL) | EPIC | Methylation assessment of >850,000 CpGs across the human genome | Reported loss of PHLDA3 as being an important gene involved upstream of the Akt pathway. |
| Di Domenico et al. (2020) | 125 PNETs | 450K | Compared DNA methylome of PNETs to sorted normal alpha and beta cells | Stratified PNETs by DNA methylation signatures which improved patient stratification which correlated with disease-free survival |
| Chan et al. (2018) | 64 PNETs (32 PNETs methylome investigated) | 450K | Compared the DNA methylome in PNETs grouped by A-D-M mutant vs wildtype | A-D-M mutant PNET had a similar methylation profile to that of an alpha cell, with high ARX and low PDX1 |
| Boons et al. (2020) | 83 PNETs (26 methylome investigated) | EPIC | Compared to DNA methylome of five normal islet cells (two alpha cells and three beta cells) | PNETs were categorised into alpha- or beta-like tumours based on methylation signatures |
| Lakis et al. (2021) | 84 PNETs | 450K | Compared to DNA methylome of 11 normal adjacent pancreata | PNETs were categorised into three subgroups T1: functional tumours with A-D-M wildtype (similar to beta cell), T2: A-D-M mutant, and T3: MEN1 mutations (similar to alpha cells) |
| Simon et al. (2022) | 57 PNENs (43 PNETs and 14 PNECs) | EPIC | Compared the DNA methylome of PNETs and PNECs to that of cell type signatures of alpha, beta, acinar, and ductal adult cells. | PNEC had similar methylomes to exocrine tissue |
| Yachida et al. (2022) | PNENs (48 PNETs and 18 PNECs) | EPIC, WGS, WES, ATAC-seq | Compared the DNA methylome of PNETs of PNECs | DAXX hypermethylation ~ all PNETs. PNECs clustered into ‘ductal’ or ‘acinar’ types. PNETs clustered into (i) MEN1 alterations with RASSF1A, PDX1, and CDX2 promoter hypermethylation and (ii) hypomethylation group including: HNF4A, MGMT, and TERT. |
| Hydroxymethylation assessment | ||||
| Sharma et al. (2022) | 60 PNETs | IHC | 5hmC staining of formalin-fixed paraffin-embedded slides | Loss of 5hmC was associated with metastatic disease |
450K, Infinium HumanMethylation450 BeadChip; ARX, Aristaless-related homeobox; A-D-M, ATRX-DAXX-MEN1; ATAC-seq, assay for transposase-accessible chromatin with high-throughput sequencing; ATRX, ATRX chromatin remodeler; CDKN2A(p14, p16); cyclin-dependent kinase inhibitor 2A; CDX2, caudal-type homeobox protein 2; DAXX, death domain-associated protein; EPIC, Infinium MethylationEPIC BeadChip; HNF4A, hepatocyte nuclear factor 4 alpha; IHC, immunohistochemistry; LINE-1, long interspersed nuclear element 1; MEN1, multiple endocrine neoplasia type 1; MGMT, O6-methyl–guanine methyltransferase; mRNA, messenger ribonucleic acid; MSP, methylation-specific PCR, NF, non-functioning; PNEC, pancreatic neuroendocrine carcinoma; PNEN, pancreatic neuroendocrine neoplasm; PNET, pancreatic neuroendocrine tumour; PSQ, pyrosequencing; RASSF1A, Ras association domain family member 1; TERT, telomerase reverse transcriptase; VHL, Von Hippel–Lindau tumour suppressor; WES, whole-exome sequencing; WGS, whole genome sequencing.
Approximately 30% of these 24 studies (7/24), which used a targeted hypothesis-driven approach, have reported that the TSGs, Ras association domain family member 1 (RASSF1) and cyclin-dependent kinase inhibitor 2A (CDKN2A), were found to be methylated in up to 83% (Dammann et al. 2003, House et al. 2003, Liu et al. 2005, Arnold et al. 2007, Malpeli et al. 2011, Stefanoli et al. 2014, Conemans et al. 2018) and 17–67% (Muscarella et al. 1998, Bartsch et al. 2000, Serrano et al. 2000, Chan et al. 2003, Dammann et al. 2003, House et al. 2003, Liu et al. 2005, Arnold et al. 2007, Stefanoli et al. 2014) in PNETs, respectively. The RASSF1 gene has two promoters (A and C) and seven different transcripts (RASSF1A–G). RASSF1A is a ubiquitously expressed scaffold protein which interacts with many different pathways, including the Wnt and Hippo pathways (Papaspyropoulos et al. 2018). CDKN2A encodes for two separate proteins p14 and p16 (INK4a) and is involved in cell cycle regulation, and a loss of function of CDKN2A is associated with cancer (Ruas & Peters 1998). One study investigated the Pleckstrin homology-like domain family A member 3 (PHLDA3) gene and reported loss of PHLDA3 expression via loss of heterozygosity and promoter methylation, which was seen in up to 72% (36/50) of PNETs, and this is comparable to that seen with menin loss of expression (60–67%). PHLDA3 is a tumour suppressor which acts by competing with Akt and inhibiting its interaction and subsequent activation with membrane lipids. Therefore, loss of PHLDA3 leads to increased Akt activation and subsequently increased signalling through the phosphatidylinositol-3-kinase/Akt/mTOR (PI3K/Akt/mTOR) pathway, which is commonly upregulated in PNETs. PHLDA3 knockout also leads to beta cell proliferation, as illustrated by studies in a PHLDA3–/– knockout mouse model (Ohki et al. 2014). Increased somatostatin receptor 2 (SSTR2) methylation was reported in 27 human PNET samples compared to non-NET tissue, and this was inversely correlated to SSTR2 expression by immunohistochemistry (Evans et al. 2022a ). Recently, DAXX was reported to be hypermethylated in almost all PNETs (Yachida et al. 2022). Overall, half of these studies have investigated protein and/or mRNA expression with their relationships to promoter and enhancer methylation and have reported a variable association between gene methylation and expression (Bartsch et al. 2000, Wild et al. 2003, Dejeux et al. 2009, Malpeli et al. 2011, Ohki et al. 2014, Schmitt et al. 2014, Cros et al. 2016, Ushiku et al. 2016, Marinoni et al. 2017, Tirosh et al. 2019, Zhang et al. 2020, Ban et al. 2022, Evans et al. 2022a , Yachida et al. 2022).
Five of these studies have investigated DNA hypomethylation in PNETs, using LINE-1 and Alu hypomethylation as a surrogate for global DNA methylation (Choi et al. 2007, Stricker et al. 2012, Stefanoli et al. 2014, Marinoni et al. 2017, Yachida et al. 2022). LINE-1, the most abundant LINE, is located non-randomly in GC-poor regions of DNA, approximately 6000 kb long, and encodes for two proteins which catalyse retro-transposition, i.e. the ability to ‘copy and paste’ itself (i.e. LINE-1) into other sections of DNA (Choi et al. 2007). Multiple copies or fragments of LINE-1 are present throughout the human genome and are usually transcriptionally silenced by either truncating mutations within the 5’UTR and/or promoter region or by methylation (Carnell & Goodman 2003, Hancks & Kazazian 2016, Sanchez-Luque et al. 2019). Global DNA hypomethylation and LINE-1 promoter hypomethylation and subsequent transcription leads to genetic instability, increases the mutation rate, and has been associated with different cancers, e.g. breast, colon, lung, head and neck, bladder, liver, prostate, oesophagus, stomach (Chalitchagorn et al. 2004), and PNETs (Chen et al. 1998, Takai et al. 2000, Choi et al. 2007, Stricker et al. 2012, Stefanoli et al. 2014, Marinoni et al. 2017). Alu elements are repetitive elements ~280 base pairs long, and are usually heavily methylated in normal pancreatic tissue and are hypomethylated in PNETs. Alu methylation was significantly inversely correlated with MGMT promoter methylation, with low levels of Alu methylation found in patients with well-differentiated PNETs and carcinoid tumours, who had MGMT methylation (Choi et al. 2007). In addition to LINE-1 and Alu hypomethylation, telomerase reverse transcriptase, MGMT and hepatocyte nuclear factor 4 alpha are hypomethylated in a subset of PNETs which tended to harbour MEN1 alterations and greater promoter hypermethylation in RASSF1, pancreatic and duodenal homeobox 1 (PDX1), and caudal type homebox 2 (Yachida et al. 2022). One study investigated alterations in enhancer regions and reported that the enhancer for the protein tyrosine phosphatase receptor type N2 (PTPRN2) gene was hypomethylated in all PNET subgroups and was associated with increased PTPRN2 transcription (Tirosh et al. 2019). PTPRN2 is highly expressed in islet cells and is upregulated in other cancers, including breast and hepatocellular cancer (Shen et al. 2015, Sengelaub et al. 2016). Of note, most studies that have reported on the DNA methylome in PNETs have focused on 5mC, with only one study reporting loss of global 5hmC to be associated with tumourigenesis (Sharma et al. 2022).
DNA methylation in hereditary-associated vs sporadic PNETs
Two studies have compared DNA methylation in patients with sporadic PNETs to that in patients with PNETs associated with hereditary syndromes (MEN1 and VHL) (Conemans et al. 2018, Tirosh et al. 2019). One study compared cumulative methylation indices and gene-specific methylation levels in 56 TSGs in 95 PNETs (61 MEN1 vs 34 sporadic) and reported that overall DNA methylation levels were comparable and that DNA methylation was increased in larger tumours and in metastatic disease (Conemans et al. 2018). However, another study compared global methylation levels in 30 non-functional PNETs (10 sporadic and 10 each from patients with MEN1 and VHL) and four normal islet samples, using the Illumina MethylationEPIC array, and reported significantly increased DNA methylation in patients with MEN1 than that in sporadic and VHL-associated PNETs. This global hypermethylation seen in PNETs from patients with MEN1 was also seen in two MEN1-knockout mouse models, (Pdx1-Cre: MEN1 floxed/floxed (pancreatic) and Pth-Cre:Men1 floxed/floxed (parathyroid)). The observed hypermethylation in the Pth-Cre:Men1 floxed/floxed was consistent with that reported in 12 patients with MEN1-associated hyperparathyroidism, when compared to 13 sporadic parathyroid adenomas, 4 parathyroid carcinomas, and 9 normal parathyroids, using HpaII tiny fragment enrichment by ligation-mediated PCR (HELP), which specifically measures global 5mC marks (Kinney et al. 2011, Yuan et al. 2016). These reported differences in MEN1-associated DNA methylation levels may, however, partly be explained by study methodology, as two studies assessed global DNA methylation levels (methylationEPIC array and HELP, respectively) whereas, one study examined 56 specific TSGs, by MS-MLPA (Nygren et al. 2005, Conemans et al. 2018, Tirosh et al. 2019).
Patients with MEN1 syndrome mainly develop tumours in endocrine organs, including the pituitary, pancreas and parathyroid glands; however, it is unclear why menin loss specifically increases the risk of tumours in these particular organs and not others. Different DNA methylation patterns have been reported in vivo in mouse endocrine vs exocrine pancreatic tissue in the Pdx1-Cre: MEN1 floxed/floxed, menin-knockout mouse model (Yuan et al. 2016). The gene RB-binding protein 5, histone lysine methyltransferase complex subunit (RBBP5), which encodes for the RbBP5 protein, one of the subunits involved in the WRAD complex (WDR5, RbBP5, Ash2L and Dpy30) that is required by KMT2A/B for H3K4 methylation (Mittal et al. 2018), binds to the DNMT1 promoter in both endocrine and exocrine tissue, however, increased DNMT1 expression is only observed in the endocrine pancreas (Yuan et al. 2016), and this may be associated with (or due to) menin loss that can lead to increased DNA methylation via increased DNMT1 expression (Fig. 2). Pathways enriched for hypermethylated genes in tumours developing in MEN1-knockout mice included those involved in the Wnt/beta-catenin pathway, with increased beta-catenin levels secondary to loss of Sox-regulatory proteins by promoter methylation (Yuan et al. 2016). In PNETs from patients with MEN1, promoter methylation in two genes has been reported: cell division cycle associated 7 like and RNA-binding motif protein 47 (Tirosh et al. 2019), with aberrant expression reported in other malignancies, including paediatric pineal germinomas and colorectal cancer (Perez-Ramirez et al. 2017, Rokavec et al. 2017). Findings from these studies may be explained by menin-mediating H3K4 methylation, an active histone mark, which may protect DNA from methylation (Cedar & Bergman 2009), or alternatively, menin loss may lead to increased global DNA methylation and gene specific TSG methylation (Fig. 2) (Iyer & Agarwal 2018). Loss of menin expression in both endocrine and exocrine cells, as occurring in the Pdx1-Cre: MEN1 floxed/floxed mouse model, was not observed to alter DNMT1 expression in the exocrine pancreas, thereby suggesting that menin is important in maintaining the DNA methylome in endocrine cells, and this may provide an explanation for the predominant development of tumours in endocrine organs in patients with MEN1.
Translational utility of PNET DNA methylation
DNA methylation to define and stratify PNETs
There are five types of endocrine cells within the islets of Langerhans, which comprise ~54% beta (insulin-secreting), ~35% alpha (glucagon-secreting), and ~11% delta (somatostatin-secreting), with a small number of gamma/pancreatic polypeptide (PP-secreting) cells (Lawlor et al. 2017). Epigenetic signatures have been used to stratify PNETs into distinct categories, using either enhancer maps (histones marks) (Cejas et al. 2019) or DNA methylation. There have been five studies comparing the DNA methylation signatures of PNETs to either normal pancreatic islet methylomes or PNECs to stratify these into different groups (Chan et al. 2018, Boons et al. 2020, Di Domenico et al. 2020, Lakis et al. 2021, Yachida et al. 2022). Two studies which stratified PNETs from PNECs reported that PNECs have a similar methylation profile to exocrine pancreatic tissue (Simon et al. 2022, Yachida et al. 2022). One of these studies used multiomic data to further stratify PNECs into ‘ductal’ (retinoblastoma-associated protein (RB1) protein loss, tumour protein 53 (TP53) mutations, and a CpG island methylator phenotype (CIMP) phenotype) and ‘acinar’ (CDKN2A alterations, deletions or promoter hypermethylation, and WNT signalling alterations) subtypes (Yachida et al. 2022). These studies provide further evidence that PNECs are a distinct biological entity when compared to PNETs and highlight the importance of accurate tumour diagnosis to ensure that patients receive the appropriate therapies. The DNA methylome has been reported in the two most common islet cell types alpha and beta cells, each with their own unique methylation signature (Boons et al. 2020, Di Domenico et al. 2020, Simon et al. 2022). The methylation signature of insulinomas (pancreatic islet tumours which secrete excess insulin) closely aligns with that of a normal beta cell methylation profile, consistent with its cell of origin (Di Domenico et al. 2020). Insulinomas account for 10% of PNETs seen in patients with MEN1 (Larsson et al. 1988, Thakker et al. 2012); however, sporadic insulinomas (and other functional PNETs) (Lakis et al. 2021) are frequently wildtype for MEN1, ATRX, or DAXX (Cao et al. 2013, Di Domenico et al. 2020, Lakis et al. 2021) and express menin (Arnold et al. 2007). The most common reported genetic driver of insulinomas (seen in up to 30%) involves the amino acid mutation Thr372Arg in Yin Yang 1(YY1) which acts through the mTOR pathway (Cao et al. 2013, Hong et al. 2020). YY1 is an evolutionary conserved ubiquitous protein involved in transcriptional activation or repression by recruitment of histone methyltransferases and plays a crucial role in ensuring LINE-1 methylation (Seto et al. 1991, Rezai-Zadeh et al. 2003, Sanchez-Luque et al. 2019). PNETs which share a similar methylation signature to normal alpha-cells (high ARX and low PDX1) tend to harbour only MEN1 mutations or have lost menin expression (Di Domenico et al. 2020, Lakis et al. 2021, Yachida et al. 2022). However, the majority of PNETs display a methylation signature somewhere between alpha and beta cells, with approximately 70% of these harbouring mutations in MEN1 and/or ATRX and DAXX (Di Domenico et al. 2020). PNETs have also been stratified into A-D-M (ATRX/DAXX/MEN1) mutant vs wildtype, with A-D-M mutant PNETs tending to display similar methylation features to that of an alpha cell, whereas, A-D-M wildtype PNETs were more heterogenous with a subset showing similar profiles to beta-cells (Chan et al. 2018). DNA methylation patterns have also been used to classify NETs by location and to determine the origin of NETs of unknown primary (How-Kit et al. 2015, Hackeng et al. 2021).
Clinical outcomes in PNETs
In PNETs, the majority of studies have looked at the presence or absence of a specific TSG and have either correlated this with overall survival (Ohki et al. 2014, Schmitt et al. 2014, Stefanoli et al. 2014, Ushiku et al. 2016), with increased tumour grade (Dejeux et al. 2009), or with the presence of metastasis (Dammann et al. 2003, Wild et al. 2003, Choi et al. 2007). RASSF1 and CDKN2A were found to be methylated in 100% and 40% of patients with metastatic disease vs 71% and 0% without metastases (Dammann et al. 2003). Multivariate analysis, controlling for clinical factors including tumour grade, size, and stage, reported that CDKN2A (House et al. 2003) and MGMT (Schmitt et al. 2014, Ban et al. 2022) methylation, but not RASSF1 (House et al. 2003), were associated with mortality. Other studies have reported that low MGMT expression and promoter hypomethylation predict response to temozolomide chemotherapy (Cros et al. 2016, Campana et al. 2018). However, a low MGMT expression was also observed in a high proportion of patients (75% (6/8)) with PNETs who did not respond to temozolomide, and it seems that a low MGMT expression has a high sensitivity, but low specificity to predict temozolomide response (Cros et al. 2016). TIMP metallopeptidase inhibitor 3 methylation has also been associated with metastatic disease, with strong staining seen in normal islets and decreased expression in 55% of patients with PNETs on univariate analysis (Wild et al. 2003). CIMP positivity, defined as multiple methylated TSGs (although there is no clear cutoff to determine CIMP positivity), has been associated with multiple cancers, including those of the colorectum, lung and prostate, and gliomas (Toyota & Issa 1999, Yates & Boeva 2022). Investigation of the CIMP-positive phenotype in PNETs has indicated that it is associated with a poorer overall survival (Arnold et al. 2007, Stefanoli et al. 2014). In addition, progressive LINE-1 hypomethylation has been associated with increased mortality (Stefanoli et al. 2014), lymph node metastases (Choi et al. 2007), and tumour grade (Choi et al. 2007, Stricker et al. 2012). No study has reported a difference in PNET methylation between sexes (Choi et al. 2007, Campana et al. 2018, Boons et al. 2020, Ban et al. 2022), although loss of global 5hmC was reported to be associated with tumourigenesis and to correlate with distant metastasis and female gender in a multivariate analysis of 60 well-differentiated PNETs (Sharma et al. 2022). The DNA methylome has also been used to cluster PNETs into different prognostic categories (Chan et al. 2018, Boons et al. 2020, Di Domenico et al. 2020, Simon et al. 2022). Studies clustering PNETs into two categories (A-D-M/ATRX-DAXX-MEN1) mutant vs wildtype have reported that the A-D-M mutant category (ARX positive and PDX1 negative) had an overall worse prognosis, when compared to A-D-M wildtype (Chan et al. 2018) and PNETs with a beta-like cell methylation signature (Boons et al. 2020, Lakis et al. 2021). Other studies clustering PNETs into alpha-like, beta-like, or intermediate tumours have reported that intermediate tumours tend to be less differentiated and of higher grade when compared to the alpha-like or beta-like PNETs and that using the DNA methylome to stratify PNETs into these three groups more accurately predicted disease-free survival when compared to the analysis of transcription factor expression, by immunohistochemistry for alpha-cell specific (ARX), beta-cell specific (PDX1), or intermediate (DAXX/ATRX) alone (Di Domenico et al. 2020). Similar results were found in a large international cohort of NETs, including 561 primary NF-PNETs and 107 metastatic NF-PNETs, which reported that ARX or PDX1 expression did not independently predict relapse-free survival (RFS), whereas ATRX/DAXX loss and alternative lengthening of telomeres (ALT) status were both independent predictors of RFS (Hackeng et al. 2022). Using DNA methylation to compare PNETs to their differentiated non-cancerous counterparts (i.e. alpha and beta cells) has been used to prognostically stratify patients, and utilising this methodology appears to be more discriminative in terms of predicting prognosis. Thus, studies have reported that beta cell phenotypes tend to have a better prognosis; however, it is important to note that tumours secreting hormones (e.g. insulin) tend to be detected at earlier stages than non-secreting (i.e. non-functioning) tumours, and this may be a confounding factor if it is the cell of origin that determines tumour aggressiveness. Given that the majority of insulinomas are indolent/typical (i.e. non-metastatic), epigenetic signatures comparing indolent vs aggressive (i.e. metastatic) insulinomas have not been investigated, likely due to the rare nature of metastatic insulinomas. One recent study reported ARX expression in all aggressive compared to indolent insulinomas and suggested that these aggressive tumours originated from an alpha-like cell and inappropriately gained insulin secretion (Hackeng et al. 2020). Another example of an inappropriate gain in secretory properties of PNETs is ectopic adrenocorticotrophin (ACTH)-secreting PNETs. One study reported lower pro-opiomelanocortin methylation of seven ACTH-PNETs when compared to seven clinically NF-PNETs. The 1-year survival for patients with ACTH-secreting PNETs was 57% (Zhang et al. 2020). This poor overall survival seen in patients with ACTH-secreting PNETs may be explained by the high morbidity associated with uncontrolled hypercortisolism (Kamp et al. 2016), and by the fact that islet cells do not normally secrete ACTH and therefore, such PNETs may harbour other epigenetic and/or genetic abnormalities which carry a poorer prognosis.
Therapeutic targeting of aberrant DNA methylation
The most common class of anti-cancer drugs used to alter the DNA methylome are inhibitors of DNMTs. DNMT inhibitors (DNMTi) may show efficacy by improving the cancer phenotype, directly (through the re-expression of the apoptotic pathway and/or cell cycle inhibitors) or indirectly by the re-expression of receptors or transcription factors which may help to overcome drug resistance, as seen with other types of chemotherapy. Azacitadine and its derivative decitabine (5–2’-deoxycytidine; first-generation DNMTi) and guadecitabine (second generation) are incorporated into replicating DNA in place of cytidine. DNMTs methylate this incorporated analogue but are unable to dissociate from DNA and are subsequently degraded, thereby leading to overall DNMT depletion and subsequent loss of DNA methylation (Hu et al. 2021). These drugs have been assessed using the human PNET cell lines (BON-1 (derived from a lymph node with metastatic insulinoma) (Townsend et al. 1993), QGP-1 (derived from a pancreatic somatostatinoma) (Kaku et al. 1980), and/or CM (derived from ascitic fluid from a metastatic insulinoma)) (Baroni et al. 1999) and were found to increase the expression of RASSF1A (Malpeli et al. 2011) and SSTR2 in vitro and in an in vivo (mouse xenograft) model (Taelman et al. 2016, Evans et al. 2022a ). Despite being used clinically for other malignancies, e.g. haematological malignancies, this class of drug has only been used in one small clinical trial of nine patients with NETs, including two patients with PNETs, who exhibited low baseline SSTR2 expression on 68Ga-DOTATE imaging (Refardt et al. 2022). In this study, hydralazine, a common anti-hypertensive medication, which in this case was utilised for its DNMTi properties, was administered daily in combination with an HDAC inhibitor (valproic acid, a common anti-epileptic medication), with the aim of upregulating SSTR2. Despite previous in vitro and in vivo (mouse xenograft) models reporting upregulation of SSTR2 (Taelman et al. 2016, Evans et al. 2022a ) using decitabine or guadecitabine, hydralazine treatment was unable to upregulate SSTR2 in either BON-1 or human PNETs (Refardt et al. 2022). The current cell lines used to investigate DNA methylation in PNETs tend to be highly proliferative and harbour genetic mutations similar to those found in PNECs (e.g. KRAS proto-oncogene GTPase (KRAS) mutations in (KRAS) mutations found in the QGP-1 cell line) (Kaku et al. 1980), and therefore the direct translatability of results from using these cells lines in vitro to the less proliferative human PNETs, which do not tend to harbour these genetic mutations in vivo, is unclear. Temozolomide therapy, with and without capecitabine, has been used in the treatment of neuroendocrine tumours including PNETs. Temozolomide (a type of chemotherapy drug), which works as an alkylating agent by forming adducts on the O6 and N7 positions of guanine and without MGMT to remove these, leads to cell death (Campana et al. 2018). Temozolomide-based chemotherapy has been reported in two clinical studies in a total of 138 PNETs, which reported low MGMT expression as a strong predictive factor for longer progression-free and overall survival (Cros et al. 2016, Campana et al. 2018). Somatostatin analogues (SSAs) are the most commonly used medical therapy for patients with PNETs, with different SSA compounds having different affinities for the somatostatin receptor subtypes (SSTR1–5). It has been reported that response to SSA treatment does not solely depend on the tumour receptor subtype expression and that other tumour factors modulate its treatment effect, for example, the natural antisense transcript of SSTR5-ASI (Pedraza-Arevalo et al. 2022).
Conclusions and future perspectives
Studies examining PNET DNA methylation levels have largely focussed on a specific subset of TSGs using MSPs, and only a subset of these have correlated protein expression with clinical outcomes. However, given the increasing availability of methylation array-based technologies in conjunction with RNA-seq, further studies looking at how changes in the DNA methylome affect cellular phenotype are likely to become mainstream. Given that 5hmC has been shown to be associated with gene transcription and protects CpG sites from methylation (5mC), newer techniques which are able to separate the specific 5’ cytosine modifications and correlate these with gene expression are needed. Current therapies (e.g. DNMTi) used in vitro have shown efficacy in PNET cell lines via re-expressing TSGs, but these cell lines tend to be highly proliferative and not representative of the more common relatively indolent PNETs, and studies in more representative cell lines and models are required. Investigating the PNET DNA methylome and using this to determine its cell of origin (i.e. alpha/beta/indeterminate cell like) will help progress in PNET research by clustering tumour subtypes epigenetically, which may help to prognostically stratify patients and to guide which patients may benefit from targeted epigenetic therapy. As yet, there have been no reported studies looking at changing the DNA methylome to improve cellular phenotype (and response to other anti-cancer agents in combination), which is likely to be an important way forward, particularly given that PNETs displaying similar phenotypes to normal alpha and beta cells have a more favourable prognosis.
Declaration of interest
There is no conflict of interest that could be perceived as prejudicing the impartiality of this review.
Funding
This work was supported by Cancer Research UK (CRUK) grant number C2195/A28699, through a CRUK Oxford Centre Clinical Research Training Fellowship (KE); National Institute for Health Research (NIHR) Senior Investigator Award (RVT); and NIHR Oxford Biomedical Research Centre Programme (KL,RVT).
Author contributions statement
KE wrote the manuscript; RVT and KL edited the manuscript.
References
Ahmad S, Naber MR, Giles RH, Valk GD & & Van Leeuwaarde RS 2021 Diagnostic and management strategies for pNETs in von Hippel-Lindau: a systematic review. Endocrine-Related Cancer 28 151–160. (https://doi.org/10.1530/ERC-20-0469)
Annunziato A 2008 DNA packaging: Nucleosomes and chromatin. Nature Education 1 26.
Arnold CN, Sosnowski A, Schmitt-Graff A, Arnold R & & Blum HE 2007 Analysis of molecular pathways in sporadic neuroendocrine tumors of the gastro-entero-pancreatic system. International Journal of Cancer 120 2157–2164. (https://doi.org/10.1002/ijc.22569)
Ban X, , Mo S, , Lu Z, , Jia C, , Shao H, , Chang X, , Mao X, , Zhang Y, , Pang J, , Zhang Y, et al.2022 Expression and methylation status of MMR and MGMT in well-differentiated pancreatic neuroendocrine tumors and potential clinical applications. Endocrine 77 538–545. (https://doi.org/10.1007/s12020-022-03102-y)
Baroni MG, Cavallo MG, Mark M, Monetini L, Stoehrer B & & Pozzilli P 1999 Beta-cell gene expression and functional characterisation of the human insulinoma cell line CM. Journal of Endocrinology 161 59–68. (https://doi.org/10.1677/joe.0.1610059)
Bartsch DK, Kersting M, Wild A, Ramaswamy A, Gerdes B, Schuermann M, Simon B & & Rothmund M 2000 Low frequency of <i>p16<sup>INK4a</sup></i> alterations in insulinomas. Digestion 62 171–177. (https://doi.org/10.1159/000007810)
Beacon TH, Delcuve GP, Lopez C, Nardocci G, Kovalchuk I, Van Wijnen AJ & & Davie JR 2021 The dynamic broad epigenetic (H3K4me3, H3K27ac) domain as a mark of essential genes. Clinical Epigenetics 13 138. (https://doi.org/10.1186/s13148-021-01126-1)
Bird AP 1986 CpG-rich islands and the function of DNA methylation. Nature 321 209–213. (https://doi.org/10.1038/321209a0)
Boons G, Vandamme T, Ibrahim J, Roeyen G, Driessen A, Peeters D, Lawrence B, Print C, Peeters M, Van Camp G, et al.2020 PDX1 DNA methylation distinguishes two subtypes of pancreatic neuroendocrine neoplasms with a different prognosis. Cancers 12 1461. (https://doi.org/10.3390/cancers12061461)
Campana D, Walter T, Pusceddu S, Gelsomino F, Graillot E, Prinzi N, Spallanzani A, Fiorentino M, Barritault M, Dall'olio F, et al.2018 Correlation between MGMT promoter methylation and response to temozolomide-based therapy in neuroendocrine neoplasms: an observational retrospective multicenter study. Endocrine 60 490–498. (https://doi.org/10.1007/s12020-017-1474-3)
Cao Y, Gao Z, Li L, Jiang X, Shan A, Cai J, Peng Y, Li Y, Jiang X, Huang X, et al.2013 Whole exome sequencing of insulinoma reveals recurrent T372R mutations in YY1. Nature Communications 4 2810. (https://doi.org/10.1038/ncomms3810)
Carnell AN & & Goodman JI 2003 The long (LINEs) and the short (SINEs) of it: altered methylation as a precursor to toxicity. Toxicological Sciences 75 229–235. (https://doi.org/10.1093/toxsci/kfg138)
Cedar H & & Bergman Y 2009 Linking DNA methylation and histone modification: patterns and paradigms. Nature Reviews. Genetics 10 295–304. (https://doi.org/10.1038/nrg2540)
Cejas P, Drier Y, Dreijerink KMA, Brosens LAA, Deshpande V, Epstein CB, Conemans EB, Morsink FHM, Graham MK, Valk GD, et al.2019 Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors. Nature Medicine 25 1260–1265. (https://doi.org/10.1038/s41591-019-0493-4)
Chalitchagorn K, Shuangshoti S, Hourpai N, Kongruttanachok N, Tangkijvanich P, Thong-Ngam D, Voravud N, Sriuranpong V & & Mutirangura A 2004 Distinctive pattern of LINE-1 methylation level in normal tissues and the association with carcinogenesis. Oncogene 23 8841–8846. (https://doi.org/10.1038/sj.onc.1208137)
Chamberlain CE, Scheel DW, Mcglynn K, Kim H, Miyatsuka T, Wang J, Nguyen V, Zhao S, Mavropoulos A, Abraham AG, et al.2014 Menin determines K-RAS proliferative outputs in endocrine cells. Journal of Clinical Investigation 124 4093–4101. (https://doi.org/10.1172/JCI69004)
Chan AO-O, Kim SG, Bedeir A, Issa JP, Hamilton SR & & Rashid A 2003 CpG island methylation in carcinoid and pancreatic endocrine tumors. Oncogene 22 924–934. (https://doi.org/10.1038/sj.onc.1206123)
Chan CS, Laddha SV, Lewis PW, Koletsky MS, Robzyk K, Da Silva E, Torres PJ, Untch BR, Li J, Bose P, et al.2018 ATRX, DAXX or MEN1 mutant pancreatic neuroendocrine tumors are a distinct alpha-cell signature subgroup. Nature Communications 9 4158. (https://doi.org/10.1038/s41467-018-06498-2)
Chen RZ, Pettersson U, Beard C, Jackson-Grusby L & & Jaenisch R 1998 DNA hypomethylation leads to elevated mutation rates. Nature 395 89–93. (https://doi.org/10.1038/25779)
Choi IS, Estecio MRH, Nagano Y, Kim DH, White JA, Yao JC, Issa JP & & Rashid A 2007 Hypomethylation of LINE-1 and Alu in well-differentiated neuroendocrine tumors (pancreatic endocrine tumors and carcinoid tumors). Modern Pathology 20 802–810. (https://doi.org/10.1038/modpathol.3800825)
Conemans EB, Lodewijk L, Moelans CB, Offerhaus GJA, Pieterman CRC, Morsink FH, Dekkers OM, De Herder WW, Hermus AR, Van Der Horst-Schrivers AN, et al.2018 DNA methylation profiling in MEN1-related pancreatic neuroendocrine tumors reveals a potential epigenetic target for treatment. European Journal of Endocrinology 179 153–160. (https://doi.org/10.1530/EJE-18-0195)
Crona J & & Skogseid B 2016 GEP- NETS UPDATE: genetics of neuroendocrine tumors. European Journal of Endocrinology 174 R275–R290. (https://doi.org/10.1530/EJE-15-0972)
Cros J, Hentic O, Rebours V, Zappa M, Gille N, Theou-Anton N, Vernerey D, Maire F, Lévy P, Bedossa P, et al.2016 MGMT expression predicts response to temozolomide in pancreatic neuroendocrine tumors. Endocrine-Related Cancer 23 625–633. (https://doi.org/10.1530/ERC-16-0117)
Dammann R, Schagdarsurengin U, Liu L, Otto N, Gimm O, Dralle H, Boehm BO, Pfeifer GP & & Hoang-Vu C 2003 Frequent RASSF1A promoter hypermethylation and K-ras mutations in pancreatic carcinoma. Oncogene 22 3806–3812. (https://doi.org/10.1038/sj.onc.1206582)
de Laat JM, Van Der Luijt RB, Pieterman CR, Oostveen MP, Hermus AR, Dekkers OM, De Herder WW, Van Der Horst-Schrivers AN, Drent ML, Bisschop PH, et al.2016 MEN1 redefined, a clinical comparison of mutation-positive and mutation-negative patients. BMC Medicine 14 182. (https://doi.org/10.1186/s12916-016-0708-1)
Dejeux E, Olaso R, Dousset B, Audebourg A, Gut IG, Terris B & & Tost J 2009 Hypermethylation of the IGF2 differentially methylated region 2 is a specific event in insulinomas leading to loss-of-imprinting and overexpression. Endocrine-Related Cancer 16 939–952. (https://doi.org/10.1677/ERC-08-0331)
Di Domenico A, Pipinikas CP, Maire RS, Bräutigam K, Simillion C, Dettmer MS, Vassella E, Thirlwell C, Perren A & & Marinoni I 2020 Epigenetic landscape of pancreatic neuroendocrine tumours reveals distinct cells of origin and means of tumour progression. Communications Biology 3 740. (https://doi.org/10.1038/s42003-020-01479-y)
Du Q, Luu PL, Stirzaker C & & Clark SJ 2015 Methyl-CpG-binding domain proteins: readers of the epigenome. Epigenomics 7 1051–1073. (https://doi.org/10.2217/epi.15.39)
Eden A, Gaudet F, Waghmare A & & Jaenisch R 2003 Chromosomal instability and tumors promoted by DNA hypomethylation. Science 300 455–455. (https://doi.org/10.1126/science.1083557)
Ehrlich M 2009 DNA hypomethylation in cancer cells. Epigenomics 1 239–259. (https://doi.org/10.2217/epi.09.33)
Evans JS, Beaumont J, Braga M, Masrour N, Mauri F, Beckley A, Butt S, Karali CS, Cawthorne C, Archibald S, et al.2022a Epigenetic potentiation of somatostatin-2 by guadecitabine in neuroendocrine neoplasias as a novel method to allow delivery of peptide receptor radiotherapy. European Journal of Cancer 176 110–120. (https://doi.org/10.1016/j.ejca.2022.09.009)
Evans LM, Geenen KR, O'Shea A, Hedgire SS, Ferrone CR & & Thiele EA 2022b Tuberous sclerosis complex-associated nonfunctional pancreatic neuroendocrine tumors: management and surgical outcomes. American Journal of Medical Genetics. Part A 188 2666–2671. (https://doi.org/10.1002/ajmg.a.62850)
Hackeng WM, Dreijerink KMA, De Leng WWJ, Morsink FHM, Valk GD, Vriens MR, Offerhaus GJA, Geisenberger C & & Brosens LAA 2021 Genome methylation accurately predicts neuroendocrine tumor origin: an online tool. Clinical Cancer Research 27 1341–1350. (https://doi.org/10.1158/1078-0432.CCR-20-3281)
Hackeng WM, Schelhaas W, Morsink FHM, Heidsma CM, Van Eeden S, Valk GD, Vriens MR, Heaphy CM, Nieveen Van Dijkum EJM, Offerhaus GJA, et al.2020 Alternative lengthening of telomeres and differential expression of endocrine transcription factors distinguish metastatic and non-metastatic insulinomas. Endocrine Pathology 31 108–118. (https://doi.org/10.1007/s12022-020-09611-8)
Hackeng WM, Brosens LAA, Kim JY, O’sullivan R, Sung YN, Liu TC, Cao D, Heayn M, Brosnan-Cashman J, An S, et al.2022 Non-functional pancreatic neuroendocrine tumours: ATRX/DAXX and alternative lengthening of telomeres (ALT) are prognostically independent from ARX/PDX1 expression and tumour size. Gut 71 961–973. (https://doi.org/10.1136/gutjnl-2020-322595)
Hancks DC & & Kazazian HH 2016 Roles for retrotransposon insertions in human disease. Mobile DNA 7 9. (https://doi.org/10.1186/s13100-016-0065-9)
Hong X, Qiao S, Li F, Wang W, Jiang R, Wu H, Chen H, Liu L, Peng J, Wang J, et al.2020 Whole-genome sequencing reveals distinct genetic bases for insulinomas and non-functional pancreatic neuroendocrine tumours: leading to a new classification system. Gut 69 877–887. (https://doi.org/10.1136/gutjnl-2018-317233)
House MG, Herman JG, Guo MZ, Hooker CM, Schulick RD, Lillemoe KD, Cameron JL, Hruban RH, Maitra A & & Yeo CJ 2003 Aberrant hypermethylation of tumor suppressor genes in pancreatic endocrine neoplasms. Annals of Surgery 238 423–432. (https://doi.org/10.1097/01.sla.0000086659.49569.9e)
How-Kit A, Dejeux E, Dousset B, Renault V, Baudry M, Terris B & & Tost J 2015 DNA methylation profiles distinguish different subtypes of gastroenteropancreatic neuroendocrine tumors. Epigenomics 7 1245–1258. (https://doi.org/10.2217/epi.15.85)
Hu C, Liu X, Zeng Y, Liu J & & Wu F 2021 DNA methyltransferase inhibitors combination therapy for the treatment of solid tumor: mechanism and clinical application. Clinical Epigenetics 13 166. (https://doi.org/10.1186/s13148-021-01154-x)
Hyun K, Jeon J, Park K & & Kim J 2017 Writing, erasing and reading histone lysine methylations. Experimental and Molecular Medicine 49 e324. (https://doi.org/10.1038/emm.2017.11)
Ito S, D'alessio AC, Taranova OV, Hong K, Sowers LC & & Zhang Y 2010 Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature 466 1129–1133. (https://doi.org/10.1038/nature09303)
Iyer S & & Agarwal SK 2018 Epigenetic regulation in the tumorigenesis of MEN1-associated endocrine cell types. Journal of Molecular Endocrinology 61 R13–R24. (https://doi.org/10.1530/JME-18-0050)
Jiao Y, Shi C, Edil BH, De Wilde RF, Klimstra DS, Maitra A, Schulick RD, Tang LH, Wolfgang CL, Choti MA, et al.2011 DAXX/ATRX, MEN1, and mTOR pathway genes are frequently altered in pancreatic neuroendocrine tumors. Science 331 1199–1203. (https://doi.org/10.1126/science.1200609)
Jones PA & & Baylin SB 2002 The fundamental role of epigenetic events in cancer. Nature Reviews. Genetics 3 415–428. (https://doi.org/10.1038/nrg816)
Kaku M, Nishiyama T, Yagawa K & & Abe M 1980 Establishment of a carcinoembryonic antigen-producing cell line from human pancreatic carcinoma. Gan 71 596–601.
Kamp K, Alwani RA, Korpershoek E, Franssen GJ, De Herder WW & & Feelders RA 2016 Prevalence and clinical features of the ectopic ACTH syndrome in patients with gastroenteropancreatic and thoracic neuroendocrine tumors. European Journal of Endocrinology 174 271–280. (https://doi.org/10.1530/EJE-15-0968)
Kinney SM, Chin HG, Vaisvila R, Bitinaite J, Zheng Y, Estève PO, Feng S, Stroud H, Jacobsen SE & & Pradhan S 2011 Tissue-specific distribution and dynamic changes of 5-hydroxymethylcytosine in mammalian genomes. Journal of Biological Chemistry 286 24685–24693. (https://doi.org/10.1074/jbc.M110.217083)
Kohli RM & & Zhang Y 2013 TET enzymes, TDG and the dynamics of DNA demethylation. Nature 502 472–479. (https://doi.org/10.1038/nature12750)
Lakis V, Lawlor RT, Newell F, Patch A-M, Mafficini A, Sadanandam A, Koufariotis LT, Johnston RL, Leonard C, Wood S, et al.2021 DNA methylation patterns identify subgroups of pancreatic neuroendocrine tumors with clinical association. Communications Biology 4 155. (https://doi.org/10.1038/s42003-020-01469-0)
Larsson C, Skogseid B, Oberg K, Nakamura Y & & Nordenskjöld M 1988 Multiple endocrine neoplasia type 1 gene maps to chromosome 11 and is lost in insulinoma. Nature 332 85–87. (https://doi.org/10.1038/332085a0)
Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, Kycia I, Robson P & & Stitzel ML 2017 Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Research 27 208–222. (https://doi.org/10.1101/gr.212720.116)
Li J, Wu X, Zhou Y, Lee M, Guo L, Han W, Mo W, Cao WM, Sun D, Xie R, et al.2018 Decoding the dynamic DNA methylation and hydroxymethylation landscapes in endodermal lineage intermediates during pancreatic differentiation of hESC. Nucleic Acids Research 46 2883–2900. (https://doi.org/10.1093/nar/gky063)
Liu B, Tang LH, Liu Z, Mei M, Yu R, Dhall D, Qiao XW, Zhang TP, Zhao YP, Liu TH, et al.2014 Alpha-internexin: a novel biomarker for pancreatic neuroendocrine tumor aggressiveness. Journal of Clinical Endocrinology and Metabolism 99 E786–E795. (https://doi.org/10.1210/jc.2013-2874)
Liu L, Broaddus RR, Yao JC, Xie S, White JA, Wu TT, Hamilton SR & & Rashid A 2005 Epigenetic alterations in neuroendocrine tumors: methylation of RAS-association domain family 1, isoform A and p16 genes are associated with metastasis. Modern Pathology 18 1632–1640. (https://doi.org/10.1038/modpathol.3800490)
Malpeli G, Amato E, Dandrea M, Fumagalli C, Debattisti V, Boninsegna L, Pelosi G, Falconi M & & Scarpa A 2011 Methylation-associated down-regulation of RASSF1A and up-regulation of RASSF1C in pancreatic endocrine tumors. BMC Cancer 11 351. (https://doi.org/10.1186/1471-2407-11-351)
Marinoni I, Wiederkeher A, Wiedmer T, Pantasis S, Di Domenico A, Frank R, Vassella E, Schmitt A & & Perren A 2017 Hypo-methylation mediates chromosomal instability in pancreatic NET. Endocrine-Related Cancer 24 137–146. (https://doi.org/10.1530/ERC-16-0554)
Mittal A, Hobor F, Zhang Y, Martin SR, Gamblin SJ, Ramos A & & Wilson JR 2018 The structure of the RbBP5 beta-propeller domain reveals a surface with potential nucleic acid binding sites. Nucleic Acids Research 46 3802–3812. (https://doi.org/10.1093/nar/gky199)
Moore LD, Le T & & Fan G 2013 DNA methylation and its basic function. Neuropsychopharmacology 38 23–38. (https://doi.org/10.1038/npp.2012.112)
Muscarella P, Melvin WS, Fisher WE, Foor J, Ellison EC, Herman JG, Schirmer WJ, Hitchcock CL, Deyoung BR & & Weghorst CM 1998 Genetic alterations in gastrinomas and nonfunctioning pancreatic neuroendocrine tumors: an analysis of p16/MTS1 tumor suppressor gene inactivation. Cancer Research 58 237–240.
Nygren AO, Ameziane N, Duarte HM, Vijzelaar RN, Waisfisz Q, Hess CJ, Schouten JP & & Errami A 2005 Methylation-specific MLPA (MS-MLPA): simultaneous detection of CpG methylation and copy number changes of up to 40 sequences. Nucleic Acids Research 33 e128. (https://doi.org/10.1093/nar/gni127)
Ohki R, Saito K, Chen Y, Kawase T, Hiraoka N, Saigawa R, Minegishi M, Aita Y, Yanai G, Shimizu H, et al.2014 PHLDA3 is a novel tumor suppressor of pancreatic neuroendocrine tumors. Proceedings of the National Academy of Sciences of the United States of America 111 E2404–E2413. (https://doi.org/10.1073/pnas.1319962111)
Olkhov-Mitsel E & & Bapat B 2012 Strategies for discovery and validation of methylated and hydroxymethylated DNA biomarkers. Cancer Medicine 1 237–260. (https://doi.org/10.1002/cam4.22)
Papaspyropoulos A, Bradley L, Thapa A, Leung CY, Toskas K, Koennig D, Pefani DE, Raso C, Grou C, Hamilton G, et al.2018 RASSF1A uncouples Wnt from Hippo signalling and promotes YAP mediated differentiation via p73. Nature Communications 9 424. (https://doi.org/10.1038/s41467-017-02786-5)
Pedraza-Arevalo S, Ibáñez-Costa A, Blázquez-Encinas R, Branco MR, Vázquez-Borrego MC, Herrera-Martínez AD, Venegas-Moreno E, Serrano-Blanch R, Arjona-Sánchez Á, Gálvez-Moreno MA, et al.2022 Epigenetic and post-transcriptional regulation of somatostatin receptor subtype 5 (SST(5) ) in pituitary and pancreatic neuroendocrine tumors. Molecular Oncology 16 764–779. (https://doi.org/10.1002/1878-0261.13107)
Perez-Ramirez M, Hernandez-Jimenez AJ, Guerrero-Guerrero A, Siordia-Reyes AG, Hernandez-Caballero ME, Garcia-Mendez A, Chico-Ponce de Leon F, Salamanca-Gomez FA & & Garcia-Hernandez N 2017 Pediatric pineal germinomas: Epigenetic and genomic approach. Clinical Neurology and Neurosurgery 152 4 5–51. (https://doi.org/10.1016/j.clineuro.2016.11.012)
Refardt J, Klomp MJ, Van Koetsveld PM, Dogan F, Konijnenberg M, Brabander T, Feelders RA, De Herder WW, Hofland LJ & & Hofland J 2022 Effect of epigenetic treatment on SST2 expression in neuroendocrine tumour patients. Clinical and Translational Medicine 12 e957. (https://doi.org/10.1002/ctm2.957)
Ren R, Horton JR, Zhang X, Blumenthal RM & & Cheng X 2018 Detecting and interpreting DNA methylation marks. Current Opinion in Structural Biology 53 88–99. (https://doi.org/10.1016/j.sbi.2018.06.004)
Rezai-Zadeh N, Zhang X, Namour F, Fejer G, Wen YD, Yao YL, Gyory I, Wright K & & Seto E 2003 Targeted recruitment of a histone H4-specific methyltransferase by the transcription factor YY1. Genes and Development 17 1019–1029. (https://doi.org/10.1101/gad.1068003)
Rokavec M, Kaller M, Horst D & & Hermeking H 2017 Pan-cancer EMT-signature identifies RBM47 down-regulation during colorectal cancer progression. Scientific Reports 7 4687. (https://doi.org/10.1038/s41598-017-04234-2)
Romanet P, Mohamed A, Giraud S, Odou MF, North MO, Pertuit M, Pasmant E, Coppin L, Guien C, Calender A, et al.2019 UMD-MEN1 database: an overview of the 370 MEN1 variants present in 1676 patients from the French population. Journal of Clinical Endocrinology and Metabolism 104 753–764. (https://doi.org/10.1210/jc.2018-01170)
Ruas M & & Peters G 1998 The p16INK4a/CDKN2A tumor suppressor and its relatives. Biochimica et Biophysica Acta 1378 F115–F177. (https://doi.org/10.1016/s0304-419x(9800017-1)
Sanchez-Luque FJ, Kempen M-JHC, Gerdes P, Vargas-Landin DB, Richardson SR, Troskie RL, Jesuadian JS, Cheetham SW, Carreira PE, Salvador-Palomeque C, et al.2019 LINE-1 evasion of epigenetic repression in humans. Molecular Cell 75 590–604.e12. (https://doi.org/10.1016/j.molcel.2019.05.024)
Scarpa A, Chang DK, Nones K, Corbo V, Patch AM, Bailey P, Lawlor RT, Johns AL, Miller DK, Mafficini A, et al.2017. Whole-genome landscape of pancreatic neuroendocrine tumours. Nature 543 6 5–7 1. (https://doi.org/10.1038/nature21063)
Schmitt AM, Pavel M, Rudolph T, Dawson H, Blank A, Komminoth P, Vassella E & & Perren A 2014 Prognostic and predictive roles of MGMT protein expression and promoter methylation in sporadic pancreatic neuroendocrine neoplasms. Neuroendocrinology 100 35–44. (https://doi.org/10.1159/000365514)
Sengelaub CA, Navrazhina K, Ross JB, Halberg N & & Tavazoie SF 2016 PTPRN2 and PLCbeta1 promote metastatic breast cancer cell migration through PI(4,5)P2-dependent actin remodeling. EMBO Journal 35 62–76. (https://doi.org/10.15252/embj.201591973)
Serrano J, Goebel SU, Peghini PL, Lubensky IA, Gibril F & & Jensen RT 2000 Alterations in the p16INK4a/CDKN2A tumor suppressor gene in gastrinomas. Journal of Clinical Endocrinology and Metabolism 85 4146–4156. (https://doi.org/10.1210/jcem.85.11.6970)
Seto E, Shi Y & & Shenk T 1991 YY1 is an initiator sequence-binding protein that directs and activates transcription in vitro. Nature 354 241–245. (https://doi.org/10.1038/354241a0)
Sharma AE, Olivas A, Parilla M, Yassan L, Wang H, Zhang SS, Weber C, Keutgen XM, Hart J, Krausz T, et al.2022 Epigenetic dysregulation of 5-hydroxymethylcytosine in Well-Differentiated Pancreatic Neuroendocrine Tumors. Applied Immunohistochemistry and Molecular Morphology 30 e11–e15. (https://doi.org/10.1097/PAI.0000000000000982)
Shen J, Lefave C, Sirosh I, Siegel AB, Tycko B & & Santella RM 2015 Integrative epigenomic and genomic filtering for methylation markers in hepatocellular carcinomas. BMC Medical Genomics 8 28. (https://doi.org/10.1186/s12920-015-0105-1)
Simon T, Riemer P, Jarosch A, Detjen K, Di Domenico A, Bormann F, Menne A, Khouja S, Monjé N, Childs LH, et al.2022 DNA methylation reveals distinct cells of origin for pancreatic neuroendocrine carcinomas and pancreatic neuroendocrine tumors. Genome Medicine 14 24. (https://doi.org/10.1186/s13073-022-01018-w)
Skvortsova K, Masle-Farquhar E, Luu PL, Song JZ, Qu W, Zotenko E, Gould CM, Du Q, Peters TJ, Colino-Sanguino Y, et al.2019 DNA hypermethylation encroachment at CpG island borders in cancer is predisposed by H3K4 monomethylation patterns. Cancer Cell 35 297–314.e8. (https://doi.org/10.1016/j.ccell.2019.01.004)
Sonbol MB, Mazza GL, Mi L, Oliver T, Starr J, Gudmundsdottir H, Cleary SP, Hobday T & & Halfdanarson TR 2022 Survival and incidence patterns of pancreatic neuroendocrine tumors over the last 2 decades: a SEER database analysis. Oncologist 27 573–578. (https://doi.org/10.1093/oncolo/oyac049)
Stefanoli M, La Rosa S, Sahnane N, Romualdi C, Pastorino R, Marando A, Capella C, Sessa F & & Furlan D 2014 Prognostic relevance of aberrant DNA methylation in g1 and g2 pancreatic neuroendocrine tumors. Neuroendocrinology 100 26–34. (https://doi.org/10.1159/000365449)
Stricker I, Tzivras D, Nambiar S, Wulf J, Liffers ST, Vogt M, Verdoodt B, Tannapfel A & & Mirmohammadsadegh A 2012 Site- and grade-specific diversity of LINE1 methylation pattern in gastroenteropancreatic neuroendocrine tumours. Anticancer Research 32 3699–3706.
Taelman VF, Radojewski P, Marincek N, Ben-Shlomo A, Grotzky A, Olariu CI, Perren A, Stettler C, Krause T, Meier LP, et al.2016 Upregulation of key molecules for targeted imaging and therapy. Journal of Nuclear Medicine 57 1805–1810. (https://doi.org/10.2967/jnumed.115.165092)
Tahiliani M, Koh KP, Shen Y, Pastor WA, Bandukwala H, Brudno Y, Agarwal S, Iyer LM, Liu DR, Aravind L, et al.2009 Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324 930–935. (https://doi.org/10.1126/science.1170116)
Takai D, Yagi Y, Habib N, Sugimura T & & Ushijima T 2000 Hypomethylation of LINE1 retrotransposon in human hepatocellular carcinomas, but not in surrounding liver cirrhosis. Japanese Journal of Clinical Oncology 30 306–309. (https://doi.org/10.1093/jjco/hyd079)
Thakker RV 2014 Multiple endocrine neoplasia type 1 (MEN1) and type 4 (MEN4). Molecular and Cellular Endocrinology 386 2–15. (https://doi.org/10.1016/j.mce.2013.08.002)
Thakker RV, Newey PJ, Walls GV, Bilezikian J, Dralle H, Ebeling PR, Melmed S, Sakurai A, Tonelli F, Brandi ML, et al.2012 Clinical practice guidelines for multiple endocrine neoplasia type 1 (MEN1). Journal of Clinical Endocrinology and Metabolism 97 2990–3011. (https://doi.org/10.1210/jc.2012-1230)
Tirosh A, Mukherjee S, Lack J, Gara SK, Wang S, Quezado MM, Keutgen XM, Wu X, Cam M, Kumar S, et al.2019 Distinct genome-wide methylation patterns in sporadic and hereditary nonfunctioning pancreatic neuroendocrine tumors. Cancer 125 1247–1257. (https://doi.org/10.1002/cncr.31930)
Tobi EW, Lumey LH, Talens RP, Kremer D, Putter H, Stein AD, Slagboom PE & & Heijmans BT 2009 DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Human Molecular Genetics 18 4046–4053. (https://doi.org/10.1093/hmg/ddp353)
Townsend CM, Ishizuka J & & Thompson JC 1993 Studies of growth regulation in a neuroendocrine cell line. Acta Oncologica 32 125–130. (https://doi.org/10.3109/02841869309083900)
Toyota M & & Issa JP 1999 CpG island methylator phenotypes in aging and cancer. Seminars in Cancer Biology 9 349–357. (https://doi.org/10.1006/scbi.1999.0135)
Ushiku H, Yamashita K, Kawamata H, Waraya M, Katoh H, Yokoi K, Tanaka T, Ishii S, Nishizawa N, Kikuchi M, et al.2016 Homeobox-Only Protein Expression Is a Critical Prognostic Indicator of Pancreatic Neuroendocrine Tumor and Is Regulated by Promoter DNA Hypermethylation. Pancreas 45 1255–1252. (https://doi.org/10.1097/MPA.0000000000000646)
Walter T, Van Brakel B, Vercherat C, Hervieu V, Forestier J, Chayvialle JA, Molin Y, Lombard-Bohas C, Joly MO & & Scoazec JY 2015 O6-methylguanine-DNA methyltransferase status in neuroendocrine tumours: prognostic relevance and association with response to alkylating agents. British Journal of Cancer 112 523–531. (https://doi.org/10.1038/bjc.2014.660)
Wild A, Ramaswamy A, Langer P, Celik I, Fendrich V, Chaloupka B, Simon B & & Bartsch DK 2003 Frequent methylation-associated silencing of the tissue inhibitor of metalloproteinase-3 gene in pancreatic endocrine tumors. Journal of Clinical Endocrinology and Metabolism 88 1367–1373. (https://doi.org/10.1210/jc.2002-021027)
Yachida S, Totoki Y, Noë M, Nakatani Y, Horie M, Kawasaki K, Nakamura H, Saito-Adachi M, Suzuki M, Takai E, et al.2022 Comprehensive genomic profiling of neuroendocrine carcinomas of the gastrointestinal system. Cancer Discovery 12 692–711. (https://doi.org/10.1158/2159-8290.CD-21-0669)
Yang YJ, Song TY, Park J, Lee J, Lim J, Jang H, Kim YN, Yang JH, Song Y, Choi A, et al.2013 Menin mediates epigenetic regulation via histone H3 lysine 9 methylation. Cell Death and Disease 4 e583. (https://doi.org/10.1038/cddis.2013.98)
Yates J & & Boeva V 2022 Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis. Briefings in Bioinformatics 23 bbab610. (https://doi.org/10.1093/bib/bbab610)
Yuan Z, Sánchez Claros C, Suzuki M, Maggi EC, Kaner JD, Kinstlinger N, Gorecka J, Quinn TJ, Geha R, Corn A, et al.2016 Loss of MEN1 activates DNMT1 implicating DNA hypermethylation as a driver of MEN1 tumorigenesis. Oncotarget 7 12633–12650. (https://doi.org/10.18632/oncotarget.7279)
Zhang C, Jin J, Xie J, Ye L, Su T, Jiang L, Zhou W, Jiang Y, Wu L, Wang T, et al.2020 The clinical features and molecular mechanisms of ACTH-secreting pancreatic neuroendocrine tumors. Journal of Clinical Endocrinology and Metabolism 105 3449–3458. (https://doi.org/10.1210/clinem/dgaa507)
