OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low- and high-grade lung neuroendocrine neoplasms.The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids , 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary carcinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of  The worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Here, Alcala and colleagues present a multi-omics analysis of these tumours, revealing distinct molecular and prognostic subgroups. Pulmonary carcinoids are rare malignant lesions, annual incidence of which has been increasing worldwide, especially at the advanced stages3. Pulmonary carcinoids account for 1–2% of all invasive lung malignancies: typical carcinoids exhibit good prognosis, although 10-23% metastasise to regional lymph nodes, resulting in a 5-year overall survival rate of 82–100%. The prognosis is worse for atypical carcinoids, with 40–50% presenting metastasis, reducing the 5-year overall survival rate to 50%.According to the WHO classification from 20153, LCNEC and SCLC require upfront aggressive, multimodal treatment for most of the patients. Owing to these differences in clinical management and prognosis, the accurate diagnosis of these diseases is critical. However, there is still no consensus on the optimal approach for their differential diagnosis;2 the current criteria, based on morphological features and immunohistochemistry, are imperfect and inter-observer variations are common, especially when separating typical from atypical carcinoids4, as well as atypical carcinoids from LCNEC in small biopsies5. Ki67 protein immune-reactivity has been suggested as a good marker of prognosis in LNENs as a whole, and for the differential diagnosis between carcinoids and SCLC7, whereas this marker does not faithfully follow the defining histological criteria of typical and atypical carcinoids4. The difficulties in finding good markers to separate these diseases might be due to the limited amount of comprehensive genomic studies available for SCLC, LCNEC, and typical carcinoids, and the complete lack of such studies for atypical carcinoids8. In addition, such studies would also be needed to validate the recent proposed molecular link between pulmonary carcinoids and LCNEC10.Contrary to pulmonary carcinoids, most of which are eligible for upfront surgery at the time of diagnosisIn this study, we provide a comprehensive overview of the molecular traits of LNENs—with a particular focus on the understudied atypical carcinoids—in order to identify the mechanisms underlying the clinical differences between typical and atypical carcinoids, to understand the suggested molecular link between pulmonary carcinoids and LCNEC, and to find new candidates for the diagnosis and treatment of these diseases.11, 75 LCNEC12, and 66 SCLC14. Taken together, we have performed multi-omics integrative analyses on 116 pulmonary carcinoids , 75 LCNEC, and 66 SCLC  for 63 pulmonary carcinoids  and 20 LCNEC. In order to perform comparative analyses, we have reanalysed published data for 74 pulmonary carcinoids15 ; cluster Carcinoid B was enriched for atypical carcinoids  and male patients ; and cluster LCNEC included 92% of the histopathological LCNEC . Note that clustering based on LF1 to LF5, weighted by their proportion of variance explained, leads to the exact same clusters  using the Multi-Omics Factor Analysis implementation of the group factor analysis statistical framework (Software MOFA)To assess whether the current histopathological classification could be improved by the combination of molecular and morphological characteristics, we undertook a machine-learning (ML) analysis. To do so, we combined the predictions from two independent random forest classifications, based on only-expression or only-methylation data. Using two independent models allowed the inclusion of samples for which only one of these data sets was available, thus maximising the power of subsequent analyses Fig. . Similarp-value = 0.650); and the bad-prognosis group  and compared their overall survival using Cox’s proportional hazard model  belonged to a patient with professional exposure to asbestos (which is known to cause mesothelioma)16 17. This sample showed the highest mutational load , and included two previously published samples (S01513 and S01522)While generally similar to LCNEC, and albeit based on small numbers, the supra-carcinoids appeared to have nonetheless some distinct genomic features based on genome-wide expression and methylation profiles Fig. . Supra-cMEN1, ARID1A, and EIF1AX as significantly mutated genes11. We also found that covalent histone modifiers and subunits of the SWI/SNF complex were mutated in 40% and 22.2% of the cases, respectively. Genomic alterations in these genes and pathways were also seen in the new samples included in this study , as well as MOFA LNEN LF2.In a previous study, mainly including typical carcinoids, we detected ids Fig. . In addipts Fig. . MEN1 wa 20 Fig. . The fulThe third latent factor from the MOFA LNEN accounted for 8% and 6% of the variance in expression and methylation, respectively, but unlike LF1 and LF2, LF3 was not associated with patient survival 20  and negatively correlated with MOFA LNET LF2 (separating clusters Carcinoid A1 and A2).We applied GSEA to identify the pathways associated with the different latent factors. We found significant associations with the immune system and the retinoid and xenobiotic metabolism pathways , the main genes driving the correlation with MOFA latent factors were the phase II enzymes involved in glucuronosyl-transferase activity  and its activator the achaete-scute family bHLH transcription factor 1 (ASCL1) , and ROBO1   gene .We explored the molecular characteristics of each cluster from the MOFA LNET based on their core differentially expressed coding genes , corresponding promoter methylation profiles Fig. , and theL1) Fig. , similarL1) Fig. ; howeverion Fig. , and had 1) Fig. . This clene Fig. . ConcordANGPTL3, 90% with FPKM > 1), and the erb-b2 receptor tyrosine kinase 4  , and NK2 homeobox 1  . UGT genes, ANGPTL3, and ERBB4 were also core-DEGs of cluster B samples when compared to LNEN clusters Carcinoid A and LCNEC  Fig. . This cl 1) Fig. . Interes 1) Fig.  was the FDR Fig. . We alsohem Fig. . Furtherhem Fig. .MKI67 expression levels in the different molecular groups and found relatively low levels in the clusters Carcinoids A1, A2, and B (78% with FPKM < 1) and high levels in the supra-carcinoids (FPKM > 1 in the three samples). As expected, LCNECs and SCLCs carried high levels of this gene . Although the levels of MKI67 for each of the clusters were different, further analyses showed that MKI67 expression levels alone were not able to accurately separate good- from poor-prognosis pulmonary carcinoids  techniques and multi-omics factor analyses (MOFA). ML analyses showed that the molecular profiles could distinguish survival outcomes within patients with atypical carcinoid morphological features, splitting them into patients with good typical-carcinoid-like survival and patients with a clinical outcome similar to LCNEC. Overall, out of the 35 histopathologically atypical carcinoids, ML reclassified 12 into the typical category.25. Samples in cluster A1 showed overexpression of ASCL1 and DLL3. The transcription factor ASCL1 is a master regulator that induces neuronal and neuroendocrine differentiation. It regulates the expression of DLL3, which encodes an inhibitor of the Notch pathway26. Overexpression of ASCL1 and DLL3 is a characteristic of the SCLC of the classic subtype26 and of type-I LCNEC12. We validated the expression of DLL3 in an independent series of 20 pulmonary carcinoids assessed by immunohistochemistry . The fact that we found a correlation between the protein levels of DLL3 and CD1A  provides orthogonal evidence to support the existence of this molecular group. Phase I trials have provided evidence for clinical activity of the anti-DLL3 humanised monoclonal antibody in high–DLL3-expressing SCLCs and LCNECs27, and additional clinical trials are ongoing in other cancer types.Unsupervised MOFA and subsequent gene-set enrichment analyses unveiled the immune system and the retinoid and xenobiotic metabolism as key deregulated processes in pulmonary carcinoids, and identified three molecular groups—clusters—with clinical implications Fig. . The firEIF1AX, and showed downregulation of the SLIT1 and ROBO1 genes. SLIT and ROBO proteins are known to be axon-guidance molecules involved in the development of the nervous system28, but the SLIT/ROBO signalling has also been associated with cancer development, progression, and metastasis. Pulmonary neuroendocrine cells (PNEC) represent 1% of the total lung epithelial cell population29, they reside isolated (Kultchinsky cells) or in clusters named neuroepithelial bodies (NEBs), and are believed to be the cell of origin of most lung neuroendocrine neoplasms30. In the normal lung, it has been shown that ROBO1/2 are expressed, exclusively, in the PNECs, and that the SLIT/ROBO signalling is required for PNEC assembly and maintenance in NEBs31. In cancer, this pathway mainly suppresses tumour progression by regulating invasion, migration, and apoptosis, and therefore, is often downregulated in many cancer types28. More specifically, the SLIT1/ROBO1 interaction can inhibit cell invasion by inhibiting the SDF1/CXCR4 axis, and can attenuate cell cycle progression by destruction of β-catenin and CDC4228. Potential clinical avenues to this finding exist, especially the ongoing development of CXCR4 inhibitors.The second group (cluster A2) harboured recurrent somatic mutations in 32. Cluster B was also characterised by recurrent somatic mutations in MEN1, the most frequently altered gene in pulmonary carcinoids and pancreatic NETs33, which is in line with the common embryologic origin of pancreas and lung. MEN1 was inactivated by genomic rearrangement due to a chromothripsis event affecting chromosomes 11 and 20 in one of our samples. This observation, together with two additional reported cases involving chromosomes 2, 12, and 1311, and chromosomes 2, 11, and 2034, respectively, suggest that chromothripsis is a rare but recurrent event in pulmonary carcinoids. Interestingly, MEN1 mutations did not have a clear prognostic value in our series. Regarding the above-mentioned deregulation of the retinoid and xenobiotic metabolism in pulmonary carcinoids, samples in cluster B presented high levels of UGT and CYP genes. In line with previous studies35,36, these samples also harboured low levels of OTP, which gene expression levels were correlated with survival in the ML predictions. High levels of ANGPTL3 and ERBB4 were also detected in this group of samples, representing candidate therapeutic opportunities. ANGPTL3 is involved in new blood vessel growth and stimulation of the MAPK pathway37. This protein has been found aberrantly expressed in several types of human cancers37. Similarly, overexpression of the epidermal growth factor receptor ERBB4, which induces a variety of cellular responses, including mitogenesis and differentiation, has also been associated with several cancer types39.The third molecular group (cluster B) was enriched in monocytes and depleted of dendritic cells, and had the worst median survival. Even in the presence of T cell infiltration, this immune contexture suggests an inactive immune response, dominated by monocytes and macrophages with potent immunosuppressive functions, and almost devoid of the most potent antigen-presenting cells, dendritic cells, suggesting dendritic cell-based immunotherapy as a therapeutic option for this group of samples3. While molecular studies have sustained this belief for pulmonary carcinoids vs. SCLC14, the identification of a carcinoid-like group of LCNECs12, the recent observation of LCNEC arising within a background of pre-existing atypical carcinoid40, and a recent proof-of-concept study supporting the progression from pulmonary carcinoids to LCNEC and SCLC9, suggest that the separation between pulmonary carcinoids and LCNEC might be more subtle than initially thought, at least for a subset of patients. Our study supports the suggested molecular link between pulmonary carcinoids and LCNEC, as we have identified a subgroup of atypical carcinoids, named supra-carcinoids, with a clear carcinoid morphological pattern but with molecular characteristics similar to LCNEC. In our series, the proportion of supra-carcinoids was in the order of 5.5% (six out of 110 pulmonary carcinoids with available expression/methylation data); however, considering the intermediate phenotypes observed in the MOFA LNEN, the exact proportion would need to be confirmed in larger series. We found high estimated levels of neutrophil infiltration in the supra-carcinoids. For both supra-carcinoids and LCNEC (but not SCLC), the pathways related to neutrophil chemotaxis and degranulation, were also altered. Neutrophil infiltration may act as immunosuppressive cells, for example through PD-L1 expression41. Indeed, the supra-carcinoids also presented levels of immune checkpoint receptors and ligands (including PDL1 and CTLA4) similar—or higher—than those of LCNEC and SCLC, as well as upregulation of other immunosuppressive genes such as HLA-G, and interferon gamma that is speculated to promote cancer immune-evasion in immunosuppressive environments43. If confirmed, this would point to a therapeutic opportunity for these tumours since strategies aiming at decreasing migration of neutrophils to tumoral areas, or decreasing the amount of neutrophils have shown efficacy in preclinical models44. Similarly, immune checkpoint inhibitors, currently being tested in clinical trials, might also be a therapeutic option for these patients.For many years, it has been widely accepted that the lung well-differentiated NETs  have unique clinico-histopathological traits with no apparent causative relationship or common genetic, epidemiologic, or clinical traits with the lung poorly differentiated SCLC and LCNECPD-L1, VISTA, and LAG3, could also be used to drive the therapeutic decision for patients harbouring a tumour belonging to this subset of very aggressive carcinoids. Nevertheless, due to the very low number of supra-carcinoids identified so far (n = 6), follow-up studies are warranted to comprehensively characterise these tumours from pathological and molecular standpoints, to evaluate the immune cell distribution, and to establish if the diagnosis of these supra-carcinoids can be undertaken in small biopsies. Finally, the current classification only recognises the existence of grade-1  and grade-2  well-differentiated lung NETs, while the grade-3 would only be associated with the poorly differentiated SCLC and LCNEC; however, in the pancreas, stomach and colon, the group of well-differentiated grade-3 NETs are well known and broadly recognised45. Whether these supra-carcinoids constitute a separate entity that may be the equivalent in the lung of the gastroenteropancreatic, well-differentiated, grade-3 NETs will require further research.Overall, although preliminary, our data suggest that supra-carcinoids could be diagnosed based on a combination of morphological features  and the high expression of a panel of immune checkpoint (IC) genes ; the levels of IC genes, such as In summary, this study provides comprehensive insights into the molecular characteristics of pulmonary carcinoids, especially of the understudied atypical carcinoids. We have identified three well-characterised molecular groups of pulmonary carcinoids with different prognoses and clinical implications. Finally, the identification of supra-carcinoids further supports the already suggested molecular link between pulmonary carcinoids and LCNEC that warrants further investigation.35, for which the pathological review had already been done.All new specimens were collected from surgically resected tumours, applying local regulations and rules at the collecting site, and including patient consent for molecular analyses as well as collection of de-identified data, with approval of the IARC Ethics Committee. These samples underwent an independent pathological review. For the typical carcinoids and LCNEC, on which methylation analyses were performed, the DNA came from the samples included in already published studies11, George et al.12, and George et al.14. In order to improve the power of the statistical analyses, we regrouped some levels of variables that had few samples. Age was discretized into three categories , Union for International Cancer Control stages were regrouped into four categories , and smoking status was regrouped into two categories . In addition, one patient (S02236) that was originally classified as male was switched to female based on its concordant whole-exome, transcriptome, and methylome data; and one patient (LNEN028) for whom no sex information was available was classified as male based on its methylation data , sex , smoking status , Union for International Cancer Control/American Joint Committee on Cancer stage, professional exposure, and survival . These data were merged with that from Fernandez-Cuesta et al.ent LNEN0 for whomp-values for multiple testing. Using samples from all histopathological types , we found that the sample provider was significantly associated with the histopathological type , the main variable of interest , and important biological covariables —using Fisher’s exact test, adjusting the 35. For the new ones, an H&E (hematoxylin and eosin) stain from a representative FFPE block was collected for all tumours for pathological review. All tumours were classified according to the 2015 WHO classification by three independent pathologists . An H&E stain was also performed in order to assess the quality of the frozen material used for molecular analyses and to confirm that all frozen samples contained at least 70% of tumour cells.Some of the samples included in this manuscript had already undergone a Central Pathological Review in the context of other published studies, so we used the classifications from the supplementary tables of the corresponding manuscriptsFFPE tissue sections (3 µm thick) from 20 atypical and typical carcinoids were deparaffinized and stained with the Ventana DLL3 (SP347) assay, UltraView Universal DAB Detection Kit  on Ventana ULTRA autostainer , and with the CD1 rabbit monoclonal antibody (cl EP3622) (Ventana). The positivity of DLL3 was defined by the percentage of tumour cells exhibiting a cytoplasmic staining, whatever the intensity. The positivity of CD1A was defined by the percentage of the total surface of the tumour exhibiting a membrane staining with 1 corresponding to less than 1%, 2 to a percentage between 1 and 5%, and 3 to greater than 5%. Results are presented in Supplementary Data 46 using the p.adjust R function (stats package version 3.4.4). All tests were two-sided. Also, a summary of the statistics associated with survival analyses is provided in Supplementary Data All tests involving multiple comparisons were adjusted using the Benjamini–Hochberg procedure controlling the false discovery rate14 had missing survival censor information and were thus excluded from the analysis .We performed survival analysis using Cox’s proportional hazard model; we assessed the significance of the hazard ratio between the reference and the other levels using Wald tests, and assessed the global significance of the model using the logrank test statistic . Kaplan–Meier and forest plots were drawn using R package survminer (v. 0.4.2). Note that three LCNEC samples from George et al.Samples included were extracted using the Gentra Puregene tissue kit 4g , following the manufacturer's instructions. All DNA samples were quantified by the fluorometric method , and assessed for purity by NanoDrop  260/280 and 260/230 ratio measurements. DNA integrity of Fresh Frozen samples was checked by electrophoresis in a 1.3% agarose gel.Samples included were extracted using the Allprep DNA/RNA extraction kit , following manufacturer's instructions. All RNA samples were treated with DNAse I for 15 min at 30 °C. RNA integrity of frozen samples was checked with Agilent 2100 Electrophoresis Bioanalyser system  using RNA 6000 Nano Kit (Agilent Biotechnologies).Whole-genome sequencing was performed on three fresh frozen pulmonary carcinoids and matched-blood samples by the Centre National de Recherche en Génomique Humaine . After a complete quality control, genomic DNA (1 µg) has been used to prepare a library for whole-genome sequencing, using the Illumina TruSeq DNA PCR-Free Library Preparation Kit , according to the manufacturer's instructions. After normalisation and quality control, qualified libraries have been sequenced on a HiSeqX5 platform from Illumina , as paired-end 150 bp reads. One lane of HiSeqX5 flow cell has been produced for each sample, in order to reach an average sequencing depth of 30x for each sample. Sequence quality parameters have been assessed throughout the sequencing run and standard bioinformatics analysis of sequencing data was based on the Illumina pipeline to generate fatsq files for each sample.Whole-exome sequencing was performed on 16 fresh frozen atypical carcinoids in the Cologne Centre for Genomics. Exomes were prepared by fragmenting 1 μg of DNA using sonication technology  followed by end repair and adapter ligation including incorporation of Illumina TruSeq index barcodes on a Biomek FX laboratory automation workstation from Beckman Coulter . After size selection and quantification, pools of five libraries each were subjected to enrichment using the SeqCap EZ v2 Library kit from NimbleGen (44Mb). After validation , the pools were quantified using the KAPA Library Quantification kit  and the 7900HT Sequence Detection System , and subsequently sequenced on an Illumina HiSeq 2000 sequencing instrument using a paired-end 2 × 100 bp protocol and an allocation of one pool with 5 exomes/lane. The expected average coverage was approximately 120x after removal of duplicates (11 GB).TM Proton Sequencer (Life Technologies Corp) aiming for deep coverage (> 250x), using the Ion PITM Hi-QTTM OT2 200 Kit and the Ion PITM Hi-QTM Sequencing 200 Kit with the Ion PITM Chip Kit v3 following the manufacturer’s protocols.Targeted sequencing was performed on the same 16 fresh frozen atypical carcinoids and 13 matched-normal tissue for the samples with enough DNA. Three sets of primers covering 1331 amplicons of 150–200 bp were designed with the QIAGEN GeneRead DNAseq custom V2 Builder tool on GRCh37 (gencode version 19). Target enrichment was performed using the GeneRead DNAseq Panel PCR Kit V2 (QIAGEN) following a validated in-house protocol (IARC). The multiplex PCR was performed with six separated primers pools . Per pool, 20 ng (1) or 10 ng (2 and 3) of DNA were dispensed and air-dried (only 2 and 3). Subsequently 11 µL (1) or 5 µL (2 and 3) of the PCR mix were added  and the DNA were amplified in a 96-well-plate as following: 15 min at 95 °C; 25 (1), 21 (2), or 23 (3) cycles of 15 s at 95 °C and 4 min at 60 °C; and 10 min at 72 °C. For each sample, amplified PCR products were pooled together, purified using 1.8x volume of SeraPure magnetic beads  (1) or NucleoMag® NGS Clean-up from Macherey-Nagel (2 and 3) and quantified by Qubit DNA high-sensitivity assay kit (Invitrogen Corporation). One-hundred nanograms of purified PCR product (6 µL) were used for the library preparation with the NEBNext Fast DNA Library Prep Set (New England BioLabs) following an in-house validated protocol (IARC). End repair was performed  followed by ligation to specific adapters and in-house prepared individual barcodes  . Bead purification of 1.8x was applied to clean libraries and 100 ng of adaptator ligated DNA were amplified with 15 µL of Master Mix Amplification . Pooling of libraries was performed equimolarly and loaded on a 2% agarose gel for electrophoresis . Using the GeneClean™ Turbo kit  pooled DNA libraries were recovered from selected fragments of 200–300 bp in length. Libraries quality and quantity were assessed using Agilent High Sensitivity DNA kit on the Agilent 2100 Bioanalyzer on-chip electrophoreses (Agilent Technologies). Sequencing of the libraries was performed on the Ion Torrenthttps://github.com/IARCbioinfo/alignment-nf, revision number 9092214665). This workflow is based on the nextflow domain-specific language47 and consists of three steps: reads mapping (software bwa version 0.7.12-r1044)48, duplicate marking 49, and reads sorting 50. Reads mapping for the targeted sequencing data was performed using the Torrent Suite software version 4.4.2 on reference genome hg19. Local realignment around indels was then performed for both using software ABRA (version 0.97bLE)51 on the regions from the bed files provided by Agilent (SeqCap_EZ_Exome_v2_probe-covered.bed) and QIAGEN, respectively, for the WES and targeted sequencing data. Consistency between sex reported in the clinical data and WES data was assessed by computing the total coverage on X and Y chromosomes  were performed using our in-house workflow 52 was used to call variants. Needlestack is an ultra-sensitive multi-sample variant caller that uses the joint information from multiple samples to disentangle true variants from sequencing errors. We performed two separate multi-sample variant callings to avoid technical batch effects: (1) The 16 WES atypical carcinoids newly sequenced in this study were analysed together with 64 additional WES samples sequenced using the same protocol from another study in order to increase the accuracy of Needlestack to estimate the sequencing error rate; (2) The 15 WES LNET  previously analysed 11 were reanalysed with their matched-normal. For both variant callings, we used default software parameters except for the minimum median coverage to consider a site for calling, the minimum mapping quality, and the SNV and INDEL strand bias13 threshold . Annotation of resulting variant calling format (VCF) files was then performed with ANNOVAR (2018Aprl16)53 using the PopFreqAll , COSMIC v84, MCAP, REVEL, SIFT, and Polyphen (dbnsfp30a) databases.We performed the same variant filtering after each of the two variant callings, based on several stringent criteria. First, we only retained variants that have never been observed in germline databases or present at low frequency (≤ 0.001) but already reported as somatic in the COSMIC database. Second, we only retained variants that were in coding regions and that had an impact on expressed proteins: we filtered out silent, non-damaging single nucleotide variants  and variants present in non-expressed genes . Additionally, for calling (2), we re-assessed the somatic status of variants reported by Needlestack in light of possible contamination errors. Indeed, Needlestack is a very sensitive caller and will sometimes detect low allelic fraction variants in normal tissue that actually come from contamination by tumour cells. In such cases the variant is found in both matched samples and is reported as germline, but we still considered a variant as somatic if its allelic fraction in the normal tissue was at least five times lower than the allelic fraction observed in the tumour.Targeted sequencing data: Software Needlestack was also used to call variants on targeted sequencing data from 16 atypical carcinoids and their matched-normal tissue. We performed the calling with default parameters except for the phred-scaled q-value and minimum median coverage to consider a site . These parameters were decreased compared to WES variants calling because we wanted a larger sensitivity in the validation set than in the discovery set. The annotation procedure was the same as for WES data. No other filters were used.Validation: For both previously published data and data generated in this study, we only report somatic mutations that were validated using a different technique: targeted sequencing, RNA sequencing , or Sanger sequencing. Results are presented Supplementary Data https://github.com/aviari/wginr) that consists of three main steps. First, the dependency between GC content and raw read count is modelled using a generalised additive smoothing model with two nested windows in order to catch short and long distance dependencies. The model is computed on a subset of human genome mappable regions defined by a narrow band around the mode of binned raw counts distribution. This limits the incorporation of true biological signal (losses and gains) by selecting only regions with (supposedly) the same ploidy. In a second step, we collect heterozygous positions in the matched-normal sample and GC-corrected read counts (RC) and alleles frequencies (AF) at these positions are used to estimate the mean tumour ploidy and its contamination by normal tissue. This ploidy model is then used to infer the theoretical absolute copy number levels in the tumour sample. In the third step, a simultaneous segmentation of RC and AF signals  is performed using a bivariate Hidden Markov Model to generate an absolute copy number and a genotype estimate for each segment.Somatic copy number variations (CNVs) were called from WGS data using an in-house pipeline  that uses WGS data and two complementary signals from the read alignments: (a) discordant pair mapping (wrong read orientation or incorrect insert-size) and (b) soft-clipping (unmapped first or last bases of reads) that allows resolving SV breakpoints at the base pair resolution. A cluster of discordant pairs and one or two clusters of soft-clipped reads defined an SV candidate: the discordant pairs cluster defined two associated regions, possibly on different chromosomes and the soft-clipped reads cluster(s), located in these regions, pinpointed the potential SV breakpoint positions. We further checked that the soft-clipped bases at each SV breakpoint were correctly aligned in the neighbourhood of the associated region. SV events were then classified as germline or somatic depending on their presence in the matched-normal sample. Results are presented as Supplementary Data Somatic structural variants (SV) were identified using an in-house tool . We also included validated mutations (we removed silent and intron/exon mutations) reported in SCLC13. In each group the p-values given by Fisher’s exact test performed for all Hallmarks were adjusted for multiple testing. Supplementary Data q-value for each group, as well as the mutated genes for each hallmarks present in each supra-carcinoid, cluster LNET, LCNEC, and SCLC samples.Gene-set enrichment for somatic mutations was assessed independently for each set of Hallmark of cancer genesq-value = 0.075; Supplementary Data We performed several robustness analyses to assess the validity of our results, in particular with regards to outlier samples/genes that would have a high leverage on the statistical results, i.e., that would alone drive the significance of a particular hallmark. First, we assessed the leverage of each individual sample using a jackknife procedure . Second, we assessed the leverage of each gene using a jackknife procedure . We observed that when we removed sample LNEN010 from the cluster LNET B, the sustaining proliferative signalling hallmark enrichment became non-significant at the 0.05 false discovery rate threshold, but was still significant at the 10% threshold , mRNA was purified and fragmented using divalent cations under elevated temperature. The RNA fragments underwent reverse transcription using random primers. This is followed by second strand complementary DNA (cDNA) synthesis with DNA Polymerase I and RNase H. After end repair and A-tailing, indexing adapters were ligated. The products were then purified and amplified (14 PCR cycles) to create the final cDNA libraries. After library validation and quantification , equimolar amounts of library were pooled. The pool was quantified by using the Peqlab KAPA Library Quantification Kit and the Applied Biosystems 7900HT Sequence Detection System. The pool was sequenced by using an Illumina TruSeq PE Cluster Kit v3 and an Illumina TruSeq SBS Kit v3-HS on an Illumina HiSeq 2000 sequencer with a paired-end (101x7x101 cycles) protocol.47 and accessible at https://github.com/IARCbioinfo/RNAseq-nf (revision da7240d). (i) Reads were scanned for a part of Illumina’s 13 bp adapter sequence ′AGATCGGAAGAGC′ at the 3′ end using Trim Galore v0.4.2 with default parameters. (ii) Reads were mapped to reference genome GRCh37 (gencode version 19) using software STAR (v2.5.2b)54 with recommended parameters55. (iii) For each sample, a raw read count table with gene-level quantification for each gene of the comprehensive gencode gene annotation file  was generated using script htseq-count from software htseq (v0.8.0)56. Gene fragments per kilobase million (FPKM) of all genes from the gencode gene annotation file were computed using software StringTie (v1.3.3b)57 in single pass mode (no new transcript discovery), using the protocols from Pertea et al.57 .The 210 raw reads files  were processed in three steps using the RNA-seq processing workflow based on the nextflow languagehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to check raw reads quality, software RSeQC (v. 2.6.4) was used to check alignment quality . Software MultiQC (v. 0.9)58 was used to aggregate the QC results across samples. Concordance between sex reported in the clinical data and sex chromosome gene expression patterns was performed by comparing the sum of variance-stabilised read counts (vst function from R package DESeq2) of each sample on the X and Y chromosomes . The annotation procedure was the same as for WES data.Software Needlestack was also used to call variants on the 20 RNA sequencing data for WES variant validation. Default parameters were used, except for the phred-scaled 13 to detect chimeric transcripts. In brief, paired-end RNA-seq reads were mapped to the human reference genome (NCBI37/hg19) using GSNAP. Potential chimeric fusion transcripts were identified using software TRUP59 by discordant read pairs and by individual reads mapping to distinct chromosomal locations. The sequence context of rearranged transcripts was reconstructed around the identified breakpoint and the assembled fusion transcript was then aligned to the human reference genome to determine the genes involved in the fusion. All interesting fusion-transcript were validated by Sanger sequencing. The code used for the fusion transcript detection is available on https://github.com/ruping/TRUP. All the associated results are presented Supplementary Data RNA-seq data was processed as previously describedome NCBI3/hg19 usi60; this transformation enables comparisons between samples with different library sizes and different variances in expression across genes. We removed genes from the sex-chromosomes in order to reduce the influence of sex on the expression profiles, resulting in a matrix of gene expression with 54,851 genes and 210 samples. We performed four analyses, with different subsets of samples. (i) An analysis with all 210 samples (LNEN and SCLC), (ii) an analysis with LNEN samples only (158 samples), (iii) an analysis with LNET and SCLC samples only (139 samples), and (iv) an analysis with LNET samples only (89 samples). For each analysis, the most variable genes  were selected . Principal component analysis (PCA) was then performed independently for each analysis (function dudi.pca from R package ade4 v1.7-8)61. Results are presented in Supplementary Fig. The raw read counts of 57,822 genes from the 210 samples were normalised using the variance stabilisation transform (vst function from R package DESeq2 v1.14.1)p-values for multiple testing. Results highlighted an association between principal component 2 and provider, histopathological type, and sex, and an association between principal components 4 and 5 and stage  were spatially close in the PCA  analyses. (i) A comparison between histopathological types; (ii) A comparison between pulmonary carcinoid (LNET) clusters A1, A2, and B  and using sex , and age  as covariables. We then extracted DE genes between each pair of groups, and adjusted the p-values for multiple testing. In order to select the genes that have the largest biological effect, we tested the null hypothesis that the two focal groups had less than 2 absolute log2-fold changes differences. For each analysis, we define the core genes of a focal group as the set of genes that are DE in all pairwise comparisons between the focal group and other groups; they correspond to genes, which expression level is specific to the focal group. For example, given three groups—A, B, and C—to find core genes, which expression levels uniquely define A compared to both B and C, we select DE genes that differentiate A from B (A vs. B), DE genes that differentiate A from C (A vs. C) and take the intersection of these gene sets [(A vs. B)∩(A vs. C)]. The code used for the DE analyses (RNAseq_supervised.R) is available at https://github.com/IARCbioinfo/RNAseq_analysis_scripts. Results of analysis (i) are reported in Supplementary Data For each differential expression (DE) analysis, among the 57,822 genes from the raw read count tables, genes that were expressed in less than 2 samples were removed from the analysis, using a threshold of 1 fragment per million reads aligned. We also removed samples with missing data in the variables of interest  or in any of the clinical covariables included in the statistical model (sex and age). This resulted in excluding two samples with missing age data from the three analyses , and further excluding three samples with no clear histopathological type  or A vs. the rest]. Note that this would find genes that are DE between A and the average level of expression of B and C, and thus this alternative method would have the unwanted behaviour of including the genes that are strongly DE in the comparison of A vs. B, but with similar expression levels in A and C. In order to compare the methods we used to detect core genes with this alternative method, we performed an analysis similar to analysis (ii) but comparing a focal group to all the other samples simultaneously (A vs. the rest). The comparison between our method and the alternative one is presented in Supplementary Fig. 62. quanTIseq uses a rigorous RNA-seq processing pipeline to quantify the gene expression of each sample, and performs supervised expression deconvolution in a set of genes identified as informative on immune cell types, using the least squares with equality/inequality constrains (LSEI) algorithm with a reference data set containing expected expression levels for the ten immune cell types. Importantly, quanTIseq also provides estimates of the total proportion of cells in the bulk sequencing that do and do not belong to immune cells.We quantified the proportion of cells that belong to each of ten immune cell types  from the RNA-seq data using software quanTIseq (downloaded 23 March 2018)p-values for multiple testing. The code used for these three analyses is available on https://icbi.i-med.ac.at/software/quantiseq/doc/index.html and the associated results are presented Figs. We tested whether immune composition differed between histopathological types, LNET clusters, LNEN clusters, and supra-carcinoids using linear permutation tests . Permutations tests are exact statistical tests that do not rely on approximations and assumptions regarding the data distribution, and are thus well-fitted to test whether a few samples come from the same distribution as a larger group of samples. As such, they were well-fitted to handle the tests involving supra-carcinoids, for which only three samples had RNA-seq data. For each of the three analyses , and for each pair of groups, we fitted one model per immune cell type, with the proportion of this cell type in each sample as explained variable and the cluster membership as explanatory variable. We adjusted the Epigenome analysis was performed on 33 typical carcinoids, 23 atypical carcinoids, and 20 LCNEC, plus 19 technical replicates. Epigenomic studies were performed at the International Agency for Research on Cancer (IARC) with the Infinium EPIC DNA methylation beadchip platform (Illumina) used for the interrogation of over 850,000 CpG sites (dinucleotides that are the main target for methylation). Each chip encompasses eight samples, so 12 chips were needed for the 95 samples. We used stratified randomisation to mitigate the batch effects, ensuring that the three histopathological types were present on every chip, while also controlling for potential confounders ; replicates were placed on different chips.TM kit  following the manufacturer's recommendations for Infinium assays. Three replicates included half the amount (300 ng). Then, 200 ng of bisulfite-converted DNA was used for hybridisation on Infinium Methylation EPIC beadarrays, following the manufacturer’s protocol (Illumina Inc.). This array shares the Infinium HD chemistry (Illumina Inc.) and a similar laboratory protocol used to interrogate the cytosine markers with HumanMethylation450 beadchip. Chips were scanned using Illumina iScan to produce two-colour raw data files (IDAT format).For each sample, 600 ng of purified DNA were bisulfite converted using the EZ-96 DNA Methylation-Gold63 and ENmix (v. 1.14.0)64. We first removed unwanted technical variation in-between arrays using functional normalisation of the raw two-colour intensities, and computed the β-values for the 866,238 probes and 96 samples. Then, we filtered four types of probes that could confound the analyses. (i) We removed probes on the X and Y chromosomes, because we were interested in variation between tumours and treated sex as a confounder. (ii) We removed known cross-reactive probes—i.e., probes that co-hybridise to other chromosomes and thus cannot be reliably investigated. (iii) We removed probes that had failed in at least one sample, using a detection p-value threshold of 0.01, where p-values were computed with the detection P function from R package minfi, that compares the total signal (methylated + unmethylated) at each probe with the background signal level from non-negative control probes. (iv) We removed probes associated with common SNPs—that reflect underlying polymorphisms rather than methylation profiles—using a threshold minor allele frequency of 5% in database dbSNP build 137 (function dropLociWithSnps from minfi). (v) We removed probes putatively associated with rare SNPs by detecting and removing probes with multimodal β-value distributions (function nmode.mc from R package ENmix). Next, we removed duplicated samples, randomly choosing one sample per pair so as to minimise potential discrepancies, and we removed one sample that came from a metastatic tumour rather than a primary tumour. The final data set contained the β-values of 767,781 CpGs for 76 samples.The resulting IDAT raw data files were pre-processed using R packages minfi (v. 1.24.0)p-values . Concordance between the sex reported in the clinical data and the methylation data was assessed using a predictor based on the median total intensity on sex-chromosomes, with a cutoff of –2 log2 estimated copy number (function getSex from minfi). Consistently with the WES and RNA-seq data, we found one sample with a mismatch between reported and inferred sex . We did not find outliers when comparing the methylated/unmethylated channel intensities of all samples, nor did we find samples with overall low detection β-values of 767,781 CpGs for 76 samples were transformed into M-values to perform unsupervised analyses; indeed, contrary to β-values, M-values theoretically range from −∞ to +∞ and are considered normally distributed. We performed two analyses, with different subsets of samples: (i) an analysis with all carcinoid and LCNEC samples (76 samples), and (ii) an analysis with carcinoid samples only (56 samples). For each analysis, the most variable CpGs  were selected  and (ii). PCA was then performed independently for each analysis (function dudi.pca from R package ade4 v1.7-8)61. Results are presented in Supplementary Fig. The M-values of the 5% most variable CpGs obtained without normalisation  in three independent analyses: (i) between histopathological types , (ii) between LNET clusters , and (iii) between LNEN clusters .M-values (function lmFit from R package limma version 3.34.9)65, using the variable of interest , in addition to the sex, age group, and the ten surrogate variables as covariables. Then, moderated t-tests were performed by empirical Bayes moderation of the standard errors (function eBayes from package limma), and p-values were computed for each CpG. Moderation enables to increase the statistical power of the test by increasing the effective degrees of freedom of the statistics, while also reducing the false-positive rate by protecting against hypervariable CpGs, and are thus favoured in array analyses. The p-values were adjusted for multiple testing, and CpGs with a q-value <0.05 were retained. The code used for the DMPs identification (DMP.R) is available in the Supplementary Software TFF1, OTOP3, SLC35D3, APOBEC2) were also DMPs for cluster B relative to LNEN clusters, showing that they harboured specific methylation levels that made them different from the LCNEC cluster, as well as from other carcinoid clusters.To detect DMPs, for each analysis, linear models were first fitted independently for each CpG to its M-values, respectively. The full data set consisted of 243 samples, but some analyses focused on a subset of the data.We performed an integrative analysis of the WES, WGS, RNA-seq, and 850 K methylation array data, using the validated somatic mutations  that explain most variation in the joint data sets. We did not include the somatic mutations in the model because the low level of recurrence https://github.com/IARCbioinfo/integration_analysis_scripts.We performed four analyses, with different subsets of samples. (i) An analysis with all 235 samples for which expression or methylation data was available (LNEN and SCLC), (ii) an analysis with LNEN samples only (183 samples), (iii) an analysis with LNET and SCLC samples only (163 samples), and (iv) an analysis with LNET samples only (111 samples). For each analysis, the most variable genes for expression  were selected , and the most variable CpGs  were selected . Note that these lists of genes and CpGs are the same as the ones used to perform the unsupervised analyses of expression and methylation data (see above sections). Also note that we did not have EPIC 850k methylation array data for SCLC; MOFA was shown to handle missing data, including samples with entire ‘omic techniques missing, by using the correlated signals from several data sets  to accurately reconstruct latent factors. MOFA was performed independently for each analysis, setting the number of latent factors to 5, because subsequent latent factors explained <2% of the variance of both ‘omic data sets (function runMOFA from R package MOFAtools v0.99.0). Because MOFA uses a heuristic algorithm, we assessed the robustness of the results using 20 MOFA runs. We then computed the correlations between each of the five first-latent factors across each run, resulting in a correlation matrix of 100 by 100 entries  are highlighted in Figs. To perform associations of latent factors with other variables, we used the results from MOFA to detect outliers and batch effects in the data set. We did not detect any outliers in any of the analyses from Supplementary Fig. 15; indeed, non-recurrent genes are not informative about molecular groups. Only two genes were retained: MEN1 and EIF1AX. We also included recurrently mutated genes reported in LCNEC12. Results are highlighted in Fig. We also tested the association between MOFA clusters and mutations using regression analysis. We tested genes recurrently mutated in carcinoids, using a threshold of three samples 66, given a specified number of clusters K, we used the K – 1 latent factors that explained most of the variation to perform clustering; this choice of number of latent factors in Mo et al.66 is said to be primarily motivated by “a general principle for separating g clusters among the n datapoints, a rank-k approximation where k ≤ g − 1 is sufficient.” In addition, because the MOFA latent factors explaining the most variance in gene expression and methylation are expected to capture more biological signal compared to the ones explaining the least variance—expected to represent more of the noise in the data set—we expect that using the first K – 1 latent factors would provide more biologically meaningful clusters than using all latent factors. In addition, following the procedure from Wilkerson and Hayes67, we performed consensus clustering to detect robust molecular clusters. This procedure involved multiple replicate clusterings , each on latent factors from an independent MOFA run done on a subsample (80%) of the data. Pairwise consensus values were defined as the proportion of runs in which two samples are clustered together and used as a similarity measure, and used to perform a final hierarchical clustering (median linkage method). Consensus clustering results for K from 2 to 5, for LNET plus LCNEC samples, and LNET samples alone, are presented in Supplementary Figs. K = 3 clusters , so the solutions with three and four clusters are coherent. Cluster memberships are highlighted in Fig. K = 3, we chose that solution, but note that the cluster memberships for K > 3 are also nested into that for K = 3, so all results are coherent across values of K.We identified molecular clusters—groups of samples with similar molecular profiles—from MOFA results. Following Mo et al.k-means algorithm, where variables (here MOFA latent factors) are given weights corresponding to their proportion of variance explained. More specifically, instead of minimising the within-cluster sum of squares, the weighted within-cluster sum of squares is minimised. Results for K = 3 clusters of LNET and LNEN samples are presented in Supplementary Fig. K-means on five latent factors) leads to the exact same cluster membership as the original approach (K-means on K – 1 latent factors), both for LNEN and LNET clusters. Indeed, among the latent factors, only the first 3 were associated with either the LNEN clusters  or the LNET clusters , which indicates that the first three latent factors captured the differences between clusters. The code used for the clustering analyses (integration_unsupervised.R) is available at https://github.com/IARCbioinfo/integration_analysis_scripts.In order to test whether using additional latent factors could increase the power to detect molecular clusters, we performed a similar analysis but using all five latent factors identified by MOFA. In order to provide more importance to the factors most likely to capture the biological variation in the data, the multiple replicate clusterings were performed using a weighted 15. This tests for each latent factor whether the distribution of the loadings of features (genes or CpGs) from a focal set are significantly different from the global distribution of loadings from features outside the set. We performed the analysis using two reference databases of gene sets: GO and KEGG. To retrieve the appropriate databases, for all genes from the muti-omics integration analysis, we downloaded GO terms using R package biomaRt68, and we retrieved KEGG pathways using R package KEGGgraph (v. 1.38.0)69. Results are presented in Supplementary Data We performed gene set enrichment analysis (GSEA) on the latent factors identified by MOFA using the built-in function FeatureSetEnrichmentAnalysis63.We performed correlation tests in two analyses: (i) between LNET clusters , and (ii) between LNEN clusters . We selected for each gene, the set of CpGs in the region −2000 to +2000 from the transcription start site (TSS) using function getnearestTSS from R package FDb.InfiniumMethylation.hg19 version 2.2.0 based on the IlluminaHumanMethylationEPICanno.ilm10b2.hg19 annotation (get Annotation function from R package minfi version 1.24.0)p-values were adjusted for multiple testing. In addition, we explored the correlation between expression and methylation data by fitting a linear model independently for each correlated CpG (function lm from R package stats version 3.5.1). Finally, we calculated the interquartile distance of β-values for each CpG. CpGs with a q-value < 0.05, r2 > 0.5 and an interquartile distance greater than 0.25 were retained and, among these CpGs, only the one with the smallest q-value has been represented in Supplementary Fig. We performed correlation test analyses (function cor.test from R package stats version 3.5.1) using the core genes lists . To be more stringent, the optimal regularisation parameter chosen was the one associated with the most regularised model with cross-validation error within one standard deviation of the minimum. In order to identify the genes associated with the poor survival of the cluster Carcinoid B, we included in the model only the expression of the core genes of this cluster defined in the MOFA considering only the LNET samples (see section Multi-omics integration). We used the normalised read counts, and centred and scaled them using R package caret (v6.0-80). The genes with non-zero estimated coefficients are listed in Supplementary Data We computed a generalised linear model with elastic net regularisation (R package glmnet v2.0-16)We performed supervised learning in order to classify typical and atypical carcinoids, and LCNEC based on the different 'omics data available: expression and methylation data.Classification algorithm: Each classification was performed using a random forest algorithm (R package randomForest v4.6-14). Considering the restricted number of samples, we performed a leave-one-out cross-validation. For each run, to increase the training set size, minority classes were oversampled so that all classes reach the same number of training samples. Note that for the sample with technical replication of RNA-seq data (S00716_A and S00716_B), in order to avoid model overfitting, the two replicates were never simultaneously included in the training and test sets. Also in order to avoid overfitting, we performed normalisation and independent feature filtering within each fold, so that test samples were excluded from this step. More specifically, for the expression data, the features of the training set were first normalised using the variance stabilisation transformation (vst function from R package DESeq2 v1.22.2), then mean-centred and scaled to unit variance. Then, the variance stabilising transformation learned from the training set was applied to the test set using the dispersionFunction function from the DESeq2 package, and centreing and scaling were performed using the values learned from the training set. For the methylation data, the M values were computed using the R package minfi (v1.28.3); the features of the training set were mean-centred and scaled to unit variance, then the test sample features were centred and scaled using the values learned from the training set. For each fold of the leave-one out, the training set was used for the feature selection. Based on the training set, we selected the most variable features, representing 50% and 5% of the total variation in expression and methylation data, respectively. The code used for the machine learning analyses (ML_functions.r) is available in the Supplementary Software Defining an Unclassified category: The random forest algorithm provides for each predicted sample the class probabilities. We considered a sample as unclassifiable (Unclassified category) if the ratio of the two highest probabilities was below 1.5. In fact, this threshold allowed us to identify a category of samples with intermediate molecular profiles, for which the algorithm assigns similar probabilities to the two most probable classes. Because of the small sample size, this parameter was chosen a priori and not tuned in order to avoid overfitting. In Supplementary Fig. p = 0.086).Number of samples and features: To classify LCNEC against atypical and typical carcinoids, 157 and 76 samples were considered using the expression and methylation data, respectively. The number of features selected in each fold of the leave-one-out are of the order of 6000 and 8000 for expression and methylation features, respectively. For the analysis based on MKI67 only  per model at the expense of the number of features (p), because each model relies only on the number of features in a single data set. An alternative approach is to maximise the number of features (p) by combining both data sets, at the expense of the number of samples n, because of the limited number of samples with both data types available. Indeed, for fixed n increasing p requires less parameters and leads to a higher statistical power. Nevertheless, in our case, because of missing data, increasing p by using both omics layers would drastically reduce n, restricting our sample set  to the set of samples with both layers . Given the existence of very rare entities such as the supra-carcinoids, accurately capturing the diversity of molecular profiles in the training set was our priority, and thus we chose to maximise n. In addition, by maximising n, we hypothetically ensured that we would also maximise the power of the subsequent analyses based on the ML results. To confirm this hypothesis, we performed the ML analyses on the restricted set of samples, including both expression and methylation data in the same model and compared the predictions of this model to the combined predictions based on expression and methylation data separately. We found that the predictions . These analyses are presented in Supplementary Fig. MKI67 expression only  we compared the overall survival of LCNEC, atypical and typical samples based on the histopathological classification and based on the ML predictions , machine learning, differential expression, and differential methylation (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Software 1Reporting Summary


Group 1 was enriched for pathways involved in cell turnover, Group 2 was enriched for metabolic processes and Group 3 for immune‐response pathways. Patients in group 1 showed the worst overall survival (p = 0.019). Key genes for the three subtypes were confirmed by immunohistochemistry. The newly defined intrinsic subtypes were analyzed in four independent datasets of gastric and esophageal adenocarcinomas with transcriptomic data available . The subgroups were represented in the independent cohorts and pooled analysis confirmed the prognostic effect of the new subtypes. In conclusion, adenocarcinomas at the GEJ comprise three distinct molecular phenotypes which do not reflect anatomical location but rather inform our understanding of the key pathways expressed.Cancers occurring at the gastroesophageal junction (GEJ) are classified as predominantly esophageal or gastric, which is often difficult to decipher. We hypothesized that the transcriptomic profile might reveal molecular subgroups which could help to define the tumor origin and behavior beyond anatomical location. The gene expression profiles of 107 treatment‐naïve, intestinal type, gastroesophageal adenocarcinomas were assessed by the Illumina‐HTv4.0 beadchip. Differential gene expression (limma), unsupervised subgroup assignment (mclust) and pathway analysis (gage) were undertaken in R statistical computing and results were related to demographic and clinical parameters. Unsupervised assignment of the gene expression profiles revealed three distinct molecular subgroups, which were not associated with anatomical location, tumor stage or grade ( What's new?Adenocarcinomas that arise at the junction between the esophagus and the stomach are currently classified based on location. Here, the authors looked at patterns of gene expression of these cancers. They found that gastro‐esophageal junction adenocarcinomas can be sorted into three biological subtypes, independent of location, based on gene expression. Group 1 cancers have boosted stomach‐specific genes that combat the effects of acid reflux. Group 2 tumors express genes characteristic to the intestinal tract, and the genes active in Group 3 relate to inflammation. The differences in biological pathway expression means that these differences could be used to improve treatment. ACRGAsian Cancer Research GroupCINchromosomal instability group (subgroup of gastric cancers defined by the TCGA)DDRDNA‐damage repairFDRfalse discovery rateGEJgastroesophageal junctionH. pyloriHelicobacter pyloriICGCInternational Cancer Genome ConsortiumOCCAMSOesophageal Cancer Clinical and Molecular Stratification (study consortium)TCGAThe Cancer Genome Atlas ConsortiumWGSWhole‐genome sequencingIncidence of tumors at the gastroesophageal junction (GEJ) has increased rapidly over the past 50 years.The Cancer Genome Atlas (TCGA) consortium compared the genomic, epigenetic and transcript profiles of esophageal and gastric cancers comprising approximately 550 cancers.According to current knowledge, cancers in the distal part of the GEJ (Siewert Type 3) are more likely to arise from the proximal stomach.The primary aim of our study was to define adenocarcinomas at the GEJ according to their transcriptomic profile. Cases were very carefully selected to ensure that we had precise information on the location of the tumor in relation to the GEJ coupled with other clinical annotation. Since the Siewert classification is the current gold standard for clinical stratification of these tumors, we ensured that we had this information on each case in order to compare it with the molecular subtypes obtained.i) as part of the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) study consortium, coordinated by the University of Cambridge, United Kingdom, (ii) at the local tissue bank at Addenbrooke's Hospital, Cambridge University Hospitals , or (iii) at the University of Magdeburg, Germany, Department of Gastroenterology, Hepatology & Infectious Diseases , before being retrospectively assessed for inclusion in our study. All patients gave written informed consent to tissue archiving and further analyses. The study was conducted in accordance with the Declaration of Helsinki. Tissue samples were obtained either during diagnostic endoscopy or surgical resection of the tumor. Diffuse‐type cancers and tumors with mixed pathology were excluded for the reasons explained in the Introduction.All tissue samples were chemotherapy and radiotherapy‐naïve and prospectively collected either (n = 23) from the core analysis, leaving n = 61 GEJ cancers. Refer to Supporting Information Figure A total of 84 patients with intestinal type adenocarcinoma at the GEJ as defined by Siewert and Stein in 1998  and sent for cellularity review (≥70% tumor cellularity required for cancer samples) by at least two expert pathologists. Careful macrodissection and microdissection were performed to maintain this cellularity threshold.RNA/DNA extraction was performed using the AllPrep kit  and using the QIAamp DNA Blood Maxi kit . RNA with an RNA integrity number (RIN) >7.0 was used for cDNA preparation . Gene expression analysis was carried out on Illumina HT12 version 4.0 beadchip kit.et al.et al.For 41 GEJ cases, whole‐genome sequencing (WGS) data was generated with 50× coverage for the cancer samples and 30× for germline reference samples as part of the International Cancer Genome Consortium (ICGC). Somatic mutations and indels were called using Strelka 1.0.13.For the OCCAMS validation cohort of 158 samples, transcriptome data was generated by RNA sequencing. Libraries were prepared using the Illumina TruSeq Stranded Total RNA Library Prep Kit and 75 bp paired‐end sequencing was performed using the HiSeq 4000 System. RNA‐seq data were aligned to the GRCh37_g1k reference genome using TopHat2. Aligned primary reads were then counted and normalized for gene length and sequencing depth. Log transformation of the expression data was performed as additional step of normalization before final analysis. Downstream analysis (see below) highlighted four outlier samples with extreme distribution of the gene expression pattern which were removed from further analysis resulting in 154 samples that were used for further validation.n = 4, GEJ2: n = 6, GEJ3: n = 6, body: n = 8, antrum: n = 6) immunohistochemical staining was performed on the Leica Bond‐II autostainer.For a subset of 30 treatment‐naïve cancers for which surgical resection specimens were available  with the proportion of stained tumor cells (0: none–10: 100%).limma in R.p Values for limma‐based differential gene expression analyses were adjusted for multiple comparison and represent false discovery rates (FDRs) for the respective tests. All unbiased group assignment was performed using mclust in R.All transcriptome data analyses were performed on R statistical computing using BioconductorThe primary data sets for our study can be accessed as GSE96669. Publicly available datasets were used for validation: “BELFAST” dataset (E‐MTAB‐4666), the “SINGAPORE” cohort GSE15459 and the a) and there was no significant difference in median survival between GEJ1 (22.2 m), GEJ2 (25.9 m) and GEJ3 (29.9 m) tumors .Sixty‐one junctional adenocarcinomas across all three Siewert types  were included in the core analysis. There was no significant difference in clinical parameters between the Siewert types, apart from an expected higher proportion of Barrett's esophagus in patients with GEJ1 cancers. Patients underwent standard clinical treatment pathways according to their stage  was the only gene with differential expression when comparing between GEJ1 and GEJ3 tumors (FDR: p = 0.004), and SESN1 (Sestrin‐1) between GEJ2 and GEJ3 tumors (FDR: p = 0.024). There were no differentially expressed genes below the threshold of p = 0.01 when GEJ1 tumors were compared to GEJ2 cancers .Differential gene expression analysis between tumors of different Siewert types using mclust algorithm,a). Patients were thus assigned by mclust to three subgroups and a group‐by‐group differential gene expression analysis was performed to identify genes defining each subtype . Of the genes mentioned above, 67 genes (82%) were also represented in this parallel analysis which were then selected for further validation  and membranous CLDN18 (p = 0.048), the absolute scores were significantly different between the three subgroups with the highest scores for Group 1. SULF1  was more intensely stained in patients of Group 1 and Group 2 (p = 0.004). Presence of IDO1 positive immune cells was highest in Group 3 tumors  and was associated with IP10 expression in the tumor (p = 0.017).The immunostaining scores for all markers were as expected for each subgroup  showed broadly similar results .Next, we assessed whether there was any association between the new subtypes and clinical parameters. Of 107 cancers, 28 (26.2%) were assigned to Group 1, 39 36.4%) to Group 2 and 40 (37.4%) to Group 3. Overall, there was no relevant difference between the groups with regards to clinical or demographic factors  compared to the other known clinical parameters: stage of disease (p < 0.001), T‐stage (p < 0.001), nodal involvement (p < 0.001) and presence of distant metastases (p < 0.001).Kaplan–Meier analysis revealed a difference in the median overall survival between the three subtypes, with borderline statistical significance (Group 1: 25.9 m p = 0.039), along with nodal involvement and distant metastases. There was no difference in the proportion of patients who underwent a curative or a palliative treatment pathway between each group . There was enrichment across all groups for mutational signatures 1, 2, 3 and 17 as defined by Alexandrov et al.b), which was as expected for gastroesophageal adenocarcinomas.c). In keeping with this, this subgroup also showed a higher proportion of “DDR impaired” positive tumors according to the classification recently published by our groupa and b).For 41 cases, WGS data were available to compare the genomic properties of the new subtypes. Bearing in mind the heterogeneous nature of genomic alterations in this cancer and the relatively small sample size with WGS available,It is crucial to determine if these findings are reproducible in other datasets across other platforms. Four further datasets were available for analysis. These were not necessarily focused on junctional tumors but demonstrate the broad applicability of these molecular subgroups to esophageal and gastric adenocarcinomas independent of their anatomical location. While the OCCAMS dataset was generated based on RNA‐sequencing, the BELFAST, SINGAPORE and ACRG datasets were generated on Affymetrix platforms. The 67 genes panel was applied to all four validation cohorts  for subtn = 51, Group 2: n = 77, Group 3: n = 26), which was also the case for the 63 esophageal adenocarcinomas of the BELFAST cohort . The 191 gastric adenocarcinomas from the SINGAPORE cohortn = 78, Group 2: n = 66, Group 3: n = 47); and subtype assignment was also consistent for the 300 gastric cancers of the Asian Cancer Research Group (ACRG)n = 85, Group 2: n = 108, Group 3: n = 107; Fig. a). For the latter two Asian validation cohorts, our classification showed statistical overlap (p < 0.001) with the subtypes that have been previously proposed by Lei et al.et al.,b). Ethnic origin did not influence the results since there was no difference in the subtype distribution between Western  and Asian  patients (p = 0.967). This was also the case when cohorts with predominantly esophageal cancers were compared to gastric tumor cohorts (p = 0.351).The 154 samples of the OCCAMS cohort recapitulated a three‐group solution as expected (Group 1: p = 0.001). Similarly to our primary cohort, also grade of differentiation (p < 0.001), UICC stage (p < 0.001), nodal involvement (p < 0.001) and distant metastases were influencing factors (p < 0.001). Cox regression analysis including stage, grading and the new subtypes as factors confirmed both stage of disease (p < 0.001) and the new subtypes (p = 0.002) as independent prognostic factor, whereas grading was not confirmed (p = 0.169). In the individual validation cohorts, a moderate statistical difference in outcome could be seen in the BELFAST (p = 0.038) and the SINGAPORE (p = 0.007) cohort, but not in the ACRG (p = 0.075) and OCCAMS (p = 0.796) datasets .A pooled analysis of all 815 cases across all five cohorts (including our primary study cohort) showed significantly different median overall survival, with Group 1 showing the worst and Group 3 the best prognosis  which interestingly also clustered into three groups suggesting that there may be some modules common across multiple cancer types, but they did not show differences in survival  and GZMB  compared to the other groups . The low prevalence of MSI positive cases is in keeping with previous reports for this disease.p = 0.007). Although there is some overlap, MSI status affects only about a third of patients in Group 3 and is therefore unlikely to be a dominant discriminating factor for our classification.We also assessed the association of our new subgroups to MSI status using data from the OCCAMS cohort for which WGS data was available. MSI status was classified as MSI stable (MSS) or MSI‐low/high (MSI‐L/H) as described before.Interestingly, there is a strong association between the new subgroups and presence of Barrett's esophagus. If only patients with junctional cancers were analyzed, there was a dominance of Barrett's positive cases for the subgroups with stromal enhancement and worse prognosis. These data need to be interpreted with care due to the limited numbers in our study and the incomplete data regarding prevalence of Barrett's esophagus. The significantly higher prevalence of Barrett's esophagus is in line with the results of the pathway analysis being suggestive of an influence of bile exposure as well as visceral adipocytes (as seen in obesity) playing a relevant role, both risk factors also relevant for Barrett's metaplasia and its progression.Our study was not designed to develop a prognostic predictor panel. Explorative analysis of the available clinical data showed a modest prognostic effect that we interpret rather as proof‐of‐principle data supporting the biological relevance of our subtypes, rather than being of robust prognostic value when compared to other studies.Interestingly, Kim and colleagues published data on a cohort of 64 patients with EAC, also demonstrating three subgroups when applying nonsupervised clustering on array‐based transcriptome data.et al. demonstrated previously that there is a stage‐dependent stromal signature in Barrett's metaplasia, dysplasia and EAC that is associated with prognosis.The staining results in our cohort further support the transcriptome analysis. Some of the immunohistochemical markers have also been previously tested in malignant and premalignant stages of colorectal and gastroesophageal cancers.In summary, our data show that the transcriptomic profiles of GEJ tumors reflect distinct molecular subgroups of intestinal type gastro‐esophageal adenocarcinomas indicative of cell biological function which is independent of anatomical location. Further understanding the biology of these subtypes will help to refine efforts for individualized targeted treatment as well as strategies for early detection and prevention.Study design: Bornschein J, under supervision from Fitzgerald RC and Malfertheiner P. Article drafting: Bornschein J, Wernisch L, Secrier M and Fitzgerald RC. Biostatistical analysis of the expression data: Bornschein J under guidance from Wernisch L and Newton R, and in cooperation with Perner J. IHC analysis: Bornschein J and Miremadi A. Biological samples process: MacRae S. Quality control of the histopathological data: O'Donovan M. Raw data processing and normalization processes of the expression data: Menon S and Eldridge MD. Whole genome sequencing data analysis: Secrier M, Bower L and Eldridge MD. RNA sequencing dataset curated and process: Devonshire G. Quality control of the clinical data: Bornschein J, Cheah C, and Turkington R. Sample acquisition contribution: Selgrad M and Venerito M. Funding for the study was obtained from Fitzgerald RC who takes responsibility for the data integrity.Appendix S1: Supporting InformationClick here for additional data file.Figure S1 Schematic overview on the sample collection and processing. Radiation‐ and chemotherapy‐naïve samples of gastroesophageal tumors have been prospectively collected before retrospective selection of samples with unequivocal allocation of the tumor location, with special focus on the gastroesophageal junction. Several checkpoints have been included to ensure quality of RNA and DNA before application of the Illumina HT v4.0 beadchip transcript array and whole‐genome sequencing.Click here for additional data file.Figure S2 Output of the mclust algorithm for optimal group selection. The top panels indicate the geometrical distribution of the gene expression data for a different number of groups, with the higher number indicating a better fit of the respective distribution model, whereas the bottom panels show the corresponding principal component plots. The results for the core cohort of 61 GEJ cancers is shown in (a), whereas (b) shows the group including 23 further non‐junctional cancers. In both cases, a three group solution demonstrated the best results.Click here for additional data file.Figure S3 Comparison of genomic data on a subcohort of the study population. Panel (a) shows the comparison of general genomic features such as total numbers of SNVs, tumor ploidy, total number of LOH or aberrant segments, and total number of amplified and deleted segments. There was no statistically significant difference between the groups. Panel (b) shows the distribution of each of the dominant mutational signatures within each group. The key signatures have been published before,Click here for additional data file.Figure S4 Grouping according to the mutational signature subtype.et al. in the exemplary subcohort with whole‐genome sequencing data. Group 3, the group with the best prognostic outcome, was enriched for the “DDR impaired” group as indicated in main data (a). This was not statistically significant. In the OCCAMS validation cohort (b) there was enrichment for “DDR impaired” tumors in Group 2, again without reaching statistical significance.The barcharts illustrate the grouping according to the dominant mutational signature subtype as previously published by Secrier Click here for additional data file.Figure S5 Prognostic outcome in independent tumor cohorts when stratified by the 67‐gene panel. Displayed are Kaplan–Meier curves for overall survival comparing the newly identified tumor subtypes in independent cohorts of patients with other tumor entities. For none of the additional carcinoma entities displayed in the bottom row  a significantly different outcome could be confirmed if the cohorts have been stratified according to the 67‐gene panel.Click here for additional data file.Figure S6 Relative expression of phenotypically relevant genes.CD8A, GZMB, and ASCL2. CD8A and GZMB indicate T‐cell activation and are more highly expressed in Group 3, which is enriched for immune response pathways. ASCL2 is an intestinal stemness marker and is dominant in Group 2 which shows features of metaplastic processes of the intestinal type.The boxplots indicate the relative gene expression of Click here for additional data file.Table S1 Differential gene expression between cancers of different Siewert type, and between junctional and nonjunctional cancers on pairwise comparison as assessed by limmaClick here for additional data file.Table S2 Differential gene expression between the three groups on pairwise comparison as assessed by limmaClick here for additional data file.Table S3 Differential gene expression between the three cancer groups and noncancer controls on pairwise comparison as assessed by limmaClick here for additional data file.Table S4 Cut‐off levels for positive immune‐reactivity score for each marker and results for each subtypeClick here for additional data file.Table S5 Pathway analysis of the three groups based on generally applicable gene set enrichment for pathway analysis (gage) and KEGG as well as Gene Ontology termsClick here for additional data file.Table S6 Ingenuity Pathway Analysis (IPA®) of the three groupsClick here for additional data file.Table S7 Demographic and clinicopathological data of the study cohort (n = 107)Click here for additional data file.