Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms

Pancreatic neuroendocrine neoplasms (pNENs): Genetic and environmental biomarkers for risk of occurrence and prognosis

Pancreatic neuroendocrine neoplasms (pNENs) are rare and heterogeneous tumors arising from neuroendocrine cells, representing approximately 10 % of all Gastro-Entero-Pancreatic neuroendocrine neoplasms. While most pNENs are sporadic, a subset is associated with genetic syndromes such as multiple endocrine neoplasia type 1 (MEN1) or von Hippel-Lindau disease (VHL). pNENs are further classified into functioning and non-functioning tumors, with distinct clinical behaviors, prognoses, and treatment approaches. This review explores genetic and environmental biomarkers that influence the risk, prognosis, and therapeutic responses in pNENs. The epidemiology of pNENs reveals an increasing incidence, primarily due to advancements in imaging techniques. Genetic factors play a pivotal role, with germline mutations in MEN1, VHL, and other genes contributing to familial pNENs. Somatic mutations, including alterations in the mTOR pathway and DNA maintenance genes such as DAXX and ATRX, are critical in sporadic pNENs. These mutations, along with epigenetic dysregulation and transcriptomic alterations, underpin the diverse clinical and molecular phenotypes of pNENs. Emerging evidence suggests that epigenetic changes, including DNA methylation profiles, can stratify pNEN subtypes and predict disease progression. Environmental and lifestyle factors, such as diabetes, smoking, and chronic pancreatitis, have been linked to an increased risk of sporadic pNENs. While the association between these factors and tumor progression is still under investigation, their potential role in influencing therapeutic outcomes warrants further study. Advances in systemic therapies, including somatostatin analogs, mTOR inhibitors, and tyrosine kinase inhibitors, have improved disease management. Biomarkers such as Ki-67, somatostatin receptor expression, and O6-methylguanine-DNA methyltransferase (MGMT) status are being evaluated for their predictive value. Novel approaches, including the use of circulating biomarkers (NETest, circulating tumor cells, and ctDNA) and polygenic risk scores, offer promising avenues for non-invasive diagnosis and monitoring. Despite these advancements, challenges remain, including the need for large, well-annotated datasets and validated biomarkers. Future research should integrate multi-omics approaches and leverage liquid biopsy technologies to refine diagnostic, prognostic, and therapeutic strategies. Interdisciplinary collaborations and global consortia are crucial for overcoming current limitations and translating research findings into clinical practice. These insights hold promise for improving prevention, early detection, and tailored treatments, ultimately enhancing patient outcomes.

Genomic and transcriptomic landscapes of metastatic neuroendocrine neoplasms from distinct primary sites and their clinical implications

Neuroendocrine neoplasms (NENs) encompass a highly heterogeneous group of neoplasms with varying prognoses and molecular alterations. Molecular profiling studies have furthered our understanding of NENs, but the majority of previous studies have focused on primary tumors and on mutational landscapes using DNA sequencing data. Here, we describe the genomic and transcriptomic landscapes of 28 metastatic NENs across different primary anatomical sites (PASs) and their potential clinical implications. Although our cohort is small, our analyses provide further insights on the molecular commonalities and distinctions between metastatic NENs of different PASs. Comparison to several reference transcriptome data sets revealed that despite considerable whole genome and transcriptome variability in NENs, the metastatic NENs are still more like each other than other cancer types. Our study also highlights the potential utility of NEN transcriptome data for molecular classification and clinical decision making.

CEP55, A Promising Prognostic Biomarker for Pancreatic Neuroendocrine Neoplasms, Promotes Tumor Progression Through Activation of PI3K/AKT/mTOR Pathway

Pancreatic neuroendocrine neoplasms (pNENs) exhibit significant heterogeneity, and the effectiveness of traditional classification methods in predicting tumor biological behavior and patient prognosis is limited. This study aims to reveal potential biomarkers to predict the prognosis of pNENs and explore the underlying mechanisms. Four mRNA sequencing datasets of pNENs were included in the study. CEP55, TPX2, and BIRC2 were identified as overlapping DEGs and were significantly associated with the clinical characteristics and prognosis of pNENs. The nomogram, which incorporated independent prognostic risk factors such as CEP55 expression, tumor grade, and TNM stage, demonstrated higher predictive efficiency than traditional methods. We found that knockdown of CEP55 resulted in the inhibition of proliferation, migration, and invasion in pNENs cells, while a reverse trend was observed in CEP55-overexpressing cells. Furthermore, CEP55 was found to enhance the PI3K/AKT/mTOR pathway in pNENs through its interaction with PI3K-p110. Everolimus, an mTOR inhibitor, was shown to counteract the effects of CEP55 overexpression both in vivo and in vitro. In conclusion, CEP55 may enhance the proliferation, invasion, and migration of pNENs by activating the PI3K/AKT/mTOR pathway through its interaction with PI3K. It may serve as a valuable prognostic marker and a promising therapeutic target.

Proteogenomic characterization of non-functional pancreatic neuroendocrine tumors unravels clinically relevant subgroups

The majority of neuroendocrine neoplasms in pancreas are non-functional pancreatic neuroendocrine tumors (NF-PanNETs), which exhibit a high occurrence of distant metastases with limited therapeutic options. Here, we perform a comprehensive molecular characterization of 108 NF-PanNETs through integrative analysis of genomic, transcriptomic, proteomic, and phosphoproteomic profiles. Proteogenomic analysis provides functional insights into the genomic driver alterations of NF-PanNETs, revealing a potential mediator of MEN1 alterations using Men1-conditional knockout mice. Machine-learning-based modeling uncovers a three-protein signature as an independent prognostic factor, which is validated by an independent external cohort. Proteomic and phosphoproteomic-based stratification identifies four subtypes with distinct molecular characteristics, immune microenvironments, and clinicopathological features. Drug screening using patient-derived tumor organoids identifies cyclin-dependent kinase (CDK) 5 and Calcium Voltage-Gated Channel Subunit Alpha1 D (CACNA1D) as ubiquitous and subtype-specific targets, respectively, with in vivo validation using xenograft models. Together, our proteogenomic analyses illustrate a comprehensive molecular landscape of NF-PanNETs, revealing biological insights and therapeutic vulnerabilities.

Molecular Classification and Characterization of Noninsulinoma: Ready for Prime Time in Clinical Practice?

Pancreatic neuroendocrine tumors are a heterogeneous group of rare clinical tumors, which can be classified into functional pancreatic neuroendocrine tumor (insulinoma is the most common) and noninsulinoma. Insulinoma and noninsulinoma have different mutation profiles. In noninsulinoma, ATRX/DAXX mutation is associated with alternative lengthening of telomeres-positive phenotype and positively correlated with poor prognosis. Copy number variation is also a prognostic marker for a high risk of recurrence. Scholars have used epigenetics as well as a multiomics approach (combining epigenetics, metabolomics, proteomics, etc) to molecularly type noninsulinoma, and there are huge differences in molecular expression and patient prognosis between different groups. In this manuscript, we summarize the published studies that utilized genome, epigenome, transcriptome, and proteome data to classify noninsulinoma.

Identification of metabolite biomarkers for pancreatic neuroendocrine tumours using a metabolomic approach

IMPORTANCE

Metabolic flexibility, a key hallmark of cancer, reflects aberrant tumour changes associated with metabolites. The metabolic plasticity of pancreatic neuroendocrine tumours (pNETs) remains largely unexplored. Notably, the heterogeneity of pNETs complicates their diagnosis, prognosis, and therapeutic management.

OBJECTIVE

Here, we compared the plasma metabolomic profiles of patients with pNET and non-cancerous individuals to understand metabolic dysregulation.

DESIGN, SETTING, PARTICIPANTS, INTERVENTION AND MEASURE

Plasma metabolic profiles of 76 patients with pNETs and 38 non-cancerous individuals were analyzed using LC-MS/MS and FIA-MS/MS (Biocrates AbsoluteIDQ p180 kit). Statistical analyses, including univariate and multivariate methods, were performed along with the generation of receiver operating characteristic (ROC) curves for metabolomic signature identification.

RESULTS

Compared with non-cancerous individuals, patients with pNET exhibited elevated levels of phosphoglyceride metabolites and reduced acylcarnitine levels, indicating an upregulation of fatty acid oxidation (FAO), which is crucial for the energy metabolism of pNET cells and one-carbon metabolism metabolites. Elevated glutamate levels and decreased lipid metabolite levels have been observed in patients with metastatic pNETs. Patients with the germline MEN1 mutations showed lower amino acid metabolites and FAO, with increased metabolites related to leucine catabolism and lipid metabolism, compared to non-MEN1 mutated patients. The highest area under the ROC curve was observed in patients with pNET harbouring MEN1 mutations.

CONCLUSION AND RELEVANCE

This study highlights the distinct plasma metabolic signatures of pNETs, including the critical role of FAO and elevated glutamate levels in metastasis, supporting the energy and biosynthetic needs of rapidly proliferating tumour cells. Mapping of these dysregulated metabolites may facilitate the identification of new therapeutic targets for pNETs management.

Tumor and α-SMA-expressing stromal cells in pancreatic neuroendocrine tumors have a distinct RNA profile depending on tumor grade

Alpha-smooth muscle actin (α-SMA) expression in the stroma is linked to the presence of cancer-associated fibroblasts and is known to correlate with worse outcomes in various tumors. In this study, using a GeoMx digital spatial profiling approach, we characterized the gene expression of the tumor and α-SMA-expressing stromal cell compartments in pancreatic neuroendocrine tumors (PanNETs). The profiling was performed on tissues from eight retrospective cases (three grade 1, four grade 2, and one grade 3). Selected regions of interest were segmented geometrically based on tissue morphology and fluorescent signals from synaptophysin and α-SMA markers. The α-SMA-expressing stromal-cell-associated genes were involved in pathways of extracellular matrix modification, whereas, in tumor cells, the gene expression profiles were associated with pathways involved in cell proliferation. The comparison of gene expression profiles across all three PanNET grades revealed that the differences between grades are not only present at the level of the tumor but also in the α-SMA-expressing stromal cells. Furthermore, the tumor cells from regions with a rich presence of adjacent α-SMA-expressing stromal cells revealed an upregulation of matrix metalloproteinase-9 (MMP9) expression in grade 3 tumors. This study provides an in-depth characterization of gene expression profiles in α-SMA-expressing stromal and tumor cells, and outlines potential crosstalk mechanisms.

Conditional Activation of c-MYC in Distinct Catecholaminergic Cells Drives Development of Neuroblastoma or Somatostatinoma

c-MYC is an important driver of high-risk neuroblastoma. A lack of c-MYC-driven genetically engineered mouse models (GEMM) has hampered the ability to better understand mechanisms of neuroblastoma oncogenesis and to develop effective therapies. In this study, we showed that conditional c-MYC induction via Cre recombinase driven by a tyrosine hydroxylase promoter led to a preponderance of PDX1+ somatostatinoma, a type of pancreatic neuroendocrine tumor. However, c-MYC activation via an improved Cre recombinase driven by a dopamine β-hydroxylase promoter resulted in neuroblastoma development. The c-MYC murine neuroblastoma tumors recapitulated the pathologic and genetic features of human neuroblastoma and responded to anti-GD2 immunotherapy and difluoromethylornithine, an FDA-approved inhibitor targeting the MYC transcriptional target ODC1. Thus, c-MYC overexpression results in different but related tumor types depending on the targeted cell. The GEMMs represent valuable tools for testing immunotherapies and targeted therapies for these diseases. Significance: The development of c-MYC-driven genetically engineered neuroblastoma and somatostatinoma mouse models provides useful tools for understanding the tumor cell origin and investigating treatment strategies.

Biomarkers to Inform Prognosis and Treatment for Unresectable or Metastatic GEP-NENs

Importance

Evidence-based treatment decisions for advanced gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) require individualized patient-centered decision-making that accounts for patient and cancer characteristics.

Objective

To create an accessible guidance document to educate clinicians and patients on biomarkers informing prognosis and treatment in unresectable or metastatic GEP-NENs.

Methods

A multidisciplinary panel in-person workshop was convened to define methods. English language articles published from January 2016 to January 2023 in PubMed (MEDLINE) and relevant conference abstracts were reviewed to investigate prognostic and treatment-informing features in unresectable or metastatic GEP-NENs. Data from included studies were used to form evidence-based recommendations. Quality of evidence and strength of recommendations were determined using the Grading of Recommendations, Assessment, Development and Evaluations framework. Consensus was reached via electronic survey following a modified Delphi method.

Findings

A total of 131 publications were identified, including 8 systematic reviews and meta-analyses, 6 randomized clinical trials, 29 prospective studies, and 88 retrospective cohort studies. After 2 rounds of surveys, 24 recommendations and 5 good clinical practice statements were developed, with full consensus among panelists. Recommendations focused on tumor and functional imaging characteristics, blood-based biomarkers, and carcinoid heart disease. A single strong recommendation was made for symptomatic carcinoid syndrome informing treatment in midgut neuroendocrine tumors. Conditional recommendations were made to use grade, morphology, primary site, and urinary 5-hydroxyindoleacetic levels to inform treatment. The guidance document was endorsed by the Commonwealth Neuroendocrine Tumour Collaboration and the North American Neuroendocrine Tumor Society.

Conclusions and Relevance

The study results suggest that select factors have sufficient evidence to inform care in GEP-NENs, but the evidence for most biomarkers is weak. This article may help guide management and identify gaps for future research to advance personalized medicine and improve outcomes for patients with GEP-NENs.

The Integrated Stress Response in Pancreatic Development, Tissue Homeostasis, and Cancer

Present in all eukaryotic cells, the integrated stress response (ISR) is a highly coordinated signaling network that controls cellular behavior, metabolism, and survival in response to diverse stresses. The ISR is initiated when any 1 of 4 stress-sensing kinases (protein kinase R-like endoplasmic reticulum kinase [PERK], general control non-derepressible 2 [GCN2], double-stranded RNA-dependent protein kinase [PKR], heme-regulated eukaryotic translation initiation factor 2α kinase [HRI]) becomes activated to phosphorylate the protein translation initiation factor eukaryotic translation initiation factor 2α (eIF2α), shifting gene expression toward a comprehensive rewiring of cellular machinery to promote adaptation. Although the ISR has been shown to play an important role in the homeostasis of multiple tissues, evidence suggests that it is particularly crucial for the development and ongoing health of the pancreas. Among the most synthetically dynamic tissues in the body, the exocrine and endocrine pancreas relies heavily on the ISR to rapidly adjust cell function to meet the metabolic demands of the organism. The hardwiring of the ISR into normal pancreatic functions and adaptation to stress may explain why it is a commonly used pro-oncogenic and therapy-resistance mechanism in pancreatic ductal adenocarcinoma and pancreatic neuroendocrine tumors. Here, we review what is known about the key roles that the ISR plays in the development, homeostasis, and neoplasia of the pancreas.

Molecular Basis of Pancreatic Neuroendocrine Tumors

Pancreatic neuroendocrine tumors (NETs) are rare well-differentiated neoplasms with limited therapeutic options and unknown cells of origin. The current classification of pancreatic neuroendocrine tumors is based on proliferative grading, and guides therapeutic strategies, however, tumors within grades exhibit profound heterogeneity in clinical manifestation and outcome. Manifold studies have highlighted intra-patient differences in tumors at the genetic and transcriptomic levels. Molecular classification might become an alternative or complementary basis for treatment decisions and reflect tumor biology, actionable cellular processes. Here, we provide a comprehensive review of genomic, transcriptomic, proteomic and epigenomic studies of pancreatic NETs to elucidate patterns shared between proposed subtypes that could form a foundation for new classification. We denote four NET subtypes with distinct molecular features, which were consistently reproduced using various omics technologies.

Proteogenomic characterization of pancreatic neuroendocrine tumors uncovers hypoxia and immune signatures in clinically aggressive subtypes

Pancreatic neuroendocrine tumors (PanNETs) represent well-differentiated endocrine neoplasms with variable clinical outcomes. Predicting patient outcomes using the current tumor grading system is challenging. In addition, traditional systemic treatment options for PanNETs, such as somatostatin analogs or cytotoxic chemotherapies, are very limited. To address these issues, we characterized PanNETs using integrated proteogenomics and identified four subtypes. Two proteomic subtypes showed high recurrence rates, suggesting clinical aggressiveness that was missed by current classification. Hypoxia and inflammatory pathways were significantly enriched in the clinically aggressive subtypes. Detailed analyses revealed metabolic adaptation via glycolysis upregulation and oxidative phosphorylation downregulation under hypoxic conditions. Inflammatory signature analysis revealed that immunosuppressive molecules were enriched in immune hot tumors and might be immunotherapy targets. In this study, we characterized clinically aggressive proteomic subtypes of well-differentiated PanNETs and identified candidate therapeutic targets.

Development of automated proteomic workflows utilizing silicon-based coupling agents

Automated methods for enzyme immobilization via 4-triethoxysilylbutyraldehyde (TESB) derived silicone-based coupling agents were developed. TESB and its oxidized derivative, 4-triethoxysilylbutanoic acid (TESBA), were determined to be the most effective. The resulting immobilized enzyme particles (IEPs) displayed robustness, rapid digestion, and immobilization efficiency of 51 ± 8%. Furthermore, we automated the IEP procedure, allowing for multiple enzymes, and/or coupling agents to be fabricated at once, in a fraction of the time via an Agilent Bravo. The automated trypsin TESB and TESBA IEPs were shown to rival a classical in-gel digestion method. Moreover, pepsin IEPs favored cleavage at leucine (>50%) over aromatic and methionine residues. The IEP method was then adapted for an in-situ immobilized enzyme microreactor (IMER) fabrication. We determined that TESBA could functionalize the silica capillary's inner wall while simultaneously acting as an enzyme coupler. The IMER digestion of bovine serum albumin (BSA), mirroring IEP digestion conditions, yielded a 33-40% primary sequence coverage per LC-MS/MS analysis in as little as 15 min. Overall, our findings underscore the potential of both IEP and IMER methods, paving the way for automated analysis and a reduction in enzyme waste through reuse, thereby contributing to a more cost-effective and timely study of the proteome. SIGNIFICANCE: This research introduces 4-triethoxysilylbutyraldehyde (TESB) and its derivatives as silicon-based enzyme coupling agents and an automated liquid handling method for bottom-up proteomics (BUP) while streamlining sample preparation for high-throughput processing. Additionally, immobilized enzyme particle (IEP) fabrication and digestion within the 96-well plate allows for flexibility in protocol where different enzyme-coupler combinations can be employed simultaneously. By enabling the digestion of entire microplates and reducing manual labor, the proposed method enhances reproducibility and offers a more efficient alternative to classical in-gel techniques. Furthermore, pepsin IEPs were noted to favor cleavage at leucine residues which represents an interesting finding when compared to the literature that warrants further study. The capability of immobilized enzyme microreactors (IMER) for rapid digestion (in as little as 15 min) demonstrated the system's efficiency and potential for rapid proteomic analysis. This advancement in BUP not only improves efficiency, but also opens avenues for a fully automated, mass spectrometry-integrated proteomics workflow, promising to expedite research and discoveries in complex biological studies.

MUC1-C Dependence for the Progression of Pancreatic Neuroendocrine Tumors Identifies a Druggable Target for the Treatment of This Rare Cancer

Patients with pancreatic neuroendocrine tumors (pNETs) have limited access to effective targeted agents and invariably succumb to progressive disease. MUC1-C is a druggable oncogenic protein linked to driving pan-cancers. There is no known involvement of MUC1-C in pNET progression. The present work was performed to determine if MUC1-C represents a potential target for advancing pNET treatment. We demonstrate that the MUC1 gene is upregulated in primary pNETs that progress with metastatic disease. In pNET cells, MUC1-C drives E2F- and MYC-signaling pathways necessary for survival. Targeting MUC1-C genetically and pharmacologically also inhibits self-renewal capacity and tumorigenicity. Studies of primary pNET tissues further demonstrate that MUC1-C expression is associated with (i) an advanced NET grade and pathological stage, (ii) metastatic disease, and (iii) decreased disease-free survival. These findings demonstrate that MUC1-C is necessary for pNET progression and is a novel target for treating these rare cancers with anti-MUC1-C agents under clinical development.

Well-differentiated G1 and G2 pancreatic neuroendocrine tumors: a meta-analysis of published expanded DNA sequencing data

Introduction

Well-differentiated pancreatic neuroendocrine tumors (PNETs) can be non-functional or functional, e.g. insulinoma and glucagonoma. The majority of PNETs are sporadic, but PNETs also occur in hereditary syndromes, primarily multiple endocrine neoplasia type 1 (MEN1). The Knudson hypothesis stated a second, somatic hit in MEN1 as the cause of PNETs of MEN1 syndrome. In the recent years, reports on genetic somatic events in both sporadic and hereditary PNETs have emerged, providing a basis for a more detailed molecular understanding of the pathophysiology. In this systematic review and meta-analysis, we made a collation and statistical analysis of aggregated frequent genetic alterations and potential driver events in human grade G1/G2 PNETs.

Methods

A systematic search was performed in concordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) reporting guidelines of 2020. A search in Pubmed for published studies using whole exome, whole genome, or targeted gene panel (+400 genes) sequencing of human G1/G2 PNETs was conducted at the 25th of September 2023. Fourteen datasets from published studies were included with data on 221 patients and 225 G1/G2 PNETs, which were divided into sporadic tumors, and hereditary tumors with pre-disposing germline variants, and tumors with unknown germline status. Further, non-functioning and functioning PNETs were distinguished into two groups for pathway evaluation. The collated genetical analyses were conducted using the 'maftools' R-package.

Results

Sporadic PNETs accounted 72.0% (162/225), hereditary PNETs 13.3% (30/225), unknown germline status 14.7% (33/225). The most frequently altered gene was MEN1, with somatic variants and copy number variations in overall 42% (95/225); hereditary PNETs (germline variations in MEN1, VHL, CHEK2, BRCA2, PTEN, CDKN1B, and/or MUTYH) 57% (16/30); sporadic PNETs 36% (58/162); unknown germline status 64% (21/33). The MEN1 point mutations/indels were distributed throughout MEN1. Overall, DAXX (16%, 37/225) and ATRX-variants (12%, 27/225) were also abundant with missense mutations clustered in mutational hotspots associated with histone binding, and translocase activity, respectively. DAXX mutations occurred more frequently in PNETs with MEN1 mutations, p<0.05. While functioning PNETs shared few variated genes, non-functioning PNETs had more recurrent variations in genes associated with the Phosphoinositide 3-kinase, Wnt, NOTCH, and Receptor Tyrosine Kinase-Ras signaling onco-pathways.

Discussion

The somatic genetic alterations in G1/G2 PNETs are diverse, but with distinct differences between sporadic vs. hereditary, and functional vs. non-functional PNETs. Increased understanding of the genetic alterations may lead to identification of more drivers and driver hotspots in the tumorigenesis in well-differentiated PNETs, potentially giving a basis for the identification of new drug targets. (Funded by Novo Nordisk Foundation, grant number NNF19OC0057915).

Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms

INTRODUCTION

Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures.

AREAS COVERED

This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023.

EXPERT OPINION

A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.

Conditional c-MYC activation in catecholaminergic cells drives distinct neuroendocrine tumors: neuroblastoma vs somatostatinoma

The MYC proto-oncogenes (c-MYC, MYCN , MYCL ) are among the most deregulated oncogenic drivers in human malignancies including high-risk neuroblastoma, 50% of which are MYCN -amplified. Genetically engineered mouse models (GEMMs) based on the MYCN transgene have greatly expanded the understanding of neuroblastoma biology and are powerful tools for testing new therapies. However, a lack of c-MYC-driven GEMMs has hampered the ability to better understand mechanisms of neuroblastoma oncogenesis and therapy development given that c-MYC is also an important driver of many high-risk neuroblastomas. In this study, we report two transgenic murine neuroendocrine models driven by conditional c-MYC induction in tyrosine hydroxylase (Th) and dopamine β-hydroxylase (Dbh)-expressing cells. c-MYC induction in Th-expressing cells leads to a preponderance of Pdx1 + somatostatinomas, a type of pancreatic neuroendocrine tumor (PNET), resembling human somatostatinoma with highly expressed gene signatures of δ cells and potassium channels. In contrast, c-MYC induction in Dbh-expressing cells leads to onset of neuroblastomas, showing a better transforming capacity than MYCN in a comparable C57BL/6 genetic background. The c-MYC murine neuroblastoma tumors recapitulate the pathologic and genetic features of human neuroblastoma, express GD2, and respond to anti-GD2 immunotherapy. This model also responds to DFMO, an FDA-approved inhibitor targeting ODC1, which is a known MYC transcriptional target. Thus, establishing c-MYC-overexpressing GEMMs resulted in different but related tumor types depending on the targeted cell and provide useful tools for testing immunotherapies and targeted therapies for these diseases.

Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.

Metabolism of pancreatic neuroendocrine tumors: what can omics tell us?

Introduction

Reprogramming of cellular metabolism is now a hallmark of tumorigenesis. In recent years, research on pancreatic neuroendocrine tumors (pNETs) has focused on genetic and epigenetic modifications and related signaling pathways, but few studies have been devoted to characterizing the metabolic profile of these tumors. In this review, we thoroughly investigate the metabolic pathways in pNETs by analyzing the transcriptomic and metabolomic data available in the literature.

Methodology

We retrieved and downloaded gene expression profiles from all publicly available gene set enrichments (GSE43797, GSE73338, and GSE117851) to compare the differences in expressed genes based on both the stage and MEN1 mutational status. In addition, we conducted a systematic review of metabolomic data in NETs.

Results

By combining transcriptomic and metabolomic approaches, we have identified a distinctive metabolism in pNETs compared with controls without pNETs. Our analysis showed dysregulations in the one-carbon, glutathione, and polyamine metabolisms, fatty acid biosynthesis, and branched-chain amino acid catabolism, which supply the tricarboxylic acid cycle. These targets are implicated in pNET cell proliferation and metastasis and could also have a prognostic impact. When analyzing the profiles of patients with or without metastasis, or with or without MEN1 mutation, we observed only a few differences due to the scarcity of published clinical data in the existing research. Consequently, further studies are now necessary to validate our data and investigate these potential targets as biomarkers or therapeutic solutions, with a specific focus on pNETs.

Whole-exome sequencing of calcitonin-producing pancreatic neuroendocrine neoplasms indicates a unique molecular signature

INTRODUCTION

Calcitonin-producing pancreatic neuroendocrine neoplasms (CT-pNENs) are an extremely rare clinical entity, with approximately 60 cases reported worldwide. While CT-pNENs can mimic the clinical and diagnostic features of medullary thyroid carcinoma, their molecular profile is poorly understood.

METHODS

Whole-exome sequencing (WES) was performed on tumor and corresponding serum samples of five patients with increased calcitonin serum levels and histologically validated calcitonin-positive CT-pNENs. cBioPortal analysis and DAVID gene enrichment analysis were performed to identify dysregulated candidate genes compared to control databases. Immunohistochemistry was used to detect the protein expression of MUC4 and MUC16 in CT-pNEN specimens.

RESULTS

Mutated genes known in the literature in pNENs like MEN1 (35% of cases), ATRX (18-20% of cases) and PIK3CA (1.4% of cases) were identified in cases of CT-pNENs. New somatic SNVs in ATP4A, HES4, and CAV3 have not been described in CT- pNENs, yet. Pathogenic germline mutations in FGFR4 and DPYD were found in three of five cases. Mutations of CALCA (calcitonin) and the corresponding receptor CALCAR were found in all five tumor samples, but none of them resulted in protein sequelae or clinical relevance. All five tumor cases showed single nucleotide variations (SNVs) in MUC4, and four cases showed SNVs in MUC16, both of which were membrane-bound mucins. Immunohistochemistry showed protein expression of MUC4 in two cases and MUC16 in one case, and the liver metastasis of a third case was double positive for MUC4 and MUC16. The homologous recombination deficiency (HRD) score of all tumors was low.

DISCUSSION

CT-pNENs have a unique molecular signature compared to other pNEN subtypes, specifically involving the FGFR4, DPYD, MUC4, MUC16 and the KRT family genes. However, a major limitation of our study was the relative small number of only five cases. Therefore, our WES data should be interpreted with caution and the mutation landscape in CT-pNENs needs to be verified by a larger number of patients. Further research is needed to explain differences in pathogenesis compared with other pNENs. In particular, multi-omics data such as RNASeq, methylation and whole genome sequencing could be informative.

A high-throughput pipeline for DNA/RNA/small RNA purification from tissue samples for sequencing

High-throughput total nucleic acid (TNA) purification methods based on solid-phase reversible immobilization (SPRI) beads produce TNA suitable for both genomic and transcriptomic applications. Even so, small RNA species, including miRNA, bind weakly to SPRI beads under standard TNA purification conditions, necessitating a separate workflow using column-based methods that are difficult to automate. Here, an SPRI-based high-throughput TNA purification protocol that recovers DNA, RNA and small RNA, called GSC-modified RLT+ Aline bead-based protocol (GRAB-ALL), which incorporates modifications to enhance small RNA recovery is presented. GRAB-ALL was benchmarked against existing nucleic acid purification workflows and GRAB-ALL efficiently purifies TNA, including small RNA, for next-generation sequencing applications in a plate-based format suitable for automated high-throughput sample preparation.

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients

BACKGROUND

Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM).

METHODS

As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell-cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype.

FINDINGS

Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset.

INTERPRETATION

By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.

An Insight on Functioning Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (PanNENs) are rare neoplasms arising from islets of the Langerhans in the pancreas. They can be divided into two groups, based on peptide hormone secretion, functioning and nonfunctioning PanNENs. The first group is characterized by different secreted peptides causing specific syndromes and is further classified into subgroups: insulinoma, gastrinoma, glucagonoma, somatostatinoma, VIPoma and tumors producing serotonin and adrenocorticotrophic hormone. Conversely, the second group does not release peptides and is usually associated with a worse prognosis. Today, although the efforts to improve the therapeutic approaches, surgery remains the only curative treatment for patients with PanNENs. The development of high-throughput techniques has increased the molecular knowledge of PanNENs, thereby allowing us to understand better the molecular biology and potential therapeutic vulnerabilities of PanNENs. Although enormous advancements in therapeutic and molecular aspects of PanNENs have been achieved, there is poor knowledge about each subgroup of functioning PanNENs.Therefore, we believe that combining high-throughput platforms with new diagnostic tools will allow for the efficient characterization of the main differences among the subgroups of functioning PanNENs. In this narrative review, we summarize the current landscape regarding diagnosis, molecular profiling and treatment, and we discuss the future perspectives of functioning PanNENs.

The Use of PDX1 DNA Methylation to Distinguish Two Subtypes of Pancreatic Neuroendocrine Neoplasms with Different Prognoses

Pancreatic neuroendocrine neoplasms (pNENs) account for approximately 5% of all pancreatic tumors; thus, they constitute the second most common tumor type in the pancreas [...].

Risk Stratification of Pancreatic Neuroendocrine Neoplasms Based on Clinical, Pathological, and Molecular Characteristics

Pancreatic neuroendocrine neoplasms consist of heterogeneous diseases. Depending on the novel features detected by various modern technologies, their classification and related prognosis predictions continue to change and develop. The role of traditional clinicopathological prognostic factors, including classification systems, is also being refined, and several attempts have been made to predict a more accurate prognosis through novel serum biomarkers, genetic factors, and epigenetic factors that have been identified through various state-of-the-art molecular techniques with multiomics sequencing. In this review article, the latest research results including the traditional approach to prognostic factors and recent advanced strategies for risk stratification of pancreatic neuroendocrine neoplasms based on clinical, pathological, and molecular characteristics are summarized. Predicting prognosis through multi-factorial assessments seems to be more efficacious, and prognostic factors through noninvasive methods are expected to develop further advances in liquid biopsy in the future.

Insights into Epigenetic Changes Related to Genetic Variants and Cells-of-Origin of Pancreatic Neuroendocrine Tumors: An Algorithm for Practical Workup

Simple Summary

Pancreatic neuroendocrine tumors are composite entities due to their heterogeneity illustrated in clinical behavior, mutational pattern, and site of origin. Pancreatic neuroendocrine tumors display a low mutation burden with frequently epigenetic alterations, such as histone modifications, chromatin remodeling, or DNA methylation status. Using the epigenomic data of the pancreatic neuroendocrine tumors converged to the identification of molecularly distinct subgroups. Furthermore, epigenetic signatures could be used as biomarkers due to their link to cell lineages and genetic driver mutations. We integrated the current knowledge on genetic and epigenetic alterations involved in endocrine lineage associated with these neoplasms to present a pathway-based overview. In this review, we suggest a simplified algorithm on how to manage pancreatic neuroendocrine tumors from a practical perspective based on pathologist ’analysis.

Abstract

Current knowledge on the molecular landscape of pancreatic neuroendocrine tumors (PanNETs) has advanced significantly. Still, the cellular origin of PanNETs is uncertain and the associated mechanisms remain largely unknown. DAXX/ATRX and MEN1 are the three most frequently altered genes that drive PanNETs. They are recognized as a link between genetics and epigenetics. Moreover, the acknowledged impact on DNA methylation by somatic mutations in MEN1 is a valid hallmark of epigenetic mechanism. DAXX/ATRX and MEN1 can be studied at the immunohistochemical level as a reliable surrogate for sequencing. DAXX/ATRX mutations promote alternative lengthening of telomeres (ALT) activation, determined by specific fluorescence in situ hybridization (FISH) analysis. ALT phenotype is considered a significant predictor of worse prognosis and a marker of pancreatic origin. Additionally, ARX/PDX1 expression is linked to important epigenomic alterations and can be used as lineage associated immunohistochemical marker. Herein, ARX/PDX1 association with DAXX/ATRX/MEN1 and ALT can be studied through pathological assessment, as these biomarkers may provide important clues to the mechanism underlying disease pathogenesis. In this review, we present an overview of a new approach to tumor stratification based on genetic and epigenetic characteristics as well as cellular origin, with prognostic consequences.

The Landscape and Clinical Application of the Tumor Microenvironment in Gastroenteropancreatic Neuroendocrine Neoplasms

Simple Summary

The tumor microenvironment (TME) plays a role in promoting tumor progression. Elucidating the relationship between the TME and tumor cells will benefit current therapies. Therefore, this review summarizes the most recent relationship between the TME and tumor characteristics, discusses the differences in the TME at various sites along the digestive tract, and compares the TMEs of neuroendocrine tumors and neuroendocrine carcinomas. Microbial ecological changes in the TME were reviewed. The clinical application of the TME was summarized from bench to bedside. The TME can be used as a tumor drug target for diagnostic value, prognosis prediction, and efficacy evaluation, further revealing the potential of immune checkpoints combined with antiangiogenic drugs. The clinical application prospects of adoptive cell therapy and oncolytic viruses were described. The potential therapeutic approaches and strategies for gastrointestinal neuroendocrine neoplasms are considered.

Abstract

Gastroenteropancreatic neuroendocrine neoplasms feature high heterogeneity. Neuroendocrine tumor cells are closely associated with the tumor microenvironment. Tumor-infiltrating immune cells are mutually educated by each other and by tumor cells. Immune cells have dual protumorigenic and antitumorigenic effects. The immune environment is conducive to the invasion and metastasis of the tumor; in turn, tumor cells can change the immune environment. These cells also form cytokines, immune checkpoint systems, and tertiary lymphoid structures to participate in the process of mutual adaptation. Additionally, the fibroblasts, vascular structure, and microbiota exhibit interactions with tumor cells. From bench to bedside, clinical practice related to the tumor microenvironment is also regarded as promising. Targeting immune components and angiogenic regulatory molecules has been shown to be effective. The clinical efficacy of immune checkpoint inhibitors, adoptive cell therapy, and oncolytic viruses remains to be further discussed in clinical trials. Moreover, combination therapy is feasible for advanced high-grade tumors. The regulation of the tumor microenvironment based on multiple omics results can suggest innovative therapeutic strategies to prevent tumors from succeeding in immune escape and to support antitumoral effects.

Protocol for analysis of RNA-sequencing and proteome profiling data for subgroup identification and comparison

RNA-sequencing and quantitative proteomic profiling simultaneously measure thousands of molecules and provide opportunities to decipher the transcriptomic and proteomic landscapes of cohort specimens for basic and health research. We present a protocol for the analysis of paired transcriptome and proteome data to identify and compare molecular subgroups among cohort specimens. We demonstrate a streamlined analysis workflow, applicable for both transcriptome and proteome data, which allows the comparison of two data types for RNA-protein variations and for derivation of biological implications.

For complete details on the use and execution of this protocol, please refer to Yang et al. (2021).

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Identify and compare subgroups using transcriptome and/or proteome data

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Streamlined workflow from data preprocessing to cluster and differential analysis

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Examine mRNA-protein correlation from paired transcriptome and proteome data

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Discover subgroups and identify distinguishing features from any groups of interest

Redox Metabolism-Associated Molecular Classification of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Redox metabolism has been recognized as the hallmark of cancer. But the concrete role of redox-related genes in patient stratification of ccRCC remains unknown. Herein, we aimed to characterize the molecular features of ccRCC based on the redox gene expression profiles from The Cancer Genome Atlas. Differentially expressed redox genes (DERGs) and vital genes in metabolism regulation were identified and analyzed in the ccRCC. Consensus clustering was performed to divide patients into three clusters (C1, C2, and C3) based on 139 redox genes with median FPKM value > 1. We analyzed the correlation of clusters with clinicopathological characteristics, immune infiltration, gene mutation, and response to immunotherapy. Subclass C1 was metabolic active with moderate prognosis and associated with glucose, lipid, and protein metabolism. C2 had intermediate metabolic activity with worse prognosis and correlated with more tumor mutation burden, neoantigen, and aneuploidy, indicating possible drug sensitivities towards immune checkpoint inhibitors. Metabolic exhausted subtype C3 showed high cytolytic activity score, suggesting better prognosis than C1 and C2. Moreover, the qRT-PCR was performed to verify the expression of downregulated DERGs including ALDH6A1, ALDH1L1, GLRX5, ALDH1A3, and GSTM3, and upregulated SHMT1 in ccRCC. Overall, our study provides an insight into the characteristics of molecular classification of ccRCC patients based on redox genes, thereby deepening the understanding of heterogeneity of ccRCC and allowing prediction of prognosis of ccRCC patients.