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The two major molecular subtypes of pancreatic adenocarcinoma reportedly have differential response to FOLFIRINOX-based therapy. To promote rapid assignment of basal versus classical subtypes, an array-based single-sample classifier assay was developed and applied to 74 formalin-fixed, paraffin-embedded biopsy or resection specimens of known subtype based on transcriptomics. The Purity Independent Subtyping of Tumors (PurIST) algorithm assigns subtype based on relative expression of 16 RNAs counted by RNA sequencing (RNAseq) versus more practical array-based NanoString nCounter Elements XT technology. Subtype calls were largely concordant between RNAseq and array methods (72/74, 97% agreement). Compared with the lengthy RNAseq protocol, the array-based assay takes just 3 working days to analyze, permitting rapid reporting of tumor subtype. In conclusion, the PurIST pancreatic cancer classifier has robust performance to classify pancreatic adenocarcinoma into basal versus classical subtypes. Clinical validation studies are underway to evaluate outcome in patients whose standard-of-care chemotherapy regimen is selected on the basis of rapid subtype assignment (NCT04683315)., Competing Interests: Disclosure Statement N.U.R. and J.J.Y. are patent holders on Purity Independent Subtyping of Tumors (PurIST) technology for subtyping pancreatic ductal adenocarcinoma (US17/601,002). GeneCentric Therapeutics, Inc., had no involvement in the current study but acquired an exclusive license from the University of North Carolina at Chapel Hill related to PurIST technology., (Copyright © 2024 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

The classification and management of neuroendocrine neoplasms (NENs) arising in the tubular gastrointestinal (GI) tract and pancreas have significantly evolved over the last decades. In the latest WHO classification published in 2022, NENs are separated regardless of their primary origin into two main groups: well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). The substantial changes in the grading system changed the definition of grade 3 to include high-grade well-differentiated NETs (G3-NETs), and poorly differentiated NECs (-NECs). Although these two subgroups are considered high grades with Ki-67 >20%, they have different genomic profiles, prognosis, and clinical behavior, which critically influence their treatment strategies. The available clinical trial data to guide therapy of these high-grade subgroups are extremely limited, which impacts their management. In this review, we will summarize the current advances in the multidisciplinary approach for the management of high-grade gastroenteropancreatic NENs (GEP-NENs) including G3-NETs and NECs.

Abstract: Recent advances in molecular pathology and an improved understanding of the etiology of neuroendocrine neoplasms (NENs) have given rise to an updated World Health Organization classification. Since gastroenteropancreatic NENs (GEP-NENs) are the most common forms of NENs and their incidence has been increasing constantly, they will be the focus of our attention. Here, we review the findings at the foundation of the new classification system, discuss how it impacts imaging research and radiological practice, and illustrate typical and atypical imaging and pathological findings. Gastroenteropancreatic NENs have a highly variable clinical course, which existing classification schemes based on proliferation rate were unable to fully capture. While well- and poorly differentiated NENs both express neuroendocrine markers, they are fundamentally different diseases, which may show similar proliferation rates. Genetic alterations specific to well-differentiated neuroendocrine tumors graded 1 to 3 and poorly differentiated neuroendocrine cancers of small cell and large-cell subtype have been identified. The new tumor classification places new demands and creates opportunities for radiologists to continue providing the clinically most relevant report and on researchers to design projects, which continue to be clinically applicable., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures., Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

In the last two decades, the increasing availability of technologies for molecular analyses has allowed an insight in the genomic alterations of neuroendocrine neoplasms (NEN) of the gastrointestinal tract and pancreas. This knowledge has confirmed, supported, and informed the pathological classification of NEN, clarifying the differences between neuroendocrine carcinomas (NEC) and neuroendocrine tumors (NET) and helping to define the G3 NET category. At the same time, the identification genomic alterations, in terms of gene mutation, structural abnormalities, and epigenetic changes differentially involved in the pathogenesis of NEC and NET has identified potential molecular targets for precision therapy. This review critically recapitulates the available molecular features of digestive NEC and NET, highlighting their correlates with pathological aspects and clinical characteristics of these neoplasms and revising their role as predictive biomarkers for targeted therapy. In this context, the feasibility and applicability of a molecular classification of gastrointestinal and pancreatic NEN will be explored., (© 2024. The Author(s).)

Background & Aims: Cytologic and histopathologic diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice, whereas it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms., Methods: DNA methylation data were obtained for 301 primary tumors spanning 6 primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and extreme gradient boosting machine learning models were trained to distinguish between tumor types. Methylation data of 29 nonpancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin., Results: After benchmarking 3 state-of-the-art machine learning models, the random forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold improved the random forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of nonpancreatic neoplasms achieved an area under the curve of >0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample., Conclusions: Pancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: The anatomic location of the pancreas can result in involvement of major vasculature, which may act as a contraindication to resection. Several classification systems have been developed. We sought to discover the variations in the HPB community determining PDAC resectability., Methods: The multiple-choice survey was distributed to all full members of the IHPBA. Questions were asked regarding demographics and clinical scenarios regarding tumor resectability., Results: 164 responses were submitted. Most of the respondents were male and had been in practice for over 10 years. The median age range was 40-50 years old. Most practiced in either Asia (n = 57,35.9%), North America (n = 52,32.7%), or Europe (n = 32,20.1%). Classification systems used to determine resectability were: NCCN (n = 42,26.3%), JPS (n = 35,21.9%), International consensus (n = 33,20.6%), AHPBA/SSO (n = 23,14.4%), Alliance (n = 3,1.9%), and other/no-classification (n = 23,14.5%). There was significant variation in the frequency of the most common answer within the scenarios (84.7%-33.5%). Participant concordance with their stated classification system found a median rate of 62.5%. Participant decision of tumor resectability was not dependent on their adopted classification system., Conclusion: When classifying PDAC resectability, there is significant variation between surgeons as to how they would classify a specific tumour, independent of the classification system they use. In addition, surgeons do not show high concordance with the definitions within that classification system., (Copyright © 2024 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Background: Pancreatic Ductal Adenocarcinoma (PDAC) patients exhibit varied responses to multimodal therapy. RNA gene sequencing has unravelled distinct tumour biology subtypes, forming the focus of this review exploring its impact on survival outcomes., Methods: A systematic search across PubMed, Medline, Embase, and CINAHL databases targeted studies assessing long-term overall and disease-free survival in PDAC patients with molecular subtyping., Results: Fifteen studies including 2731 patients were identified. Molecular subtyping was performed by RNA sequencing and Immunohistochemistry in 14 studies and by Mass Spectrometry in 1 study. Two main tumour subtypes were identified (classical and basal-like or squamous) with basal like associated with poorer outcomes. Further subtypes were identified in individual studies. Superior survival was seen with classical subtype in all other analyses that compared the classical and basal subtypes. High risk stromal subtypes were identified on further analysis of the stroma and were associated with a worse survival independent of the tumour subtype., Conclusion: Molecular subtyping of PDAC specimens can identify patients with high-risk tumour biology and poor survival outcomes. Routine subtyping is limited by the cost of RNA sequencing and the volume of raw data generated which has made its translation into routine clinical practice difficult., (Copyright © 2024 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Pancreatic carcinoma is a relatively common malignant tumor with increasing incidence and mortality. The tumor is usually diagnosed at an advanced stage and generally has a poor prognosis, with only 5% of patients surviving 5 years from the time of diagnosis. The stage of the disease at the time of diagnosis is a crucial factor for the prognosis; 25% of patients with localized tumors survive 3 years from diagnosis, compared to only 1% of those with generalized tumors. Radical surgical removal of the tumor (partial or total pancreatectomy) is a key factor in improving survival. Therefore, the topic is highly relevant to surgeons. Statistics on pancreatic carcinoma mainly focus on ductal adenocarcinoma, which is the most common and least favorable malignant tumor of the pancreas. This review focuses on ductal adenocarcinoma, its variants, and precancerous lesions. The article summarizes information from the latest WHO classification of 2019, which was released 11 years after the previous edition. Compared to the previous version, this new WHO classification introduced rather minor changes in the field of ductal adenocarcinoma. The delineation of rare variants of ductal adenocarcinoma is justified based on genetic and morphological similarities and clinical relevance, as individual subtypes significantly differ in prognosis. The article also includes a description of macroscopic and microscopic precursors of ductal adenocarcinoma and their definitions. Genetic and immunohistochemical differential diagnostic aspects are briefly discussed, as these are more relevant to pathologists than to surgeons.

Purpose: Risk factors for sporadic GEP-NENs are still not well defined. To identify the main clinical risk factors represents the aim of this study performed by three Italian referral centers for NENs., Methods: We performed a retrospective case-control study including 148 consecutive sporadic GEP-NENs and 210 age- and sex-matched controls. We collected data on clinical features, cancer family history and other potential risk factors., Results: Mean age was 58.3 ± 15.8 years; 50% males, primary site was pancreas (50.7%), followed by ileum (22.3%). The 62.8% and 29.1% of cases were G1 and G2, respectively; the 40% had locally advanced or metastatic disease at diagnosis. Independent risk factors for GEP-NENs were: family history of non-neuroendocrine GEP cancer (OR 2.16, 95% CI 1.31-3.55, p = 0.003), type 2 diabetes mellitus (T2DM) (OR 2.5, 95% CI 1.39-4.51, p = 0.002) and obesity (OR 1.88, 95% CI 1.18-2.99, p = 0.007). In the T2DM subjects, metformin use was a protective factor (OR 0.28, 95% CI 0.08-0.93, p = 0.049). T2DM was also associated with a more advanced (OR 2.39, 95% CI 1.05-5.46, p = 0.035) and progressive disease (OR 2.47, 95% CI 1.08-5.34, p = 0.03). Stratifying cases by primary site, independent risk factors for pancreatic NENs were T2DM (OR 2.57, 95% CI 1.28-5.15, p = 0.008) and obesity (OR 1.98, 95% CI 1.11-3.52, p = 0.020), while for intestinal NENs family history of non-neuroendocrine GEP cancer (OR 2.46, 95% CI 1.38-4.38, p = 0.003) and obesity (OR 1.90, 95% CI 1.08-3.33, p = 0.026)., Conclusion: This study reinforces a role for family history of non-neuroendocrine GEP cancer, T2DM and obesity as independent risk factors for GEP-NENs and suggests a role of metformin as a protective factor in T2DM subjects. If confirmed, these findings could have a significant impact on prevention strategies for GEP-NENs., (© 2021. Italian Society of Endocrinology (SIE).)

Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson's correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regression analysis was used to establish a risk model with 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) that was evaluated by Kaplan-Meier analysis, the areas under the curve (AUCs) of the receiver operating characteristics and the C-index. Additionally, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was an independent prognostic factor (p < 0.001). A nomogram and calibration plots were drawn to visualize the clinical features. The Wilcoxon signed-rank test and Pearson's correlation analysis further explored the irlncRNA signatures and immune cell infiltration, as well as the immunotherapy response.

Objectives: The aim of this study is to evaluate the computed tomography texture parameters in predicting grading., Methods: This study analyzed 68 nonfunctioning pancreatic neuroendocrine neoplasms (Pan-NENs). Clinical and radiological parameters were studied. Four model models were built, including clinical and standard radiologic parameters (model 1), first- and second-order computed tomography features (models 2 and 3), all parameters (model 4). The diagnostic accuracy was reported as area under the curve. A score was computed using the best model and validated to predict progression-free survival., Results: The size of tumors and heterogeneous enhancement were related to the risk of "non-G1" Pan-NENs (coefficients 0.471, P = 0.012, and 1.508, P = 0.027). Four second-order parameters were significantly related to the presence of "non-G1" Pan-NENs: the gray level co-occurrence matrix correlation (6.771; P = 0.011), gray level co-occurrence matrix contrast variance (0.349; P = 0.009), the neighborhood gray-level different matrix contrast (-63.129; P = 0.001), and the gray-level zone length matrix with the low gray-level zone emphasis (-0.151; P = 0.049). Model 4 was the best, with a higher area under the curve (0.912; P = 0.005). The score obtained predicted the progression-free survival., Conclusions: Computed tomography radiomics signature can be useful in preoperative workup., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments., Competing Interests: The authors have declared that no competing interests exist.

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.L is an employee of NVIDIA Corporation.

Intraductal papillary mucinous neoplasms (IPMN) are common and one of the main precursor lesions of pancreatic ductal adenocarcinoma (PDAC). PDAC derived from an IPMN is called intraductal papillary mucinous carcinoma (IPMC) and defines a subgroup of patients with ill-defined specificities. As compared to conventional PDAC, IPMCs have been associated to clinical particularities and favorable pathological features, as well as debated outcomes. However, IPMNs and IPMCs include distinct subtypes of precursor (gastric, pancreato-biliary, intestinal) and invasive (tubular, colloid) lesions, also associated to specific characteristics. Notably, consistent data have shown intestinal IPMNs and associated colloid carcinomas, defining the "intestinal pathway", to be associated with less aggressive features. Genomic specificities have also been uncovered, such as mutations of the GNAS gene, and recent data provide more insights into the mechanisms involved in IPMCs carcinogenesis. This review synthetizes available data on clinical-pathological features and outcomes associated with IPMCs and their subtypes. We also describe known genomic hallmarks of these lesions and summarize the latest data about molecular processes involved in IPMNs initiation and progression to IPMCs. Finally, potential implications for clinical practice and future research strategies are discussed.

In the initiation and progression of pancreatic cancer, DNA methylation plays a critical role. The present study attempts to explore specific prognosis subtypes based on DNA methylation data and develop an epigenetic signature to predict the overall survival (OS) of patients with pancreatic cancer.147 samples were included in the training cohort, whereas the validation cohort included 226 samples. The 298 OS-related methylation sites in the training cohort were selected for consensus clustering, and the authors identified three subtypes with a significant difference in prognosis. Cluster1 was associated with poor OS, low-grade disease and high lymph node involvement. In addition, we identified 33 specific methylation sites in Cluster1. Subsequently, we developed a robust qualitative signature consisting of 14 methylation sites to individually predict OS in the training cohort, and the predictive accuracy of this model was confirmed in the validation cohort. Functional enrichment analysis showed that the selected genes in the model were mainly enriched in known cancer-related pathways. Patients were divided into high- and low-risk groups by the model, and a significant difference in OS was observed between these groups. Classification based on the modeling of a specific DNA methylation site can reveal the heterogeneity of pancreatic cancer and provide guidance for clinicians in predicting the prognosis of pancreatic cancer and providing personalized treatment.

Purpose: Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts., Patients and Methods: Data from whole-exome sequencing (WES) of The Cancer Genome Atlas (TCGA) patients were used as an input for the artificial neural network (ANN) to predict the anatomical site, iClusters (cell-of-origin patterns) and molecular subtype classifications. The Ohio State University (OSU) and the International Cancer Genome Consortium (ICGC) patients with HPB cancer were included in external validation cohorts. TCGA, OSU and ICGC data were merged, and survival analyses were performed using both the 'classic' survival analysis and a machine learning algorithm (random survival forest)., Results: Although the ANN predicting the anatomical site of the tumour (i.e. cholangiocarcinoma, hepatocellular carcinoma of the liver, pancreatic ductal adenocarcinoma) demonstrated a low accuracy in TCGA test cohort, the ANNs predicting the iClusters (cell-of-origin patterns) and molecular subtype classifications demonstrated a good accuracy of 75% and 82% in TCGA test cohort, respectively. The random survival forest analysis and Cox' multivariable survival models demonstrated that models for HPB cancers that integrated clinical data with molecular classifications (iClusters, molecular subtypes) had an increased prognostic accuracy compared with standard staging systems., Conclusion: The analyses of genetic status (i.e. WES, gene panels) of patients with HPB cancers might predict the classifications proposed by TCGA project and help to select patients suitable to targeted therapies. The molecular classifications of HPB cancers when integrated with clinical information could improve the ability to predict the prognosis of patients with HPB cancer., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose: Although the same distal pancreatectomy (DP) is performed regardless of the location of left-sided pancreatic ductal adenocarcinoma (PDAC), the clinicopathological features may differ depending on the tumor location. The present study investigated the relationship between the tumor location and clinicopathological features in patients with left-sided PDAC., Methods: The records of 59 patients who underwent DP for PDAC were enrolled. The relationship between the tumor location and clinicopathological features was investigated. The tumor location was classified into three groups according to the 7th AJCC/UICC TNM classification: body (Pb), body and tail (Pbt), and tail (Pt)., Results: Tumors were located at the Pb in 26 patients, Pbt in 15, and Pt in 18. There was no metastasis to the lymph nodes around the common hepatic artery in Pt. The rate of peritoneal dissemination in the Pt was higher than that in the Pb (P = 0.034) or Pbt (P = 0.002). There were no significant differences in the overall survival among the three groups., Conclusion: There was no metastasis to the lymph nodes around the common hepatic artery, and peritoneal dissemination was the most common site of recurrence in Pt tumors.

Pancreatic neuroendocrine tumors (pNETs) are a rare group of cancers accounting for about 1-2% of all pancreatic neoplasms. About 10% of pNETs arise within endocrine tumor syndromes, such as Multiple Endocrine Neoplasia type 1 (MEN1). pNETs affect 30-80% of MEN1 patients, manifesting prevalently as multiple microadenomas. pNETs in patients with MEN1 are particularly difficult to treat due to differences in their growth potential, their multiplicity, the frequent requirement of extensive surgery, the high rate of post-operative recurrences, and the concomitant development of other tumors. MEN1 syndrome is caused by germinal heterozygote inactivating mutation of the MEN1 gene, encoding the menin tumor suppressor protein. MEN1-related pNETs develop following the complete loss of function of wild-type menin. Menin is a key regulator of endocrine cell plasticity and its loss in these cells is sufficient for tumor initiation. Somatic biallelic loss of wild-type menin in the neuroendocrine pancreas presumably alters the epigenetic control of gene expression, mediated by histone modifications and DNA hypermethylation, as a driver of MEN1-associated pNET tumorigenesis. In this light, epigenetic-based therapies aimed to correct the altered DNA methylation, and/or histone modifications might be a possible therapeutic strategy for MEN1 pNETs, for whom standard treatments fail.

Abstract: Pancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background/objectives: Various classifications of pancreatic ductal adenocarcinoma (PDAC) based on RNA profiling resulted in two main subtypes. Kalimuthu and coworkers proposed a morphology-based classification that concurred with these subtypes. Immune therapy approaches in PDAC were so far disappointing. Morphologic PDAC subtypes may differ regarding key immune-oncology pathways. We aimed to examine the reproducibility and prognostic value of Kalimuthu's morphologic classification, and to evaluate differences between subtypes regarding gene expression related to tumor biology and immune-oncology., Methods: PDAC specimens from 196 patients were included, 108 consecutive chemotherapy-naïve surgical specimens and 88 endoscopic ultrasound-guided fine needle biopsies (EUS-FNBs). The specimens were evaluated as per Kalimuthu by two pancreatic pathologists, resulting in Group A and Group B tumors. Digital mRNA expression profiling was performed, on the surgical specimens using the NanoString IO360 panel of 770 key tumor biology related and 30 custom-genes, and on the EUS-FNBs using a targeted panel of 123 genes., Results: Morphologic subtyping reached substantial interobserver agreement between the two pathologists. In the surgical and EUS-FNB cohorts, 44.4% and 38.6% were Group A tumors, which were associated with improved survival. Group A showed higher expression of immune-related genes and cytokine/chemokine/interleukin signaling and Group B of genes related to cancer cell proliferation and cell cycle regulation. Hierarchical clustering based on significant differences in gene expression levels between Groups A and B revealed clusters with prognostic value., Conclusions: Morphologic subtyping according to Kalimuthu is reproducible and holds prognostic value, in surgical as well as EUS-FNB specimens. As upregulation of immune-related genes was found in Group A, future studies should evaluate the potential of immune therapy approaches with special emphasis on this subtype of PDAC., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Objectives: There is a pressing need to develop clinical management pathways for grade 3 (G3) gastroenteropancreatic neuroendocrine neoplasms (GEP NEN)., Methods: We performed a retrospective study on patients with metastatic G3 GEP NEN. The relationship between baseline characteristics and progression-free survival and overall survival was analyzed using the Kaplan-Meier method. Univariate and multivariate analyses were performed using the Cox proportional hazards model., Results: We included 142 patients (74 well-differentiated neuroendocrine tumors [WDNETs], 68 poorly differentiated neuroendocrine carcinomas [PDNECs]). Patients with WDNET had prolonged survival compared with PDNEC (median, 24 vs 15 months, P = 0.0001), which persisted in both pancreatic and nonpancreatic cohorts. Well-differentiated morphology, Ki-67 <50% and positive somatostatin receptor imaging were independently associated with prolonged survival. Of the subgroup treated with first-line platinum-based chemotherapy, response rates were favorable (partial response, 47%; stable disease, 30%); there was no significant difference in response rates nor progression-free survival between WDNET and PDNEC despite significantly prolonged overall survival in the WDNET cohort., Conclusions: Our study corroborates the knowledge of 2 prognostically distinct subgroups within the World Health Organization 2019 G3 GEP NEN population, observed in both pancreatic and nonpancreatic gastrointestinal cohorts. Definitive management pathways are needed to reflect the differences between G3 WDNET and PDNEC., Competing Interests: C.To. reports advisory board and speaker honoraria from Ipsen, Novartis and AAA; outside the submitted work. He has received education grants from Ipsen, Novartis and AAA for the Royal Free NET unit. M.E.C. has received advisory board and speaker honoraria from Ipsen, Novartis, AAA, Lexicon and Pfizer; outside the submitted work. C.Th. reports consulting for Ipsen and Boehringer-Ingelheim and conference travel support from Bayer and Novartis; outside the submitted work. The other authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The nomenclature and classification of pancreatic neuroendocrine neoplasms has evolved in the last 15 years based on the advances in knowledge of the genomics, clinical behaviour and response to therapies. The current 2019 World Health Organization classification of pancreatic neuroendocrine neoplasms categorises them into three groups; pancreatic neuroendocrine tumours (PanNETs)(grade 1 grade 2, grade 3), pancreatic neuroendocrine carcinomas and mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs) based on the mitotic rate, Ki-67 index, morphological differentiation and/or co-existing tissue subtype. PanNETs are also classified into non-functional NET, insulinoma, gastrinoma, VIPoma, glucagonoma, somatostatinoma, ACTH-producing NET and serotonin producing NET based on hormone production and clinical manifestations. A portion of the cases were associated with genetic syndromes such as multiple neuroendocrine neoplasia 1 (MEN 1), neurofibromatosis and Von Hippel-Lindau syndrome. In view of the distinctive pathology and clinical behaviour of PanNENs, the current 8th AJCC/UICC staging system has separated prognostic staging grouping for PanNETs from the pancreatic neuroendocrine carcinomas or MiNENs. Pancreatic neuroendocrine carcinomas and MiNENs are staged according to the prognostic stage grouping for exocrine pancreatic carcinoma. The new stage grouping of PanNETs was validated to have survival curves separated between different prognostic groups. This refined histological and staging would lead to appropriate selections of treatment strategies for the patients with pancreatic neuroendocrine neoplasms.

Background: In 2014, the World Health Organization (WHO) released a classification system introducing neuroendocrine neoplasms (NENs) of the female reproductive tract, excluding the ovaries. This study aimed to evaluate whether retrospective adaption of the gastroenteropancreatic (GEP)-NEN classification is feasible for ovarian NENs (O-NENs) and correlates with prognosis., Methods: Sixty-eight patients diagnosed with carcinoid, small cell carcinoma (pulmonary type), paraganglioma, non-small/large cell neuroendocrine carcinoma (NEC), mixed NEC, or undifferentiated carcinomas at 20 institutions in Japan were included in this retrospective cross-sectional study. We identified O-NENs through central pathological review using a common slide set, followed by reclassification according to WHO 2010 guidelines for GEP-NENs. A proportional hazards model was used to assess the association of prognostic factors (age, stage, performance status, histology, and residual disease) with overall survival (OS) and progression-free survival (PFS)., Results: Of the 68 enrolled patients, 48 were eligible for analysis. All carcinoids (n = 32) were reclassified as NET G1/G2, whereas 14 of 16 carcinomas were reclassified as NEC/mixed adeno-NEC (MANEC) (Fisher's exact test; p < 0.01). The OS/PFS was 49.0/42.5 months and 6.5/3.9 months for NET G1/G2 and NEC/MANEC, respectively. Histology revealed that NEC/MANEC was associated with increased risk of death (HR = 48.0; 95% CI, 3.93-586; p < 0.01) and disease progression (HR = 51.6; 95% CI, 5.54-480; p < 0.01)., Conclusion: Retrospective adaption of GEP-NEN classification to O-NENs is feasible and correlates well with the prognosis of O-NENs. This classification could be introduced for ovarian tumors., (© 2020 S. Karger AG, Basel.)

To date, pancreatic cancer is still one of the most lethal cancers in the world, mainly due to the lack of early diagnosis and personalized treatment strategies. In this context, the possibility and the opportunity of identifying genetic and molecular biomarkers are crucial to improve the feasibility of precision medicine. In 2019, the World Health Organization classified pancreatic ductal adenocarcinoma cancer (the most common pancreatic tumor type) into eight variants, according to specific histomorphological features. They are: colloid carcinoma, medullary carcinoma, adenosquamous carcinoma, undifferentiated carcinoma, including also rhabdoid carcinoma, undifferentiated carcinoma with osteoclast-like giant cells, hepatoid carcinoma, and signet-ring/poorly cohesive cells carcinoma. Interestingly, despite the very low incidence of these variants, innovative high throughput genomic/transcriptomic techniques allowed the investigation of both somatic and germline mutations in each specific variant, paving the way for their possible classification according also to specific alterations, along with the canonical mutations of pancreatic cancer ( KRAS , TP53 , CDKN2A , SMAD4 ). In this review, we aim to report the current evidence about genetic/molecular profiles of pancreatic cancer variants, highlighting their role in therapeutic and clinical impact.

Histologically, the World Health Organization has classified pancreatic neuroendocrine neoplasms (p-NENs) into well-differentiated pancreatic neuroendocrine tumors (G1/G2 p-NETs) and poorly-differentiated pancreatic neuroendocrine carcinoma (G3 p-NECs) based on tumor mitotic counts and Ki-67 index. Recently, the 8th edition of American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging manual has incorporated some major changes in 2017 that the TNM staging system for p-NENs should only be applied to well-differentiated G1/G2 p-NETs, while poorly-differentiated G3 p-NECs be classified according to the new system for pancreatic exocrine adenocarcinomas. However, this new manual for p-NENs has seldom been evaluated.Data of patients with both G1/G2 and G3 non-functional p-NENs (NF-p-NENs) from our institution was retrospectively collected and analyzed using 2 new AJCC 8th staging systems. We also made survival comparisons between the 8th and 7th edition system separately for different subgroups.For G1/G2 NF-p-NETs, there were 52 patients classified in AJCC 8th edition stage I, 40 in stage II, 41 in stage III and 19 in stage IV. As for G3 NF-p-NECs, 17, 19, 24, and 18 patients were respectively defined from AJCC 8th edition stage I to stage IV. In terms of the AJCC 7th staging system, the 230 patients with NF-p-NENs were totally distributed from stage I to stage IV (94, 63, 36, 37, respectively). For the survival analysis of both G1/G2 NF-p-NETs and G3 NF-p-NECs, the AJCC 7th edition system failed to discriminate the survival differences when compared stage III with stage II or stage IV (P > .05), while the 8th edition ones could perfectly allocate patients into 4 statistically different groups (P < .05). The HCIs of AJCC 8th stage for G1/G2 NF-p-NETs [HCI=0.658, 95% confidence interval (CI)=0.602-0.741] and stage for G3 NF-p-NECs (HCI=0.704, 95% CI=0.595-0.813) was both statistically larger than those of AJCC 7th stage for different grading NF-p-NENs [(HCI=0.578, 95% CI=0.557-0.649; P=.031), (HCI=0.546, 95% CI=0.531-0.636; P = .019); respectively], indicating a more accurate predictive ability for the survivals of NF-p-NENs.Our data suggested the 2 new AJCC 8th staging systems were superior to its 7th edition for patients with both G1/G2 NF-p-NETs and G3 NF-p-NECs.

Background/objectives: Pancreatic neuroendocrine carcinoma (PanNEC)-G3 often presents along with genetic abnormalities such as KRAS, RB1, and TP53 mutations. However, the association between these genetic findings and response to chemotherapy and prognosis has not been clarified. This study aimed to clarify the clinicopathological features of PanNEC-G3., Methods: We performed a subgroup analysis of the Japanese PanNEN-G3 study (multicenter, retrospective study), which revealed that Rb loss and KRAS mutation were predictors of the response to platinum-based regimen in PanNEN-G3. We re-classified WHO grades of PanNENs using the 2017 WHO classification and then analyzed the clinicopathological features and prognostic factors in 49 patients with PanNEC-G3., Results: The rates of Rb loss and KRAS mutation in PanNEC-G3 were 54.5% and 48.7%, respectively. Patients with Rb loss and/or KRAS mutation showed a higher response rate to first-line platinum-based regimen than those without Rb loss or KRAS mutation (object response rate 70.0% vs 33.3%, odds ratio 9.22; 95% CI 1.26-67.3, P = 0.029), but tended to have shorter overall survival rates than those without Rb loss or KRAS mutation (median 239 vs 473 days, hazard ratio 2.11; 95% CI 0.92-4.86, P = 0.077)., Conclusions: Patients with PanNEC-G3 have varied clinical outcomes for platinum-based regimen. When grouped based on Rb loss and KRAS mutation, there seemed to be two groups with distinct prognoses and responses to the platinum-based regimen. PanNEC-G3 could, therefore, be classified into two distinct groups based on immunohistochemical and genetic findings., Competing Interests: Declaration of competing interest Susumu Hijioka received honoraria from Novartis pharma, Teijin pharma and Nobel pharma. Masafumi Ikeda received honoraria from Novartis pharma and commercial research funding from Novartis pharma. Noritoshi Kobayashi received honoraria from Novartis pharma. Nobumasa Mizuno received grants from Japan Society for the Promotion of Science (JSPS), and Japan Agency for Medical Research and Development (AMED), grants and personal fees from Yakult Honsha, Novartis, and Taiho Pharmaceutical, personal fees from Pfizer. Other authors have no conflict of interest directly relevant to the content of this article., (Copyright © 2020 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Objective: Although acute pancreatitis (AP) is a self-limited disease under supportive and medical treatment, it can have life-threatening potential in some patients. Results of studies reporting outcomes of AP-associated pancreatic malignancy are controversial. The aim of this study was to evaluate the severity and prognosis of pancreatic cancer (PC)-related AP by modified Balthazar score., Methods: A total of 354 patients hospitalized and followed up in our clinic between 2013 and 2019 were included in the study. Demographic data of all patients were recorded. The etiology of all cases was determined. According to the etiology, the cases were divided into 2 groups: AP related to pancreatic malignancy and AP due to nonmalignant causes. The patients underwent computed tomography of the abdomen within the first 12 hours of admission and after 3 to 7 days. Patients were evaluated and classified by modified Balthazar classification., Results: Malignancy-related AP was detected in 18 (5.1%) patients. A total of 336 cases (94.9%) were related to nonmalignant causes. There was no statistically significant difference in the severity of AP in both groups at admission and after 3 days ( p > 0.05). The changes (regression, progression, or no change) in the disease severity at the first and the subsequent imaging were examined. There was no significant relationship between the 2 groups ( p > 0.05)., Conclusion: AP may be the clinical manifestation of PC or PC may induce AP in various ways. It was shown that the underlying malignancy did not adversely affect the severity and course of AP.

Background: Pancreatic neuroendocrine neoplasms (p-NENs) are a group of highly heterogeneous tumors with distinct clinicopathological features and long-term prognosis. In 2017, in order to better stratify patients into prognostic groups and predicting their outcomes, World Health Organization (WHO) officially updated its grading system for p-NENs which distinguished these neoplasms among Grading 1 (G1) pancreatic neuroendocrine tumors (p-NETs), G2 p-NETs, G3 p-NETs and G3 pancreatic neuroendocrine carcinomas (p-NECs). However, this new grading classification for p-NENs has not yet been rigorously validated., Methods: Data of patients who were surgically treated and histopathologically diagnosed as p-NENs at West China Hospital of Sichuan University from January 2002 to December 2018 were retrospectively collected and analyzed according the novel WHO 2017 grading classification., Results: We eventually enrolled 480 eligible patients with p-NENs in our present study, in which 150 patients with WHO 2017 G1 p-NETs, 158 with G2 p-NETs, 64 with G3 p-NETs and 108 with G3 p-NECs were identified. The estimated 5-year overall survival for patients with G1 p-NETs, G2 p-NETs, G3 p-NETs and G3 p-NECs was 75.8, 58.4, 35.1 and 11.1%, with a median survival time of 85.3mons, 67.4mons, 51.3mons and 26.8mons, respectively. Patients with G2 p-NETs present notably worse survival than those with G1 p-NETs (P = 0.03). Survival of G3 p-NETs were significantly worse than that of G1 p-NETs or G2 p-NETs (P < 0.001, P = 0.023, respectively), as well as that when comparing G3 p-NECs with G1 p-NETs or G2 p-NETs (P < 0.001, P < 0.001, respectively). Patients with G3 p-NECs showed statistically shorter survival than those with G3 p-NETs (P < 0.001). Both WHO 2017 and 2010 grading criteria could be independent predictor for the OS of p-NENs (P = 0.016, P = 0.022; respectively). The 95% confidence intervals of WHO 2017 grading classification (0.983-9.454) was slightly smaller than that of WHO 2010 criteria (0.201-13.374), indicating a relatively more accurate predicting ability for the prognosis of p-NENs., Conclusion: The WHO 2017 grading classification for p-NENs could successfully allocate patients into four groups with distinct clinical features and significant survival differences, which might be superior to the WHO 2010 criteria for its better prognostic stratification and more accurate predicting ability.

Single cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identification remains a partially manual task. Published algorithms for automatic cell annotation are limited to known cell types and fail to capture novel populations, especially cancer cells. We developed northstar, a computational approach to classify thousands of cells based on published data within seconds while simultaneously identifying and highlighting new cell states such as malignancies. We tested northstar on data from glioblastoma, melanoma, and seven different healthy tissues and obtained high accuracy and robustness. We collected eleven pancreatic tumors and identified three shared and five private neoplastic cell populations, offering insight into the origins of neuroendocrine and exocrine tumors. Northstar is a useful tool to assign known and novel cell type and states in the age of cell atlases.

Competing Interests: Disclosure Research reported in this publication was supported by National Cancer Institute of the National Institutes of Health under award number P30-CA240139 and NIH R01 CA161976 to NBM.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a relative paucity of cancer cells that are surrounded by an abundance of nontumor cells and extracellular matrix, known as stroma. The interaction between stroma and cancer cells contributes to poor outcome, but how proteins from these individual compartments drive aggressive tumor behavior is not known. Here, we report the proteomic analysis of laser-capture microdissected (LCM) PDAC samples. We isolated stroma, tumor, and bulk samples from a cohort with long- and short-term survivors. Compartment-specific proteins were measured by mass spectrometry, yielding what we believe to be the largest PDAC proteome landscape to date. These analyses revealed that, in bulk analysis, tumor-derived proteins were typically masked and that LCM was required to reveal biology and prognostic markers. We validated tumor CALB2 and stromal COL11A1 expression as compartment-specific prognostic markers. We identified and functionally addressed the contributions of the tumor cell receptor EPHA2 to tumor cell viability and motility, underscoring the value of compartment-specific protein analysis in PDAC.

Disrupted chromatin regulatory processes contribute to the development of cancer, in particular pancreatic ductal adenocarcinoma. The assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) is typically used to study chromatin organization. Here, we present a revised ATAC-seq protocol to study chromatin accessibility in adherent patient-derived cell lines. We provide details on how to calculate the library molarity using Agilent's Bioanalyzer and an analysis pipeline for peak calling and transcription factor mapping. For complete details on the use and execution of this protocol, please refer to Brunton et al. (2020)., Competing Interests: The authors declare no competing interests., (© 2020 The Authors.)

Pancreatic ductal adenocarcinoma (PDA) is a devastating disease with a poor survival rate. It is resistant to therapy in part due to its unique tumor microenvironment, characterized by a desmoplastic reaction resulting in a dense stroma that constitutes a large fraction of the tumor volume. A major contributor to the desmoplastic reaction are cancer-associated fibroblasts (CAFs). CAFs actively interact with cancer cells and promote tumor progression by different mechanisms, including extracellular matrix deposition, remodeling, and secretion of tumor promoting factors, making CAFs an attractive target for PDA. However, emerging evidences indicate significant tumor-suppressive functions of CAFs, highlighting the complexity of CAF biology. CAFs were once considered as a uniform cell type within the cancer stroma. Recently, the existence of CAF heterogeneity in PDA has become appreciated. Due to advances in single cell technology, distinct subtypes of CAFs have been identified in PDA. Here we review recent updates in CAF biology in PDA, which may help develop effective CAF-targeted therapies in the future.

Pancreatic neuroendocrine neoplasms (PanNENs) contain two primary subtypes with distinct molecular features and associated clinical outcomes: well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). PanNENs are a group of clinically heterogeneous tumors, whose diagnosis is based on tumor morphologic features and proliferation indices. However, these standards incompletely meet clinical needs by failing to adequately assess the likelihood of tumor recurrence and the potential for therapeutic response. We therefore focused on discussing molecular advances that facilitate the understanding of heterogeneity and exploration of reliable recurrence/treatment predictors. Taking advantage of high-throughput technologies, emerging methods of molecular subtyping in PanNETs include classifications based on co-existing multi-gene mutations, a large-scale loss of heterozygosity or copy number variation, and islet cell type-specific signatures. PanNEC molecular updates were discussed as well. This review aims to help the field classify PanNEN molecular subtypes, gain insights to aid in the solving of clinical, pathological unmet needs, and detect challenges and concerns of genetically-driven trials., Competing Interests: Declaration of Competing Interest Authors declare no conflicts of interest., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

This article is the result of the North American Neuroendocrine Tumor Society consensus conference on the medical management of pancreatic neuroendocrine tumors from July 19 to 20, 2018. The guidelines panel consisted of medical oncologists, pathologists, gastroenterologists, endocrinologists, and radiologists. The panel reviewed a series of questions regarding the medical management of patients with pancreatic neuroendocrine tumors as well as questions regarding surveillance after resection. The available literature was reviewed for each of the question and panel members voted on controversial topics, and the recommendations were included in a document circulated to all panel members for a final approval.

Some recent reports suggested that many Sertoli cell tumors, not otherwise specified (SCTs-NOS) of the testis were analogs of the solid pseudopapillary neoplasm (SPN) of the pancreas. One of the most relied on pieces of information for this assertion was the shared occurrence in both neoplasms of exon 3 mutations of the CTNNB1 gene, which was reflected by nuclear β-catenin expression. We, therefore, compared the morphologic and immunohistochemical features of 18 SCTs-NOS with strong, diffuse nuclear β-catenin expression with 16 SPNs that also showed such positivity. Although there were clear similarities in the light microscopic features of these neoplasms, there were also significant differences that included, in SCT-NOS and SPN, respectively: hollow tubules (53% vs. 0%), sheet-like growth (44% vs. 94%), circumscription (79% vs. 25%), corded or trabecular patterns (81% vs. 31%), formation of papillae or pseudopapillae (24% vs. 69%), growth in nests or clusters (94% vs. 50%), perivascular pseudorosettes (13% vs. 56%), and rhabdoid cytology (6% vs. 50%). Commonly shared morphologic features included signet-ring cells, pale or foamy cytoplasm, myxoid stroma, cyst formation, perivascular hyalinization, and globular or band-like basement membrane deposits. On immunohistochemical study, sex cord markers were frequently positive in SCTs-NOS (steroidogenic factor-1-94%; FOXL2-87%; SOX9-69%; calretinin-60%; Wilms tumor-1-38%; inhibin-29%) whereas all of these markers were negative in the SPNs. We conclude that even though SCT-NOS and SPN share some morphologic features and nuclear immunoreactivity for β-catenin, there remain differences, both morphologically and immunohistochemically, between these neoplasms to the degree that SCT-NOS should not be equated with pancreatic SPN.

The classification of ampullary adenocarcinoma into intestinal and pancreatobiliary sub-types has been found to be important in predicting prognosis and determining therapeutic strategy. Due to considerable inter-observer variability in sub-typing based solely on morphology, higher frequency of poorly differentiated cancers and low incidence of the disease, the histomorphologic classification of ampullary adenocarcinoma remains one of the grey zones in surgical pathology. Pan-IN is a well recognized precursor to pancreatic adenocarcinoma. Three studies have shown concurrent Pan-IN in patients with ampullary carcinoma, but their association with the two sub-types has not yet been reported. Fourteen cases of surgical resection for ampullary adenocarcinoma were retrieved from the archives. The cases were classified into two groups based on the presence or absence of concomitant Pan-IN. All the cases were stained for CK7, CK 20, Villin and CDX 2 and were classified as intestinal or pancreatobiliary types based on the staining pattern. All the 10 cases with Pan-IN stained negative for CDX2 and were classified as pancreatobiliary type (p = 0.01). Of the cases without Pan-IN, 3 were classified as intestinal sub-type based on morphology and CDX2 positivity and 1 was classified as pancreatobiliary type. Concomitant Pan-IN was present in 91% of pancreatobiliary type of ampullary adenocarcinoma. The grade of Pan-IN did not influence the grade or stage of the adenocarcinoma (p > 0.05). The co-occurrence of Pan-IN in a high percentage of the pancreatobiliary sub-type and its complete absence in the intestinal sub-type may serve as a strong differentiator between the two sub-types.

Background: En bloc celiac axis resection (CAR) for pancreatic cancer is considered increasingly after modern neoadjuvant chemotherapy (NAC). Appleby and distal pancreatectomy with CAR are anatomically inaccurate terms, as tumors can extend beyond celiac axis proper, requiring concurrent resection of the proper hepatic artery and/or superior mesenteric artery., Study Design: A 3-level classification for CAR (class 1, 2, or 3) was developed after retrospective review of an arterial resection database describing anatomical variants that dictate pancreatectomy-type, formal arterial revascularization, and gastric preservation. Perioperative and oncologic outcomes were assessed., Results: Of 90 CARs for pancreatic cancer, 89% patients received NAC, 35% requiring chemotherapeutic switch. There were 41 class 1, 33 class 2, and 16 class 3 CARs, with arterial and venous revascularization performed 62% and 66%, respectively. Ninety-day mortality decreased to 4% in the last 50 cases (p = 0.035); major morbidity was unchanged (55%). Any hepatic or gastric ischemia occurred in 20% and 10% patients, respectively, and arterial revascularization was protective. R0 resection rate (88%) was associated with chemoradiation (p = 0.004). Median overall survival was 36.2 months, superior with NAC (8.0 vs. 43.5 months). Predictors of survival after NAC included chemotherapy duration, carbohydrate antigen 19-9 response, pathologic response, and lymph node status. Major pathologic response (p = 0.036) and extended duration NAC (p = 0.007) were independent predictors on multivariate analysis., Conclusions: Current terminology for CAR inadequately describes all operative variants. Our classification, based on the largest single-center series, allows complex operative planning and standardized reporting across institutions. Extent of arterial involvement determines pancreatectomy type, need for arterial revascularization, and likelihood of gastric preservation. Operative mortality has improved, morbidity remains significant, and survival favorable after extended NAC with associated pathologic responses; given these factors, CAR should only be considered in fit patients with objective NAC responses at specialized centers., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background/objectives: We sought to identify the reliability of AJCC clinical staging was in comparison to pathologic staging in surgically resected patients with pancreatic cancer., Methods: We used the National Cancer Database Pancreas from 2004 to 2016 and evaluated patients who underwent resection for PDAC with all documented components of clinical and pathologic stage. We first evaluated the distribution of overall clinical stage and pathologic stage and then evaluated for stage migration by assessing the number of patients who shifted from a clinical stage group to a respective pathologic stage group. To further characterize the migratory pattern, we assessed the distribution of clinical and pathologic T-stage and N-stage., Results: In our cohort of 28,338 patients who underwent resection for PDAC, AJCC clinical staging did not reliably predict pathologic stage. Stage migration after resection was responsible for discrepancies between the distribution of overall clinical stage and pathologic stage. The predominant migration was from patients with clinical stage I disease to pathologic stage II disease. Most patients with clinical T1 and T2 disease were upstaged to pathologic T3 disease and over half of patients with clinical N0 disease were upstaged to pathologic N1 disease after resection., Discussion: Clinical staging appears to overrepresent early T1, T2, and N0 disease, and underrepresent T3 and N1 disease., Competing Interests: Declaration of competing interest The National Cancer Data Base (NCDB) is a joint project of the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society. The CoC’s NCDB and the hospitals participating in the CoC NCDB are the source of the de-identified data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors. The authors report no conflict of interest related to this work., (Copyright © 2020 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Background/objectives: This single-center study aimed to evaluate treatment outcomes and long-term prognosis of patients with pancreatic neuroendocrine neoplasms (PanNENs) based on the World Health Organization (WHO) 2017 classification., Methods: We enrolled 245 patients with PanNENs treated at Kyushu University Hospital between January 1987 and March 2018. PanNENs were categorized according to the WHO 2017 classification or further subdivisions of Ki-67 index. Clinicopathological features, median survival time (MST), and prognostic factors were retrospectively analyzed., Results: The number of PanNENs, especially non-functioning PanNENs, has increased over the last decade. The mean MST of all patients was 202 months; which was longest in patients with NET G1 (n = 145, MST = 261 months) relative to NET G2 (n = 72, 132 months), NET G3 (n = 3, 34 months) and NEC G3 (n = 17, 9 months). Prognosis in patients with surgery as the first-line treatment was significantly better than in those with drug therapy. However, 26% of patients who underwent curative resection developed recurrence after a median time of 28.7 months. In unresectable PanNENs (n = 97), the MST and 5-year survival rate were 78 months and 55.8%, respectively. Poor differentiation, Ki-67 index of >10% and presence of liver metastasis were significant unfavorable predictors. Response to first-line therapy (stable disease/partial response) and three or more treatment regimens were significant favorable predictors for unresectable PanNENs according to multivariate analyses (p < 0.01)., Conclusions: We demonstrated the utility of the WHO 2017 classification for PanNENs in the real clinical setting. For better prognosis in PanNENs, the use of three or more regimens should be considered., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2020 IAP and EPC. Published by Elsevier B.V. All rights reserved.)

Objective: Insulinomas and non-functional pancreatic neuroendocrine tumours (NF-PanNETs) have distinctive clinical presentations but share similar pathological features. Their genetic bases have not been comprehensively compared. Herein, we used whole-genome/whole-exome sequencing (WGS/WES) to identify genetic differences between insulinomas and NF-PanNETs., Design: The mutational profiles and copy-number variation (CNV) patterns of 211 PanNETs, including 84 insulinomas and 127 NF-PanNETs, were obtained from WGS/WES data provided by Peking Union Medical College Hospital and the International Cancer Genome Consortium. Insulinoma RNA sequencing and immunohistochemistry data were assayed., Results: PanNETs were categorised based on CNV patterns: amplification, copy neutral and deletion. Insulinomas had CNV amplifications and copy neutral and lacked CNV deletions. CNV-neutral insulinomas exhibited an elevated rate of YY1 mutations. In contrast, NF-PanNETs had all three CNV patterns, and NF-PanNETs with CNV deletions had a high rate of loss-of-function mutations of tumour suppressor genes. NF-PanNETs with CNV alterations (amplification and deletion) had an elevated risk of relapse, and additional DAXX/ATRX mutations could predict an increased relapse risk in the first 2-year period., Conclusion: These WGS/WES data allowed a comprehensive assessment of genetic differences between insulinomas and NF-PanNETs, reclassifying these tumours into novel molecular subtypes. We also proposed a novel relapse risk stratification system using CNV patterns and DAXX/ATRX mutations., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

This study aims to identify the core modules associated with pancreatic cancer (PC) types and the ncRNAs and transcription factors (TFs) that regulate core module genes by weighted gene co-expression network analysis (WGCNA). WGCNA was used to analyze the union of genes related to PC in NCBI and OMIM databases and the differentially expressed genes screened by TCGA-PAAD database. Samples were clustered according to gene expression in gene modules and Fisher exact method was performed. GO and KEGG were used for enrichment analysis to visually display module genes and screen driver genes. Hypergeometric test method was used to calculate pivot nodes among ncRNAs, TFs and mRNA based on RAID 2.0 and TRRUST v2 databases. The blue and yellow modules were identified as the core modules associated with PC types. MST1R, TMPRSS, MIR198, SULF1, COL1A1 and FAP were the core genes in the modules. Hypergeometric test results showed that ANCR, miR-3134, MT1DP, LOC154449, LOC28329 and other ncRNAs were key factors driving blue module genes, while LINC-ROR, UCA1, SNORD114-4, HEIH, SNORD114-6 and other ncRNAs were key factors driving yellow module genes. TFs with significant regulatory effect on blue module included LCOR, PIAS4, ZEB1, SNAI2, SMARCA4, etc. and on yellow module included HOXC6, PER2, HOXD3, TWIST2, VHL, etc. The core modules associated with PC types were proved as yellow and blue modules, and important ncRNAs and TFs regulating yellow and blue modules were found. This study provides relevant evidence for further identification of PC types.

Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes ( GRAP2 , ICAM3 and A2ML1 ) correlated with prognosis were identified. Conclusion: Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease that can be separated into distinct subtypes based on molecular signatures. Identifying PDAC subtype-specific therapeutic vulnerabilities is necessary to develop precision medicine approaches to treat PDAC., Experimental Design: A total of 56 PDAC liver metastases were obtained from the UNMC Rapid Autopsy Program and analyzed with quantitative proteomics. PDAC subtypes were identified by principal component analysis based on protein expression profiling. Proteomic subtypes were further characterized by the associated clinical information, including but not limited to survival analysis, drug treatment response, and smoking and drinking status., Results: Over 3,960 proteins were identified and used to delineate four distinct PDAC microenvironment subtypes: (i) metabolic; (ii) progenitor-like; (iii) proliferative; and (iv) inflammatory. PDAC risk factors of alcohol and tobacco consumption correlate with subtype classifications. Enhanced survival is observed in FOLFIRINOX treated metabolic and progenitor-like subtypes compared with the proliferative and inflammatory subtypes. In addition, TYMP, PDCD6IP, ERAP1, and STMN showed significant association with patient survival in a subtype-specific manner. Gemcitabine-induced alterations in the proteome identify proteins, such as serine hydroxymethyltransferase 1, associated with drug resistance., Conclusions: These data demonstrate that proteomic analysis of clinical PDAC liver metastases can identify molecular signatures unique to disease subtypes and point to opportunities for therapeutic development to improve the treatment of PDAC., (©2019 American Association for Cancer Research.)