Comprehensive Genomic Profiling of Neuroendocrine Carcinomas of the Gastrointestinal System

Abstract

The neuroendocrine carcinoma of the gastrointestinal system (GIS-NEC) is a rare but highly malignant neoplasm. We analyzed 115 cases using whole-genome/exome sequencing, transcriptome sequencing, DNA methylation assays, and/or ATAC-seq and found GIS-NECs to be genetically distinct from neuroendocrine tumors (GIS-NET) in the same location. Clear genomic differences were also evident between pancreatic NECs (Panc-NEC) and nonpancreatic GIS-NECs (Nonpanc-NEC). Panc-NECs could be classified into two subgroups (i.e., "ductal-type" and "acinar-type") based on genomic features. Alterations in TP53 and RB1 proved common in GIS-NECs, and most Nonpanc-NECs with intact RB1 demonstrated mutually exclusive amplification of CCNE1 or MYC. Alterations of the Notch gene family were characteristic of Nonpanc-NECs. Transcription factors for neuroendocrine differentiation, especially the SOX2 gene, appeared overexpressed in most GIS-NECs due to hypermethylation of the promoter region. This first comprehensive study of genomic alterations in GIS-NECs uncovered several key biological processes underlying genesis of this very lethal form of cancer.

Significance: GIS-NECs are genetically distinct from GIS-NETs. GIS-NECs arising in different organs show similar histopathologic features and share some genomic features, but considerable differences exist between Panc-NECs and Nonpanc-NECs. In addition, Panc-NECs could be classified into two subgroups (i.e., "ductal-type" and "acinar-type") based on genomic and epigenomic features. This article is highlighted in the In This Issue feature, p. 587.

Figure 1.

Genomic alterations of GIS-NECs. A, Landscape of genomic alterations in GIS-NEC cases. The left oncoplot indicates WGS data, and representative gene expression data are obtained from frozen samples. The cases are arranged from left to right according to descending order of the number of SVs in each primary organ. Asterisks, organoid samples. The right oncoplot shows WES data in patients differing from patients available on the WGS data. B, Hematoxylin and eosin (H&E) staining and synaptophysin immunolabeling of TP53 and RB1 double knockout (TR-KO) organoids before and after blocking Notch signaling with a γ-secretase inhibitor (DAPT). The synaptophysin-positive cells were increased by the Notch inhibitor. Scale bar, 100 μm. C, Unsupervised hierarchical cluster analysis with 2,000 high variant probes for DNA methylation in GIS-NECs. D, Integration of RNA-seq and DNA methylation array data comparing GIS-NECs with normal tissues. RNA-seq data were filtered using significant differentially expressed gene (DEG; abs [log2FC] ≥ 1) with significant FDR values (<0.05). DNA methylation assay data were filtered using differentially methylated regions (DMR; abs [Δβ value] ≥ 0.1) with significant adjusted P values (<0.05). In the area of the figure showing high levels of gene expression and hypermethylation, 199 DMRs are situated, of which 39 (19.6%) are transcription factors (TF; red dots) including SOX2 and ASCL1. In contrast, in the area of the figure showing high levels of gene expression and hypomethylation, 424 DMRs are situated, of which 28 (6.6%) are TFs. CHGA, chromogranin A. E, Schematic of NET–AKR fusion genes detected in two gastric NECs. The neuroepithelioma transforming gene1 (NET1) is a specific guanine nucleotide exchange factor for RhoA. Both aldo-keto reductase family 1 members C3 (AKR1C3) and C4 (AKR1C4) are reductase enzymes that play critical roles in the biotransformation of endogenous substrates such as steroids. The chimeric genes demonstrate in-frame fusion of the NET1 amino terminus (exons 1–3) and the AKR1C3 carboxyl terminus (exons 2–9) or the AKR1C4 carboxyl terminus (exons 6–9). NLS, nuclear localization signal; DH, Dbl homology; PH, pleckstrin homology; PDZ, post-synaptic density 95; aa, amino acids. F, Gastric NEC with the Merkel cell polyomavirus (MCPyV; case NE002). The read depth along the polyomavirus genome is shown in blue, and read pairs bridging the polyomavirus genome and the integration site on chromosome 8 are indicated by red lines. Polyomavirus genes are indicated by large T antigen, small T Antigen, VP1, VP2, and VP3.

Figure 2.

Genomic alterations of Panc-NECs and Panc-NETs. A, Landscape of genomic alterations in Panc-NECs and Panc-NETs. The left oncoplot indicates WGS data obtained from frozen samples. The cases are arranged from left to right according to descending order in the number of SV in each pancreatic neuroendocrine neoplasm based on WHO classification 2019. The right oncoplot shows WES data differing from patients available on the WGS data. Asterisk, organoid sample; double asterisk, a case for which tumor purity was not calculated due to the limited number of mutations. B, Unsupervised clustering analysis using gene expression of high variant 160 TFs. The expression of representative TFs specific to each cluster and immune checkpoint molecules is shown at the bottom. C, Unsupervised clustering of the methylation levels with 2,000 high variant CpG probes in Panc-NECs and Panc-NETs. D, Principal component analysis, based on reads of distal elements by ATAC-seq, could clearly discriminate between Panc-NETs (green), acinar-type Panc-NECs (blue, NE033), and ductal-type Panc-NECs (red). Motif enrichment analysis could also clearly discriminate between Panc-NETs (subgroups: PDX1-high and HNF4A-high), acinar-type Panc-NECs, and ductal-type Panc-NECs.

Figure 3.

Landscape of DNA copy-number alterations in Panc-NETs and Panc-NECs. Examination of the chromosome-level copy number allowed stratification into six groups in Panc-NETs. Group 1: recurrent pattern of whole chromosomal loss, affecting specific chromosomes (1, 2, 3, 6, 8, 10, 11, 15, 16, 21, and 22); group 2: recurrent pattern of whole chromosomal loss, affecting specific chromosomes and subsequent WGD; group 3, chromosome-scale LOH to chromosome 11; group 4, chromosome-scale LOH to chromosome 3; group 5, chromosome-scale LOH to nonspecific one or two chromosome(s); group 6, no chromosome-scale LOH. Chromosomes 1–22 are depicted from bottom to top, and individual samples are shown from left to right. Pink indicates chromosome-scale LOH.

Figure 4.

Mutational signature analysis of GIS-NENs. Hierarchical clustering by de novo extraction of mutational signatures with nonnegative matrix factorization of available WGS data. Stability plotting indicated 12 mutational signatures (>0.85; Supplementary Fig. S18). Their profiles and functions were assigned based on COSMIC SBS Signatures (v3.1). Sto, stomach; Eso, esophagus, Col, colorectum; Pan, pancreas; Amp, ampullary; Bil, bile duct; Platinum, platinum chemotherapy treatment; Merkel, gastric NEC with Merkel cell polyomavirus. The parentheses in the left figure indicate cosine similarity to COSMIC signatures.

Figure 5.

Bidirectional differentiation in GIS-NECs. A, Representative microscopic features of GIS-NECs with nonneuroendocrine carcinoma elements (Non-NEC; adenocarcinoma or squamous cell carcinoma). Based on RB1 immunolabeling, these GIS-NECs are classified into three patterns. Pattern 1, loss of RB1 only in the NEC component; pattern 2, loss of RB1 in both the NEC and Non-NEC components; pattern 3, intact RB1 in both the NEC and Non-NEC components. The NEC components are positive for synaptophysin in all cases. NEC, NEC component; Ad, adenocarcinoma component. B, In three cases of GIS-NECs with Non-NEC elements (adenocarcinoma or squamous cell carcinoma), NEC and Non-NEC components were separately macrodissected from FFPE materials. WES was performed for each component. The table shows driver mutations (TVAF ≥ 0.1) identified by OncodriveMUT (39) in the NEC and Non-NEC components in these cases. The genes in bold font are listed in the COSMIC Cancer Gene Census. SCC, squamous cell carcinoma component; aVAF, variant allele frequency adjusted to tumor purity; Liver met., liver metastasis.

Figure 6.

Geographic mapping of subclones based on multiregion WES and proposed clonal evolution of the Panc-NEC in the autopsied patient. A, Proposal clonal evolution model drawn according to the evolutional lineage tree based on VAF of the mutations (LICHeE; ref. 40) in 20 primary regions and 5 liver metastases. The numbers inside the circles are for mutations used by LICHeE to infer the subclonal structure. The colors in each subdivision describe the mutation groups characterizing cells in this subpopulation. The numbers and colors inside the squares indicate the region numbers shown in B and the composition of subpopulations, respectively. B, Macroscopic picture of the maximum section through the primary Panc-NEC. Sections are marked, corresponding to the colors of the predicted subclones based on the evolutional lineage tree. C, Microscopic picture of region 12 in the primary Panc-NEC (H&E staining). Copy-number analysis demonstrated that WGD occurred in only the adjacent regions 12, 16, and 17 (Supplementary Table S20). Adenocarcinoma component observed together with NEC only in region 12. NEC, NEC component; Ad, adenocarcinoma component. D, Pie chart showing the relationship between mutations detected in plasma cfDNA and in tissue samples (20 primary regions and 5 liver metastases).

Figure 7.

Schematic diagram of genomic alterations involved in the genesis of pancreatic NEC, pancreatic NET, and nonpancreatic NEC of the gastrointestinal system (GIS). NE-TF, transcription factor for neuroendocrine differentiation.