ABSTRACT
Recent data suggest that Pancreatic Neuroendocrine Tumours (PanNETs) originate from α- or β-cells of the islets of Langerhans. The majority of PanNETs are nonfunctional and do not express cell-type specific hormones. We examined whether tumour DNA methylation (DNAme) profiling combined with genomic data could identify cell of origin and reveal pathways involved in PanNET progression. We analysed genome-wide DNAme data of 125 PanNETs and sorted α- and β-cells. To confirm cell identity, we investigated ARX and PDX1 expression. Based on epigenetic similarities, PanNETs clustered in α-like, β-like and intermediate tumours. The epigenetic similarity to α-cells progressively decreased in the intermediate tumours, which presented unclear differentiation. Specific transcription factor methylation and expression varied in the respective α/β-tumour groups. Depending on DNAme similarity to α/β-cells, PanNETs have different mutational spectra, stage of the disease and prognosis, indicating potential means of PanNET progression.
Introduction
Pancreatic neuroendocrine tumours (PanNETs) are tumours of the islets of Langerhans. The cell of origin is unclear, and mechanisms associated with progression are largely unknown. Surgery is currently the only curative option; however, 5-year disease free survival is approximately 50% in patients following an R0 resection1. To date, there is no validated risk prediction tool to accurately guide follow up and select patients at high risk of recurrence who might benefit from adjuvant therapy2. PanNETs are clinically and genetically heterogeneous; ~40% of patients present with mutations in either DAXX or ATRX and MEN1, which encode for proteins involved in epigenetic regulation3. PanNET with mutations in DAXX or ATRX have a shorter disease free survival compared to wild type tumours4.
The islets of Langerhans include five different cell types producing specific hormones: glucagon is produced by α-cells, insulin by β-cells, somatostatin by δ-cells, ghrelin by ε-cells and pancreatic polypeptide by PP-cells. Only a minority of PanNETs are functional, leading to clinical syndromes due to inadequate hormone secretion. The majority of functional PanNETs are insulinomas. Whether functional tumours and non-functional tumours originate from the same cell type remains uncertain.
Recent studies of gene expression and master regulator analysis alongside investigation of superenhancer signatures have suggested both α- and β-cells as two possible cells of origin for nonfunctioning (NF)-PanNETs5–8. On the other hand, Sadanandam et al. reported that a group of aggressive PanNET, namely, “metastasis-like primary”, have a phenotype characterised by “stemness” transcripts compared to well differentiated tumours, also suggesting a common progenitor cell origin5. Similarly, based on the identification of master regulator proteins, de-differentiation and acquisition of stem cell characteristics seem to be one of the pathways associated with tumour progression6.
The cell of origin in cancer refers to the normal cell that acquires the initial cancer-promoting genetic hit(s). During development, cell lineage fate is determined by cell-type specific transcription factor (TF) expression, which in turn is dependent on the type of epigenetic markers that are located at the relative regulatory regions (e.g. super-enhancer activation)9. The five endocrine cell types derive from a common endocrine precursor, which has segregated from a ducto-endocrine bipotent cell population10. The TFs, Pax4 (Paired Box 4) and Arx (Aristaless Related Homeobox), are required for β- and α-cell fates, respectively. Lineage decision is determined via cross-inhibitory interactions11. Pdx1 (Pancreatic And Duodenal Homeobox 1) expression becomes restricted to cells at the stage of initiating insulin expression and, in the pancreatic islets of Langerhans, remains up-regulated exclusively in β-cells12. Integrative analysis of human epigenomes including histone modification patterns, DNA accessibility, DNA methylation and RNA expression has revealed that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks13. In the context of tumour biology, epigenetic states of cell lineages shape the vulnerability for specific genetic alterations and thereby reveal a distinct class of lineage-associated cancer genes14–6. Therefore, determining the cell of origin is crucial to understand tumour specific carcinogenesis and progression17. Cancer DNA methylation profiles have been utilised to determine the cell of origin of several tumour types18,19. In this study, we set out to determine the putative cell of origin of PanNETs through DNA methylation analysis. We also identified genetic driver mutations specific to different cells of origin which are related to clinical outcome. Based on our findings we propose a new model of PanNET origin and progression.
Results
DNA methylation signature of PanNETs showed similarities to α- and β-cells
We analysed the methylomes of 125 PanNETs and of isolated normal α- and β-cells20 to determine which of these is the cell of origin (cohort details are summarised in table 1 and reported in table S1). A flow chart of the analysis performed is provided in figure S1. The comparison between sorted normal α- (n=2) and β-cells (n=2) resulted in 2703 differentially methylated CpG sites (adj. p-value<0.001 and more than 20% difference between the mean β-values of each group – |Δβ|>0.2, table S2), 2131 of them were retained, after filtering and normalization, in the tumour DNA methylation matrix. Phyloepigenetic analysis of the 2131 identified CpG sites using either normal β- or normal a-cells as roots, indicated clear hierarchical relationships between normal cells and tumour specimens (Fig. 1A and S2A). We defined PanNETs that grouped together with normal α- or β-cells as α-like or β-like PanNETs, respectively (Fig. 1A and table S1). We compared these phyloepigenetic groups to cell type similarity obtained from cell-type deconvolution analysis. We assigned each PanNET sample a degree of methylome similarity to each of the following cell types: α, β, acinar, ductal20, fibroblastic pancreatic cells21 and hematopoietic cells22 (Fig. S2B). Notably, phyloepigenies that were close to normal a/β-cells showed similarity >65% and an average of 72% and 73% of similarity to a and β-cells, respectively (SD for α-like ±15% and for β-like ±9%, table S1). T-distributed stochastic neighbour embedding (t- SNE) analysis, using the identified 2131 CpGs and including normal cells and PanNET samples, showed a consistent segregation, according to the groups defined via similarity scores and phyloepigeny analysis (Fig. S2C). These results showed at least two groups of tumours with clear and high similarity to either α- or β-cells (Fig. 1A, S2A and S2C). The majority of tumours, however, revealed an intermediate differentiation profile. Intermediate tumours clustered between α- and β-cells and showed weak similarities to α- (as average 51 ±20 % of similarity) and very weak similarity to β-cells (as average 14 ±16 % of similarity), respectively.
Regulatory sites of α- and β-cell specific transcription factors were differentially methylated
We used previously published data to select 10 α- and 10 β-cell-type associated TFs23–25 (table 2). As methylation of enhancers and regulatory states are important elements of epigenetic guidance of cell states, these regions were selected based on integrated ChIP-seq, DNA methylation and ATAC-seq experiments of normal islets26. This analysis resulted in 49 CpGs for the α-cell TFs and 51 CpGs for the β-cell TFs. Consensus clustering of the 125 PanNETs according to each cell-specific TF signature (k-means = 2, reflecting one cell-type enriched cluster and one non-specific cluster) showed clear and consistent cluster formation. The β-specific cluster separated very clearly and included all the β-like tumours. Interestingly, the α-specific cluster showed a broader inclusion of tumours. While the α-like (phyloepigenies with >65% of similarities to alpha cells) tumours clustered mainly close to each other on one extreme, intermediate PanNETs were included to this group as well but progressively distant from the α-like tumours (Fig. 1B).
Alpha-, intermediate and β-like PanNETs have distinct genetic aberrations
Within the β-like PanNETs 13 of 14 were MEN1/DAXX/ATRX wild type. Alpha-like PanNETs were enriched for MEN1 only mutations (11/19), 8/19 α-like PanNETs were MEN1/DAXX/ATRX wild type and none harboured MEN1/DAXX/ATRX mutations (Fig. 1A and table S1). Sixty-seven percent (62/92) of intermediate PanNETs harboured mutations in MEN1 and/or DAXX/ATRX genes (intermediate-ADM) while the rest were wild type (intermediate-WT) (Fig. 1A and table S1).
Inferred copy number aberrations (CNAs) from HM450 signal intensities stratified the tumours into 3 subtypes (Fig. S3E). Copy number aberration (CNA) group 1 had few copy number events. Group 2 included samples with gains and losses. CNA group 3 showed the largest number of CNAs, predominantly with loss of multiple chromosomes (Fig. S3E). Notably, α- and β-like tumours presented few copy number events while the intermediate PanNETs showed increasingly copy number events (Fig. 1A).
DNA methylation signatures associated with cell of origin correlated with expression of hormones and transcription factors
To confirm the methylation-based cell lineage similarity, we performed immunostaining for ARX, PDX1, insulin and glucagon on a subset of samples for which formalin fixed paraffin-embedded tissue was available (n=39, table S1). Nuclear positivity for ARX and PDX1 was present in the expected distribution in normal islets (Fig. S4). We identified ARX positivity in 28/39 PanNETs, 6 of them also produced glucagon (table S1 and Fig. 1C). As expected, all of the insulin producing PanNETs expressed PDX1, except the single malignant insulinoma, which showed nuclear positivity for ARX but not for PDX1 (table S1 and Fig. 1C). None of the 39 tumours were double positive and only 4 PanNETs were double negative (table S1 and Fig. 1C).
Immunohistochemistry confirmed the same group segregation as was determined by the methylation analysis: 1) α-like tumours expressed ARX, a subset produced glucagon (2/3, 67%), 2), β-like tumours expressed both PDX1 and insulin, with only 1 sample positive for ARX and PDX1 negative (Fig. 1C), 3) 24/29 (83%) intermediate PanNETs were positive for ARX and rarely produced glucagon (4/22, 18%). A minority of intermediate tumours (4/29, 14%) were negative for both PDX1 and ARX and only one intermediate PanNET expressed PDX1 and insulin (Fig. 1C, table S1).
These results indicate that cell type assigned by DNA methylation, corresponded to the cell type assigned by TF expression.
PDX1 and ARX immunopositivity correlates with mutation spectrum of PanNETs in an independent cohort
To validate the correlation of putative cell of origin to mutational status, we performed immunostaining for ARX, PDX1, insulin, glucagon and DAXX/ATRX on an independent cohort of 65 G1/G2 PanNETs (cohort details are summarised in table 3 and reported in table S3). We scored nuclear positivity for ARX in 34 samples (52%), for PDX1 in 21 samples (32%), 3 tumours (5%) showed strong double positivity and 13 (20%) were negative for both TFs (Fig 2A and 2B). Notably, all the DAXX/ATRX negative samples expressed ARX, confirming the α-like tumour susceptibility for these mutations (Fig. 1A and 2C). While only a subset of ARX+ tumours secreted glucagon (n=7, 20% of all the ARX+ tumours, Fig. 2C), almost all the PDX1+ tumours produced insulin (n=18, 95% of all PDX1+ tumours, Fig. 2C).
We observed that advanced stage PanNETs either expressed ARX or none of the TFs and often showed DAXX/ATRX loss (Fig. 2C, S5A and table S3). Tumours positive for PDX1 had the lowest risk of relapse (Fig. S5B and C). However, DAXX/ATRX status did not improve stratification of ARX+ samples for risk of relapse (Fig. S5B).
Epigenetic differentiation status determines clinical outcome of PanNETs
In order to determine clinical utility, we reviewed clinical outcome and prognosis for α-like, β-like and intermediate tumours. Fourteen of 19 α-like PanNETs were G1 and 14/18 (1 without data) were of low stage (T1 or T2, Fig. 1A and table S1). Similarly, 8/14 β-like PanNETs were G1 and 9/11 (3 without data) were T1 or T2 (Fig. 1A and table S1). Only one patient with an α-like tumour had relapse and none of the patients with a β-like tumour (median follow-up time 37.2 months, table S1 and Fig. 3A). Tumours that belonged to the intermediate phyloepigenies, were instead enriched for high stage (63/88 were T3 or T4, 4 without data) and higher grade (65/92 are G2) PanNETs had increased relapse risk (37/70, 22 had not available data – Fig. 1A, 3A and table S1). Disease free survival in intermediate tumours was significantly shorter than in α-like and β-like tumours (p<0.001, Fig. 3A). To assess if epigenetic profile can stratify patients better than TF and DAXX/ATRX expression we performed disease free survival analysis on all samples where DNA methylation and TF expression data were available. We determined time to relapse, using either expression of ARX, PDX1 and DAXX/ATRX or the epigenetic status as discriminant (Fig. 3B and C). Epigenetic profiles could predict more accurately the risk of relapse as shown in figures 3B and C.
Further, to identify a tumour dependent signature, suitable for stratification in clinical practice, we have compared the three cell-type specific clusters, α-like, β-like and intermediate. The comparison revealed 6364 unique differentially methylated sites (tables S4-S6; adj. p-value<0.001 and |Δβ|>0.2). Consensus clustering of the 6364 CpG sites showed the maximal stability for k=4 (Fig. S6A). The existence of α-like and β-like clusters was confirmed (Fig. 3D and tableS1). The intermediate subgroup was divided into 2 further groups, one enriched for DAXX/ATRX/MEN1 mutations, intermediate-ADM, (61/76 had mutations in at least 1 of the 3 genes; Fig. 3D and tableS1) and the other enriched for DAXX/ATRX/MEN1 wild type samples, intermediate-WT (15/16, only 1 DAXX/ATRX mutated;, Fig. 3D and tableS1). PanNETs included in the intermediate-ADM and intermediate-WT showed comparable risk to relapse (Fig. S6B). To prove the stability of the signature, we repeated the analysis including 32 new G1/G2 PanNETs, for which DNA methylation data were public available7 (table S7). After filtering and normalization processes, 6359/6364 of the previously identified differentially methylated sites were used in the consensus clustering algorithm. The results confirmed the 4 PanNET subgroups with risks to relapse similar to previous analysis (Fig. S6 C-E).
Discussion
This study identified at least two cells of origin for PanNETs (α-like / β-like) and demonstrated that DNA methylation analysis can discriminate α-like, β-like and intermediate (-ADM and -WT) PanNETs. Additionally, this study identified that relapses and metastases occurred most commonly in the intermediate (-ADM and -WT) PanNET groups.
Phyloepigenetic analysis of PanNETs, according to differentially methylated CpG sites between normal α- and β-cells, showed two clusters around normal α- and β-cells, which we named α-like and β-like. PanNET sample segregation was consistent in the t-SNE analysis. Using a similar approach, in a recent study, methylation-based subclasses of colorectal cancer were explained as clonal amplifications of one specific epigenotype, confirmed by enrichment of different mutations in the peculiar subtypes and by the investigation of methylation signatures in serial tumour xenografts and derived spheroids18. Similarly, in medulloblastoma subtypes with distinct developmental origin, DNA methylation signatures were able to stratify the tumours according to the specific subtype and partially resemble the cell of origin as well as acquired epigenetic changes during tumour progression14,27,28. Even if in the aforementioned studies tumours were not directly compared to the putative normal cells of origin, the epigenetic and mutational landscape of the malignancies reflected the tumour classes and cells of origin defined via in vivo studies. Altogether the data confirm the valuable use of DNA methylation profiles for the identification of tumour cells of origin.
Cell-type deconvolution analysis of methylome data from ductal, acinar, inflammatory and pancreatic stromal cells together with α- and β- cells found that α-like and β-like PanNETs are largely similar (at ~73%) to α- and β- cells, respectively. This figure is comparable to other studies: Houseman and Ince29 demonstrated the application of their algorithm to estimate normal cell proportions in breast cancer heterogeneous tissues. Similarly, cell type deconvolution analysis based on DNA methylation for mantel cell lymphoma (MCL), showed a minimum of similarity between MCL and B cell samples of 40%30. When a similar algorithm (L1-regularized logistic regression) is used to classify cancers of unknown origin (CUPs) based on DNA methylation, a probability >30% was used to ascribe CUPs to a specific tumour type31. The deconvolution of the methylation estimates for PanNETs identified two important aspects of the tumour methylomes: that the results were not influenced by the composition of non-tumoral cells within the PanNET samples (Fig. S2B) and that the composition of early stage α-like and β-like PanNETs is abundant for either α- or β-cells (Fig. 1A).
Analysis of methylation status of specific TFs regulating α and β differentiation might help to better identify the intermediate groups of PanNET. While β-like samples separated very clearly according to β-cell TF methylation sites, α-cell similarity decreased gradually among the remaining samples. Seventy of seventy-six intermediate-ADM PanNETs clustered together with the α-like tumours (table S1). Twelve of sixteen intermediate-WT PanNETs did cluster neither with α-like nor with β-like tumours (table S1). Additionally, intermediate-ADM PanNETs were on average more similar to α- (53±17%) than β-cells (12±13%). Ninety-two percent of the intermediate-ADM tumours were positive for ARX (23/25) and none for PDX1. This might indicate that intermediate-ADM PanNETs are more related to a-cells rather than β-cells. The PanNET methylation sub-groups that we have identified reflect the groups A (tumours that most likely originate form a-cells), B (tumours that most likely originate form β-cells) and C (tumours with intermediate phenotype) recently described by Cejas et al., via the analysis of super-enhancer signatures8. Comparable to our study, the analysis of TF expression via IHC performed by Cejas et al. revealed mutually exclusive expression of either ARX or PDX1 in the majority of the cases. Additionally, while PDX1 expression appears to be specific for benign and low stage tumours, ARX expression is retained at early and at advanced stages and only DAXX/ATRX status provide more information about stage and risk of disease progression8.
We identified a clear correlation between driver mutation status and epigenetic profiles across all PanNETs. Alpha-like PanNETs and intermediate-ADM PanNETs harboured MEN1 mutations, this is supported by the results of Chan et al. and Cejas et al7,8 Fifty-eight percent of the clinically indolent α-like PanNETs were characterized by MEN1 mutations only. MEN1 inactivation, is an early event in PanNET progression32,33, which enhances endocrine cell proliferation34, hence it might be a tumour initiating event for the α-like PanNETs and for the intermediate-ADM. In turn Intermediate-ADM PanNETs might progress upon acquisition of DAXX/ATRX inactivation. In addition, CNA increased from α-like and β-like PanNETs to intermediate-ADM tumours. All these data together suggest a potential progression from α-like to intermediate-ADM PanNET. We cannot exclude, however, that the two intermediate PanNET clusters might originate from putative endocrine precursor cells.
During islet cell development, ARX is already expressed in endocrine precursors11, furthermore different endocrine cell types, as α- and g-cells share ARX expression23,24. Conversely PDX1 expression is restricted to differentiated β-cells, within the endocrine lineages12. Of the intermediate-WT tumours for which IHC data were available (n=4), one was positive for PDX1, one for ARX and 2 were negative for both TFs. Additionally, similarly to the β-like tumours, intermediate-WT PanNETs showed only few copy number events (group 1, table S1). Currently our data only weakly support progression from β- like to intermediate-WT PanNETs, nevertheless this possibility cannot be excluded.
While the vast majority of β-like PanNETs expressed PDX1 and insulin, the two malignant (N1 and/or M1) insulinomas of our cohort, showed one intermediate-ADM (DAXX/ATRX mutated) and one α-like (DAXX/ATRX wild type) methylation signatures (table S1). Only for the intermediate-ADM insulinoma, data on PDX1 and ARX were available; it resulted positive for ARX, and negative for PDX1 expression. Accordingly, the only malignant insulinoma of the Chan et al. cohort was mutated for ATRX and clustered with the intermediate-ADM tumours. In line with this observation, a recent study including 37 sporadic insulinomas (35 primary and 2 liver metastases) showed that all the five insulinomas which metastasized were ARX positive and 4/5 had ALT activation (3 primary and 2 liver metastases)33. These data suggest that malignant insulinomas may arise from a-cells or stem-cells rather than β-cells. Under certain conditions and stimuli α-cells are able to trans-differentiate into β-cells35–39. Additionally, α-cell specific Men1 knockout in mice leads to the development of glucagonomas which evolve into mixed glucagonoma/insulinoma to ultimately become insulinomas, possibly via trans-differentiation of the Men1 -deleted α-cells40,41. Beta-like and/or PDX1 positive tumours are strongly enriched for benign insulinomas in both the first and second cohort. They generally show no mutations in any of the most commonly mutated genes for PanNETs (DAXX, ATRX and MEN1). These data confirmed the genetic difference between non-functioning tumours and insulinomas5,42–44. Of the thirteen wild type PanNETs obtained from Chan et al.7, five were included in the β-like cluster (2 insulinomas and 3 NF-PanNETs), 6 in the intermediate-WT cluster and only 2 in the intermediate-ADM cluster. All the ADM-mutant PanNETs were included in the intermediate-ADM cluster (table S7). These data and the few β-like NF-PanNETs included in the cohort 1, demonstrate that occasionally also NF-PanNETs might originate from β-cells.
Our methylation data supports the possibility of two evolutionary pathways for PanNET development, originating from α- and β-cells (Fig. 3D). Beta-like PanNETs usually manifest as insulinomas. The α-like PanNETs are susceptible to MEN1 mutations in early tumorigenesis. Tumour progression occurs upon DAXX/ATRX mutations, coupled with ALT activation and a characteristic CNA profile4,45–47. Progressive loss of differentiation might further predispose the tumours to enhanced proliferation and higher cell plasticity (Fig. 3D). Alternatively or additionally to this dedifferentiation model, potential endocrine progenitor cells might be the cell of origin of intermediate tumours (Fig. 3D).
We acknowledge that our study is based on static observations taken at one timepoint in each tumour’s development. Sequential sampling or in vivo experiments would be able to determine real time tumour evolution and would be able to address the unanswered questions we propose here.
Clinically α-like, β-like and intermediate tumours have different outcomes. While α-like and β-like tumours are indolent, intermediate tumours (-ADM and -WT) have high risk of relapse. Interestingly, DAXX/ATRX status alone is not sufficient to discriminate between ARX+ with high and low risk of relapse. Indeed, in the intermediate-WT group we observed the presence of ARX+ tumours, with high risk of relapse. Only the DNA methylation profile is able to separate ARX+ PanNET with low risk from high risk of relapse. The intermediate groups of PanNET are characterized by high risk of relapse but are molecularly different (ADM and WT) (Fig. 3D and S6D). Further work is needed to assess whether intermediate-ADM and intermediate-WT aggressive PanNETs have different origins or respond differently to therapies.
DNA methylation analysis clearly bears advantages over ChIPseq assays, it is easily performed on diagnostic routine FFPE specimens. We foresee a potential clinical use of epigenetic profiling for PanNETs similar to CNS tumor classification, able to define clinically relevant groups28,48,49. In addition, DNA methylation remains stable in circulating tumor cells (CTCs) and cell-free DNA (cfDNA) (reviewed in 50), hence might be applied to monitor progression in individual patients in a non-invasive liquid biopsy.
In conclusion DNA methylation analysis could be easily implemented in clinical practice to identify patients with high relapse risk and those which might benefit from adjuvant therapy.
Methods
Patient cohorts
A cohort of primary PanNETs was assembled from two international centres; 19 samples previously analyzed51 from UCL Cancer Institute (London, UK) and 26 from Institute of Pathology (Bern, Switzerland). All cases were classified according to WHO 2017 criteria52. TNM staging is based on the 8th edition UICC/AJCC. Inclusion criteria were histopathologic diagnosis of well-differentiated G1/G2 PanNETs, availability of tissue material and sufficient tumour purity (>70%). Seventeen of 45 tumour samples (38%) were classified as G1, 28/45 (62%) as G2 (table 1). Two samples derived from MEN1-patients. Median follow-up time is 51.5 months. All analyses were performed on Formalin-Fixed Paraffin-Embedded (FFPE) tissue specimens obtained from routine pathological work-up. Additional clinico-pathological characteristics are reported in table S1. The study on this cohort was approved by the local ethics committees (Bern: number 105/2015; London: number 09/H0722/27).
Clinical and molecular data of 80 PanNETs were provided by the International Cancer Genome Consortium (ICGC, https://icgc.org/, projects: PAEN-AU and PAEN-IT). Cohort features are summarised in table 1 and table S1. Genomic and molecular analysis of the samples were performed within Scarpa et al. study45.
As immunohistochemical validation cohort, 65 PanNET samples were assembled at the Institute of Pathology (Bern, Switzerland). All cases were reclassified according to WHO 2017 criteria52. TNM staging is based on the 8th edition UICC/AJCC53. As for the previous cohorts, only well-differentiated G1/G2 PanNETs were included. Cohort features are summarised in table 3 and extensively reported in table S3.
A flow chart of the analysis performed on the 2 cohorts is provided in figure S1.
Immunohistochemistry (IHC) and Telomeric-Fluorescence In Situ Hybridization (Telo-FISH)
All samples were assessed for DAXX and ATRX expression via IHC and ALT activation via Telo-FISH on 2.5 μm sections prepared from a tissue microarray (TMA – Bern cohort and validation cohort) or whole block sections (London cohort) as previously described4.
Similarly, 2.5 μm sections from TMAs or whole blocks were used for ARX (1:1500, R&D Systems, sheep, AF7068), PDX1 (1:100, R&D Systems, mouse, MAB2419), insulin (1:12000, Sigma Aldrich, mouse, I-2018) and glucagon (1:20000, Sigma Aldrich, mouse, G-2654) immunostainings. The immunostaining for all antigens was performed on an automated staining system (Leica Bond RX; Leica Biosystems, Nunningen, Switzerland). Antigen retrieval was performed by heating Tris30 buffer at 95° for 30 minutes. The primary antibody was incubated for 30 minutes at the specified dilutions. Visualization was performed using Bond Polymer Refine Detection kit, using DAB as chromogen (3,3’-Diaminobenzidine).
Samples showing single cell positivity of any of the TFs were finally scored as negative, most of the cases were indeed strong positive for one of the two TFs with only few exceptions (single cell positivity is reported for each sample in tables S1 and S3). Only strong positivity for insulin and glucagon was considered for classification of the tumours as hormone producing PanNETs. Nuclear positivity for ARX and PDX1 was first assessed on normal islets, proving the selectivity for endocrine α- and β-cells (Fig. S4).
DNA methylation analysis
We extracted DNA from FFPE tissues according to manufacturer recommendations (QIAamp DNA minikit, Qiagen). Serial sections were cut and macrodissected using a razor blade upon histological evaluation (5 × 6μm), to make sure to achieve >70% tumour purity. We assessed DNA quality using the Illumina FFPE QC Kit. Ligation of FFPE DNA and bisulphite conversion were performed as described54. Efficiency of bisulphite conversion was confirmed by quantitative PCR as previously shown54. Converted DNA was processed on the HumMeth450 BeadChip (Illumina HM450). We analysed all the DNA methylation data included in this study using the ChAMP pipeline (v2.12.4, minfi method was used for raw data loading)55–57. Filtering was performed as implemented in the ChAMP pipeline58. Batch correction was performed using the ComBat algorithm as part of the ChAMP pipeline59,60.
We identified differentially methylated sites between normal α- and β-cells according to the ChAMP pipeline61,62. To build phyloepigenetic trees, distances between samples were calculated according to pearson correlation and the neighbour-joining tree estimation was used within the ape (v5.3) R package63. The tSNE approach was performed as implemented in the R package tsne using the following parameters: perplexity=50, max_iter=5,000.
We used the RnBeads pipeline64 for calculating cell type contribution according to the Houseman et al. method65. Sorted normal hematopoietic cell type data (granulocytes, CD4+ and CD8+ T cells, CD14+ monocytes, CD19+ B cells, CD56+ natural killer cells, neutrophils and eosinophils) were downloaded from Reinius et al.22 (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, accession number: GSE35069). Sorted acinar, duct, alpha and beta pancreatic cell DNA methylation profiles were downloaded from Jäkel et al.20 (European Genome-Phenome Archive, https://ega-archive.org/, accession number: EGAS00001002533). DNA methylation data for sorted normal pancreatic fibroblast cells were downloaded from Xiao et al.21 (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, accession number: GSE80369). Marker selection was performed by screening 10 000 CpG positions and the final number of selected cell type markers was set to 500 (as for default).
Consensus clustering was performed following the ConsensusClusterPlus pipeline66, samples were clustered according to the hierarchical clustering algorithm, ward.D2 method was used for inner linkage and average method was used for the final linkage.
Based on genomic position, the 450K probes were annotated with the chromatin state, as assigned to normal pancreatic islets (obtained from the integration of ATAC-Seq, DNAme and ChIP-seq data)26. Probes associated with the cell specific transcription factors (TFs) and associated to the epigenetic states “closed weak enhancer”, “lowly-methylated weak enhancer”, “open weak enhancer”, “closed strong enhancer”, “open strong enhancer”, “genic enhancer”, “insulator” and “polycomb repressed states” were included for looking at the specific TF methylation. Enhancer regions were associated to the nearest gene in the genome using the GenomicRanges R package67.
Next Generation Sequencing
We sequenced MEN1, DAXX and ATRX genes by semi-conductive sequencing using two Ion Torrent AmpliSeq NGS custom made panels (Life Technologies), one for ATRX and one for MEN1 and DAXX, covering whole protein coding exons. Protein-coding exons were amplified by multiplex polymerase chain reaction using 2 pools designed by the Ion AmpliSeq Designer and the Ion AmpliSeq Library kit 2.0 according to the manufacturer’s recommendations (Life Technologies). Template preparation was performed using the Ion Chef System. Sequencing was performed using the Ion S5. The Torrent Suite 5.10 platform was used for sequence alignment with the hg19 human genome reference. Variant calling was performed with the variant caller and the IonReporter 5.10 software (Life Technologies). The coverage depth was sufficient (quality criteria for a sample to be analysed was minimum 500 reads). The 80 ICGC samples were sequenced by whole-genome sequencing (WGS) within the Scarpa et al. study45.
Copy-number aberration analysis
Genome-wide CNAs were inferred from HM450 signal intensities using the Conumee R package68. Chromosome bins and segments were measured (bin size: 50,000 to 5,000,000 bp; minimum number of probes per bin: 15). CNA profile zero-threshold was manually adjusted according to FISH results for the UB-UCL cohort (FISH for chr4 and chr17 was performed for detecting gains, FISH for MEN1 and chr11 was used for detecting losses; see supplementary materials and Fig. S3A-D). Zero-threshold of the CNA profiles obtained from the ICGC cohort was manually adjusted according to the results obtained from Scarpa et al.45. Copy number (CN) of each chromosomal arm for each tumour was obtained calculating the median of the relative copy number bins. Arm level copy number data were clustered using Ward’s method, Euclidian distance.
Statistical analysis and graphic representation
Statistical analysis and graphical representations were performed within the R environment (v. 3.5.0)69. Specific packages used in the study and parameters selected are mentioned in the relative method chapters. For disease free survival analysis, the “survival” and “survminer” packages were used70,71.
Data availability
The authors declare that all data supporting the findings of this study are available within the article and its Supplementary data and figures. The datasets generated during the current study (UB-UCL cohort) are available in the ArrayExpress repository (EMBL-EBI, https://www.ebi.ac.uk/arrayexpress/, accession number: E-MTAB-7924). The datasets analysed during the current study (ICGC cohort) are available in the ICGC repository (ICGC, https://icgc.org/, projects: PAEN-AU and PAEN-IT). The datasets of sorted normal hematopoietic cells are available in the GEO repository (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, accession number: GSE35069). The datasets of sorted acinar, duct, alpha and beta pancreatic cells are available in the EGA repository (European Genome-Phenome Archive, https://ega-archive.org/, accession number: EGAS00001002533). The datasets of sorted normal pancreatic fibroblastic cells are available in the GEO repository (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, accession number: GSE80369). Chan et al.7 dataset is available in the GEO repository (Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, accession number: GSE117852).
Author Contributions
A.D.D. was involved in the study design, was responsible for generation, assembly and analysis of the data and writing of the manuscript. C.P.P. contributed data acquisition and supported in DNA methylation analysis. M.R.S. gave technical support. K.B., M.D. and A.P. revised pathological data. C.S. provided bioinformatic support. E.V. performed NGS analysis. C.T. contributed acquisition of clinical and pathological data, provided PanNET methylation data from UCL cases and supported in DNA methylation analysis. C.P.P., K.B., C.S., M.D. and C.T. contributed critical revision of the manuscript for important intellectual content. A.P. and I.M. contributed concept and design of the study, generation, assembly and analysis of the data, writing of the manuscript. C.P.P., C.T, A.P. and I.M. obtained funding.
All the authors have read and approved the final manuscript.
The authors declare no potential conflict of interest.
Financial Disclosures
the authors have nothing to disclose.
Writing Assistance
none
Acknowledgements
We thank Cornelia Schlup and Maja Neuenschwander for technical assistance, Heidi Erika Lisa Tschanz-Lischer, for scientific and bioinformatics support, Tissue Biobank Bern (TBB) for support, Ruth Pidsley and Magali Humbert for helpful discussions.
Footnotes
Grant support: Marie Heim-Vögtlin SNF (PMPDP3_164484) and Tumour Forschung Bern to Ilaria Marinoni. Swiss Cancer League (KLS-4227-08-2017) to Aurel Perren. Cancer Research UK and Experimental Cancer Medicine Centre to Christina Thirlwell and Christodoulos Pipinikas.