Genomic landscape of pancreatic neuroendocrine tumors

Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.

Spatiotemporal heterogeneity and clinical challenge of pancreatic neuroendocrine tumors

During the course of pancreatic neuroendocrine tumors (NETs), they generally become more heterogeneous with individual cells exhibiting distinct molecular fingerprints. This heterogeneity manifests itself through an unequal distribution of genetically-variant, tumor cell subpopulations within disease locations (i.e., spatial heterogeneity) or changes in the genomic landscape over time (i.e., temporal heterogeneity); these characteristics complicate clinical diagnosis and treatment. Effective, feasible tumor heterogeneity detection and eradication methods are essential to overcome the clinical challenges of pancreatic NETs. This review explores the molecular fingerprints of pancreatic NETs and the spectrum of tumoral heterogeneity. We then describe the challenges of assessing heterogeneity by liquid biopsies and imaging modalities and the therapeutic challenges for pancreatic NETs. In general, navigating these challenges, refining approaches for translational research, and ultimately improving patient care are available once we have a better understanding of intratumoral spatiotemporal heterogeneity.

Comprehensive Genomic Profiling of Gastroenteropancreatic Neuroendocrine Neoplasms (GEP-NENs)

PURPOSE

GEP-NENs are rare malignancies with increasing incidence. Their molecular characteristics are still undefined. We explored the underlying biology of GEP-NENs and the differences between gastrointestinal (GI) and pancreatic (PNEN), high-grade (HG), and low-grade (LG) tumors.

EXPERIMENTAL DESIGN

GEP-NENs were analyzed using next-generation sequencing (NGS; MiSeq on 47 genes, NextSeq on 592 genes), IHC, and in situ hybridization. Tumor mutational burden (TMB) was calculated on the basis of somatic nonsynonymous missense mutations, and microsatellite instability (MSI) was evaluated by NGS of known MSI loci.

RESULTS

In total, 724 GEP-NENs were examined: GI (N = 469), PNEN (N = 255), HG (N = 135), and LG (N = 335). Forty-nine percent were female, and median age was 59. Among LG tumors, the most frequently mutated genes were ATRX (13%), ARID1A (10%), and MEN1 (10%). HG tumors showed TP53 (51%), KRAS (30%), APC (27%), and ARID1A (23%). Immune-related biomarkers yielded a lower prevalence in LG tumors compared with HG [MSI-H 0% vs. 4% (P = 0.04), PD-L1 overexpression 1% vs. 6% (P = 0.03), TMB-high 1% vs. 7% (P = 0.05)]. Compared with LG, HG NENs showed a higher mutation rate in BRAF (5.4% vs. 0%, P < 0.0001), KRAS (29.4% vs. 2.6%, P < 0.0001), and PI3KCA (7% vs. 0.3%, P < 0.0001). When compared with GI, PNEN carried higher frequency of MEN1 (25.9% vs. 0.0%, P < 0.0001), FOXO3 (8.6% vs. 0.8%, P = 0.005), ATRX (20.6% vs. 2.0%, P = 0.007), and TSC2 (6.3% vs. 0.0%, P = 0.007), but lower frequency of mutations in APC (1.0% vs. 13.8%, P < 0.0001).

CONCLUSIONS

Significant molecular differences were observed in GEP-NENs by tumor location and grade, indicating differences in carcinogenic pathways and biology.

[Gastroenteropancreatic neuroendocrine neoplasms-Heterogeneity, management and perspectives of treatment and research]

The term neuroendocrine neoplasms (NEN) encompasses a molecularly and biologically very heterogeneous group of tumors, which have in common their origin in neuroendocrine cells. The also very heterogeneous subgroup of gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) is the best classified and investigated group. This article provides a systematic review of the current classification, diagnostics and treatment options of GEP-NEN. In order to achieve a better overview, it was consciously decided not to use an approach based on the primary localization. Instead, a thematic organization according to classification, clinical phenotype, diagnostics and treatment was chosen.

Differences between Well-Differentiated Neuroendocrine Tumors and Ductal Adenocarcinomas of the Pancreas Assessed by Multi-Omics Profiling

Most pancreatic neuroendocrine tumors (PNETs) are indolent, while pancreatic ductal adenocarcinomas (PDACs) are particularly aggressive. To elucidate the basis for this difference and to establish the biomarkers, by using the deep sequencing, we analyzed somatic variants across coding regions of 409 cancer genes and measured mRNA/miRNA expression in nine PNETs, eight PDACs, and four intestinal neuroendocrine tumors (INETs). There were 153 unique somatic variants considered pathogenic or likely pathogenic, found in 50, 57, and 24 genes in PDACs, PNETs, and INETs, respectively. Ten and 11 genes contained a pathogenic mutation in at least one sample of all tumor types and in PDACs and PNETs, respectively, while 28, 34, and 11 genes were found to be mutated exclusively in PDACs, PNETs, and INETs, respectively. The mRNA and miRNA transcriptomes of PDACs and NETs were distinct: from 54 to 1659 differentially expressed mRNAs and from 117 to 250 differentially expressed miRNAs exhibited high discrimination ability and resulted in models with an area under the receiver operating characteristics curve (AUC-ROC) >0.9 for both miRNA and mRNA. Given the miRNAs high stability, we proposed exploring that class of RNA as new pancreatic tumor biomarkers.

Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes

Background

Detection of DNA copy number alterations (CNAs) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales, and distinct biological origins and functions. Although several computational tools are available to identify CNAs using read depth (RD) signal, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. Additionally, RD signal is affected by overdispersion-driven biases at low coverage, which significantly inflate false detection of CNA regions.

Results

We have developed CNAtra framework to hierarchically discover and classify ‘large-scale’ and ‘focal’ copy number gain/loss from a single whole-genome sequencing (WGS) sample. CNAtra first utilizes a multimodal-based distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We implemented Savitzky-Golay smoothing filter and Modified Varri segmentation to capture the change points of the RD signal. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy numbers. Next, we identified focal alterations in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. Using cancer cell lines and patient datasets, we confirmed CNAtra’s ability to detect and distinguish the segmental aneuploidies and focal alterations. We used realistic simulated data for benchmarking the performance of CNAtra against other single-sample detection tools, where we artificially introduced CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall and f-measure. CNAtra shows the highest sensitivity of 93 and 97% for detecting large-scale and focal alterations respectively. Visual inspection of CNAs revealed that CNAtra is the most robust detection tool for low-coverage cancer data.

Conclusions

CNAtra is a single-sample CNA detection tool that provides an analytical and visualization framework for CNA profiling without relying on any reference control. It can detect chromosome-level segmental aneuploidies and high-confidence focal alterations, even from low-coverage data. CNAtra is an open-source software implemented in MATLAB^®. It is freely available at https://github.com/AISKhalil/CNAtra.

The pathogenic role of the GIP/GIPR axis in human endocrine tumors: emerging clinical mechanisms beyond diabetes

The glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone produced in the gastrointestinal tract in response to nutrients. GIP has a variety of effects on different systems, including the potentiation of insulin secretion from pancreatic β-cells after food intake (i.e. incretin effect), which is probably the most important. GIP effects are mediated by the GIP receptor (GIPR), a G protein-coupled receptor expressed in several tissues, including islet β-cells, adipocytes, bone cells, and brain. As well as its involvement in metabolic disorders (e.g. it contributes to the impaired postprandial insulin secretion in type 2 diabetes (T2DM), and to the pathogenesis of obesity and associated insulin resistance), an inappropriate GIP/GIPR axis activation of potential diagnostic and prognostic value has been reported in several endocrine tumors in recent years. The ectopic GIPR expression seen in patients with overt Cushing syndrome and primary bilateral macronodular adrenal hyperplasia or unilateral cortisol-producing adenoma has been associated with an inverse rhythm of cortisol secretion, with low fasting morning plasma levels that increase after eating. On the other hand, most acromegalic patients with an unusual GH response to oral glucose suppression have GIPR-positive somatotropinomas, and a milder phenotype, and are more responsive to medical treatment. Neuroendocrine tumors are characterized by a strong GIPR expression that may correlate positively or inversely with the proliferative index MIB-1, and that seems an attractive target for developing novel radioligands. The main purpose of this review is to summarize the role of the GIP/GIPR axis in endocrine neoplasia, in the experimental and the clinical settings.

Molecular Signatures and Their Clinical Utility in Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (PNETs) are classified based on their histologic differentiation and proliferative indices, which have been used extensively to determine prognosis. Advances in next-generation sequencing and other high-throughput techniques have allowed researchers to objectively explore tumor specimens and learn about the genetic alterations associated with malignant transformation in PNETs. As a result, targeted, pathway-specific therapies have been emerging for the treatment of unresectable and metastatic disease. As we continue to trial various pharmaceutical products, evidence from studies using multi-omics approaches indicates that clinical aggressiveness stratifies along other genotypic and phenotypic demarcations, as well. In this review, we explore the clinically relevant and potentially targetable molecular signatures of PNETs, their associated trials, and the overall differences in reported prognoses and responses to existing therapies.

The Molecular and Clinical Landscape of Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors are rare tumors of the pancreas originating from the islets of the Langerhans. These tumors comprise 1% to 3% of all newly diagnosed pancreatic cancers every year and have a unique heterogeneity in clinical presentation. Whole-genome sequencing has led to an increased understanding of the molecular biology of these tumors. In this review, we will summarize the current knowledge of the signaling pathways involved in the tumorigenesis of pancreatic neuroendocrine tumors as well as the major studies targeting these pathways at preclinical and clinical levels.

Site-Specific Genomic Alterations in a Well-Differentiated Pancreatic Neuroendocrine Tumor With High-Grade Progression

The major categories of pancreatic neuroendocrine tumor (PanNET) are well-differentiated NET and poorly differentiated neuroendocrine carcinoma. Sequencing of these tumors has identified multiple important genes in the pathogenesis of PanNETs, such as DAXX/ATRX, MEN1, TP53, RB, and mTOR pathway genes. We identified a case of well-differentiated PanNET with high-grade progression with simultaneous low- and high-grade histologic regions containing variable genomic profiles. We performed tumor microdissection and analyzed both regions using a 409-gene comprehensive cancer panel using next-generation sequencing in addition to immunohistochemical and morphologic studies. The low-grade region showed a change in the DAXX gene as a copy number variant (CNV) deletion. The high-grade region showed CNV deletion changes in the DAXX gene as well as the MEN1 gene. We observed additional mutational changes in the PTEN gene and SMAD4 gene in the high-grade region. Our data support that high-grade progression in PanNETs may be the result of the progressive accumulation of genetic changes (CNVs and point mutational changes) within the body of the tumor. Next generation sequencing may provide pathologists and clinicians with ancillary information to accurately characterize and treat these tumors.

Electrophoretic cytopathology resolves ERBB2 forms with single-cell resolution

In addition to canonical oncoproteins, truncated isoforms and proteolysis products are implicated in both drug resistance and disease progression. In HER2-positive breast tumors, expression of truncated HER2 isoforms resulting from alternative translation and/or carboxy-terminal fragments (CTFs) resulting from proteolysis (collectively, t-erbB2) have been associated with shortened progression-free survival of patients. Thus, to advance clinical pathology and inform treatment decisions, we developed a high-selectivity cytopathology assay capable of distinguishing t-erbB2 from full-length HER2 expression without the need for isoform-specific antibodies. Our microfluidic, single-cell western blot, employs electrophoretic separations to resolve full-length HER2 from the smaller t-erbB2 in each ~28 pL single-cell lysate. Subsequently, a pan-HER2 antibody detects all resolved HER2 protein forms via immunoprobing. In analysis of eight breast tumor biopsies, we identified two tumors comprised of 15% and 40% t-erbB2-expressing cells. By single-cell western blotting of the t-erbB2-expressing cells, we observed statistically different ratios of t-erbB2 proteins to full-length HER2 expression. Further, target multiplexing and clustering analyses scrutinized signaling, including ribosomal S6, within the t-erbB2-expressing cell subpopulation. Taken together, cytometric assays that report both protein isoform profiles and signaling state offer cancer classification taxonomies with unique relevance to precisely describing drug resistance mechanisms in which oncoprotein isoforms/fragments are implicated.

The neuroendocrine phenotype, genomic profile and therapeutic sensitivity of GEPNET cell lines

Experimental models of neuroendocrine tumour disease are scarce, and no comprehensive characterisation of existing gastroenteropancreatic neuroendocrine tumour (GEPNET) cell lines has been reported. In this study, we aimed to define the molecular characteristics and therapeutic sensitivity of these cell lines. We therefore performed immunophenotyping, copy number profiling, whole-exome sequencing and a large-scale inhibitor screening of seven GEPNET cell lines. Four cell lines, GOT1, P-STS, BON-1 and QGP-1, displayed a neuroendocrine phenotype while three others, KRJ-I, L-STS and H-STS, did not. Instead, these three cell lines were identified as lymphoblastoid. Characterisation of remaining authentic GEPNET cell lines by copy number profiling showed that GOT1, among other chromosomal alterations, harboured losses on chromosome 18 encompassing the SMAD4 gene, while P-STS had a loss on 11q. BON-1 had a homozygous loss of CDKN2A and CDKN2B, and QGP-1 harboured amplifications of MDM2 and HMGA2 Whole-exome sequencing revealed both disease-characteristic mutations (e.g. ATRX mutation in QGP-1) and, for patient tumours, rare genetic events (e.g. TP53 mutation in P-STS, BON-1 and QGP-1). A large-scale inhibitor screening showed that cell lines from pancreatic NETs to a greater extent, when compared to small intestinal NETs, were sensitive to inhibitors of MEK. Similarly, neuroendocrine NET cells originating from the small intestine were considerably more sensitive to a group of HDAC inhibitors. Taken together, our results provide a comprehensive characterisation of GEPNET cell lines, demonstrate their relevance as neuroendocrine tumour models and explore their therapeutic sensitivity to a broad range of inhibitors.

Intrinsic Contact Between T and N Classifications in Resected Well-Moderately Differentiated Locoregional Pancreatic Neuroendocrine Neoplasms

BACKGROUND

The role of N classification is controversial in several prognostication systems proposed for pancreatic neuroendocrine neoplasms (pNENs). The widely accepted modified European Neuroendocrine Tumor Society (mENETS) system suggests this contradiction may be related to T classification.

METHODS

Data were collected retrospectively from 981 patients in the Surveillance, Epidemiology, and End Results (SEER) database (1973-2012; cohort 1) and 140 patients from the Pancreatic Cancer Institute of Fudan University (2006-2016; cohort 2). All patients had resected well- to moderately differentiated locoregional pNENs, whereby the mENETS system was adopted. Factors related to N1 classification and the association between N and T classifications were analyzed, and N classification prognosis based on T classification was assessed.

RESULTS

In cohorts 1 and 2, tumor size (2-4 cm: p < 0.001 and p = 0.037, respectively; > 4 cm: p < 0.001 and p = 0.012, respectively) and tumors extending beyond the pancreas (p < 0.001 and p = 0.016, respectively), which are factors for T classification, affected N1 classification. For tumors limited to the pancreas, the N1 classification was associated with tumor size (p < 0.001 and p = 0.046, respectively) and predicted poor disease-specific survival (DSS), while for tumors extending beyond the pancreas, the N1 classification did not affect patient outcomes. Findings obtained with data from the SEER database were reproducible with our institutional data.

CONCLUSIONS

N classification is associated with T classification, limiting the value of N1 classification for the pNENs tumor-node-metastasis system. A new risk model is necessary to predict patient outcomes and guide clinical practice for the prognosis of pNENs.

The Role of Genetics in Sporadic GEP-NETs: A Comprehensive Review of the Literature

Neuroendocrine tumors (NETs) are composed of a heterogeneous group of malignancies from neuroendocrine cell compartments, with roles in both the endocrine and the nervous system. The majority of NETs are gastroenteropancreatic (GEP) in origin, arising in the foregut, midgut, or hindgut. The genomic landscape of GEP-NETs has been scarcely studied in terms of genomic profiling.The following algorithm was followed using the keywords neuroendocrine, genomics, targeted therapy, personalized medicine, gastroenteropancreatic and NET. The search was performed in PubMed and ScienceDirect database. Our current knowledge of sporadic GEP-NETs genetics must be further advanced to elucidate the molecular basis and pathogenesis of the disease, improve the accuracy of diagnosis, and guide tailor-made therapies.

Clinical and Preclinical Advances in Gastroenteropancreatic Neuroendocrine Tumor Therapy

The molecular events leading to gastroenteropancreatic neuroendocrine tumor (GEP-NET) formation are largely unknown. Over the past decades, systemic chemotherapies have been replaced by therapies directed at particular molecular targets such as the somatostatin receptors, mTOR complexes or proangiogenic molecules. These approaches have demonstrated some success in subtypes of this heterogeneous tumor group, but responses are still widely varied. This review highlights the clinical trials ongoing for neuroendocrine tumors (NETs) and includes emerging immunotherapy, which holds great promise for NETs based on successes in other tumor types. Current avenues of preclinical research, including Notch and PI3K/AKT, will lead to additional targeted therapies based on genome-wide studies that have cast a wide net in the search for driver mutations. Future preclinical and clinical investigations are required to identify those mutations predictive of therapeutic response or disease progression. Results of current clinical trials outlined here will better inform patient management with respect to agent selection, timing, duration and combination therapy in the treatment of NETs.

Long-term acquired everolimus resistance in pancreatic neuroendocrine tumours can be overcome with novel PI3K-AKT-mTOR inhibitors

Background:

The mTOR-inhibitor everolimus improves progression-free survival in advanced pancreatic neuroendocrine tumours (PNETs). However, adaptive resistance to mTOR inhibition is described.

Methods:

QGP-1 and BON-1, two human PNET cell lines, were cultured with increasing concentrations of everolimus up to 22 weeks to reach a dose of 1 μM everolimus, respectively, 1000-fold and 250-fold initial IC₅₀. Using total DNA content as a measure of cell number, growth inhibitory dose–response curves of everolimus were determined at the end of resistance induction and over time after everolimus withdrawal. Response to ATP-competitive mTOR inhibitors OSI-027 and AZD2014, and PI3K-mTOR inhibitor NVP-BEZ235 was studied. Gene expression of 10 PI3K-Akt-mTOR pathway-related genes was evaluated using quantitative real-time PCR (RT–qPCR).

Results:

Long-term everolimus-treated BON-1/R and QGP-1/R showed a significant reduction in everolimus sensitivity. During a drug holiday, gradual return of everolimus sensitivity in BON-1/R and QGP-1/R led to complete reversal of resistance after 10–12 weeks. Treatment with AZD2014, OSI-027 and NVP-BEZ235 had an inhibitory effect on cell proliferation in both sensitive and resistant cell lines. Gene expression in BON-1/R revealed downregulation of MTOR, RICTOR, RAPTOR, AKT and HIF1A, whereas 4EBP1 was upregulated. In QGP-1/R, a downregulation of HIF1A and an upregulation of ERK2 were observed.

Conclusions:

Long-term everolimus resistance was induced in two human PNET cell lines. Novel PI3K-AKT-mTOR pathway-targeting drugs can overcome everolimus resistance. Differential gene expression profiles suggest different mechanisms of everolimus resistance in BON-1 and QGP-1.