Advances in Management of Nonfunctional Pancreas Neuroendocrine Tumors.

This article presents updates in the surgical management of non-functional sporadic pancreas neuroendocrine tumors NET, including considerations for assessment of biologic behavior to support decision-making, indications for surgery, and surgical approaches tailored to the unique nature of neuroendocrine tumors.

Pancreatic Cancer Screening among High-risk Individuals.

Pancreatic ductal adenocarcinoma (PDAC) continues to remain one of the leading causes of cancer-related death. Unlike other malignancies where universal screening is recommended, the same cannot be said for PDAC. The purpose of this study is to review which patients are at high risk of developing PDAC and therefore candidates for screening, methods/frequency of screening, and risk for these groups of patients.

Pancreas Cyst Diagnosis and Advances in Cyst Fluid Analysis.

Pancreatic Cystic Neoplasms (PCN) represent a diverse group of tumors, some of which may progress to pancreatic cancer. Considering their high prevalence in the general population, the development of reliable biomarkers is crucial. The ideal biomarker will accurately diagnose the subtype of PCN and assess the risk of high-grade dysplasia or invasive cancer. Cyst fluid analysis has emerged as a promising approach to accomplish this goal, yet no single marker has yet gained unanimous support for routine inclusion in PCN evaluation.

Use of Diagnostic Laparoscopy and Peritoneal Washings for Pancreatic Cancer.

Pancreatic adenocarcinoma is an aggressive malignancy that often presents with advanced disease. Accurate staging is essential for treatment planning and shared decision-making with patients. Staging laparoscopy is a minimally invasive procedure that can detect radiographically occult metastatic disease. Its routine use with the collection of peritoneal washings in patients with pancreatic cancer remains controversial. We, herein, review the current literature concerning staging laparoscopy and peritoneal washings in patients with pancreatic cancer.

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. AIM: The study aims to diagnose a patient's pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. METHODS: Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. RESULTS: Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features "plasma_CA19_9", REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. CONCLUSIONS: This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.

Navigating the diagnostic gray zone: a challenging case of pancreatic high-grade neuroendocrine neoplasm.

BACKGROUND: Grade 3 neuroendocrine tumor (G3 PanNET) and poorly differentiated neuroendocrine carcinoma (PanNEC) of the pancreas are considered distinct entities from a biological and prognostic perspective but may have overlapping features complicating a definitive diagnosis. CASE PRESENTATION: A 52-year-old female presented with a pancreatic body mass and liver lesions. Initial biopsies showed variable lower- and higher-grade morphologies and modestly elevated Ki67 proliferation index up to 30%, leading to a diagnosis of G3 PanNET. The patient underwent everolimus treatment followed by surgical resection, revealing a complex tumor with features of both G3 PanNET and PanNEC, including admixed well- and poorly differentiated morphologies, modestly elevated hotspot Ki67 of 28%, retained ATRX/DAXX expression, and loss of RB expression. The final diagnosis rendered was "high-grade neuroendocrine neoplasm" with discussion of both entities in the differential. Post-operatively, the patient remains alive with stable metastases. CONCLUSIONS: This case highlights the diagnostic complexities of distinguishing G3 PanNET and PanNEC even with the support of ancillary immunohistochemical and molecular studies. In addition, such cases raise the possibility that G3 PanNET and PanNEC may lie on a spectrum of disease with potential biological overlap.

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma.

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review.

Pancreatic ductal adenocarcinoma (PDAC) is frequently detected in late stages, which leads to limited therapeutic options and a dismal overall survival rate. To date, no robust method for the detection of early-stage PDAC that can be used for targeted screening approaches is available. Liquid biopsy allows the minimally invasive collection of body fluids (typically peripheral blood) and the subsequent analysis of circulating tumor cells or tumor-associated molecules such as nucleic acids, proteins, or metabolites that may be useful for the early diagnosis of PDAC. Single biomarkers may lack sensitivity and/or specificity to reliably detect PDAC, while combinations of these circulating biomarkers in multimarker panels may improve the sensitivity and specificity of blood test-based diagnosis. In this narrative review, we present an overview of different liquid biopsy biomarkers for the early diagnosis of PDAC and discuss the validity of multimarker panels.

Analytical Validation of an Early Detection Pancreatic Cancer Test Using 5-Hydroxymethylation Signatures.

Early detection of pancreatic cancer has been shown to improve patient survival rates. However, effective early detection tools to detect pancreatic cancer do not currently exist. The Avantect Pancreatic Cancer Test, leveraging the 5-hydroxymethylation [5-hydroxymethylcytosine (5hmC)] signatures in cell-free DNA, was developed and analytically validated to address this unmet need. We report a comprehensive analytical validation study encompassing precision, sample stability, limit of detection, interfering substance studies, and a comparison with an alternative method. The assay performance on an independent case-control patient cohort was previously reported with a sensitivity for early-stage (stage I/II) pancreatic cancer of 68.3% (95% CI, 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). Precision studies showed a cancer classification of 100% concordance in biological replicates. The sample stability studies revealed stable assay performance for up to 7 days after blood collection. The limit of detection studies revealed equal results between early- and late-stage cancer samples, emphasizing strong early-stage performance characteristics. Comparisons of concordance of the Avantect assay with the enzymatic methyl sequencing (EM-Seq) method, which measures both methylation (5-methylcytosine) and 5hmC, were >95% for all samples tested. The Avantect Pancreatic Cancer Test showed strong analytical validation in multiple validation studies required for laboratory-developed test accreditation. The comparison of 5hmC versus EM-Seq further validated the 5hmC approach as a robust and reproducible assay.

Non-functional alpha-cell hyperplasia with glucagon-producing NET: a case report.

Introduction: Alpha-cell hyperplasia (ACH) is a rare pancreatic endocrine condition. Three types of ACH have been described: functional or nonglucagonoma hyperglucagonemic glucagonoma syndrome, reactive or secondary to defective glucagon signaling, and non-functional. Few cases of ACH with concomitant pancreatic neuroendocrine tumors (pNETs) have been reported and its etiology remains poorly understood. A case report of non-functional ACH with glucagon-producing NET is herein presented. Case report: A 72-year-old male was referred to our institution for a 2 cm single pNET incidentally found during imaging for acute cholecystitis. The patient's past medical history included type 2 diabetes (T2D) diagnosed 12 years earlier, for which he was prescribed metformin, dapagliflozin, and semaglutide. The pNET was clinically and biochemically non-functioning, apart from mildly elevated glucagon 217 pg/ml (<209), and 68Ga-SSTR PET/CT positive uptake was only found at the pancreatic tail (SUVmax 11.45). The patient underwent a caudal pancreatectomy and the post-operative 68Ga-SSTR PET/CT was negative. A multifocal well-differentiated NET G1, pT1N0M0R0 (mf) strongly staining for glucagon on a background neuroendocrine alpha-cell hyperplasia with some degree of acinar fibrosis was identified on pathology analysis. Discussion and conclusion: This case reports the incidental finding of a clinically non-functioning pNET in a patient with T2D and elevated glucagon levels, unexpectedly diagnosed as glucagon-producing NET and ACH. A high level of suspicion was required to conduct the glucagon immunostaining, which is not part of the pathology routine for a clinically non-functioning pNET, and was key for the diagnosis that otherwise would have been missed. This case highlights the need to consider the diagnosis of glucagon-producing pNET on an ACH background even in the absence of glucagonoma syndrome.

Serum PCSK9 is a novel serological biomarker for the diagnosis and prognosis of pancreatic cancer.

Background: Although CA19-9 is an essential blood biomarker of pancreatic cancer (PC), its sensitivity and specificity are limited for early detection. Methods: We analyzed the serum proprotein convertase subtilisin/kexin type 9 (sPCSK9) in PC patients, benign disease groups (BDG), and healthy controls (HC) by ELISA. Results: Consistently, sPCSK9 was considerably lower in PC patients than in HC (Z = -2.546, P < 0.05), and sPCSK9 in PC patients was statistically significantly higher than in BDG (Z = -5.457, P < 0.001). sPCSK9 was linked to the invasion of lymph nodes (χ2 = 6.846, P < 0.01). According to ROC curves, combining sPCSK9 with CA19-9 could potentially enhance the diagnostic capability of CA19-9 in early-stage PC patients. Furthermore, the low sPCSK9 group (n = 41) exhibited statistically significantly prolonged overall survival compared to the high sPCSK9 group (n = 15), with median survival times of 27 months (95% CI [17.59-36.41]) and 11 months (95% CI [7.21-14.79]), respectively (P = 0.022). Conclusion: The diagnostic performance of CA19-9 for early-stage PC patients could be improved by combining sPCSK9 with CA19-9. Moreover, the higher sPCSK9 group has a significantly shorter overall survival rate.

Serum matrix metalloproteinase-7, Syndecan-1, and CA 19-9 as a biomarker panel for diagnosis of pancreatic ductal adenocarcinoma.

AIMS AND BACKGROUND: Matrix metalloproteinase-7 (MMP-7) and Syndecan-1 (SDC1) are involved in multiple functions during tumorigenesis. We aimed to evaluate the diagnostic and prognostic performance of these serum proteins, as potential biomarkers, in patients with pancreatic ductal adenocarcinoma (PDAC) and benign pancreatic cysts. METHODS: In this case-control study, patients with newly diagnosed PDAC (N = 121) were compared with the benign cyst (N = 66) and healthy control (N = 48) groups. Serum MMP-7 and SDC1 were measured by ELISA. The diagnostic accuracy of their levels for diagnosing PDAC and pancreatic cysts was computed, and their association with survival outcomes was evaluated. RESULTS: MMP-7 median serum levels were significantly elevated in the PDAC (7.3 ng/mL) and cyst groups (3.7 ng/mL) compared with controls (2.9 ng/mL) (p < 0.001 and 0.02, respectively), and also between the PDAC and cyst groups (p < 0.001), while SDC1 median serum levels were significantly elevated in PDAC (43.3 ng/mL) compared with either cysts (30.1 ng/mL, p < 0.001) or controls (31.2 ng/mL, p < 0.001). The receiver operating characteristic curve analysis area under the curve in PDAC versus controls was 0.90 and 0.78 for MMP-7 and SDC1, respectively, while it was 1.0 for the combination of the two and CA 19-9 (p < 0.001). The combination of the three biomarkers had a perfect sensitivity (100%). CONCLUSIONS: Due to its high sensitivity, this biomarker panel has the potential to rule out PDAC in suspected cases.

Molecular diagnosis for detecting KRAS mutation in peritoneal washing fluid of pancreatic ductal adenocarcinoma.

Positive peritoneal washing cytology is an indicator of poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC); however, its sensitivity is relatively low. This study evaluated the performance of peptide nucleic acid (PNA)-directed PCR clamping as a molecular-based peritoneal washing cytology for sensitive detection of KRAS mutation in PDAC. Intraoperative peritoneal washing fluid (IPWF) obtained from patients with PDAC who underwent surgery was analyzed. PNA-directed PCR clamping was performed on DNA extracted from IPWF. Among 54 patients enrolled, threshold cycle (Ct) was significantly lower in patients with positive peritoneal washing cytology than in those with negative peritoneal washing cytology (P < 0.001) and in patients with peritoneal dissemination than in those without peritoneal dissemination (P < 0.01). The optimal Ct cut-off to predict KRAS mutations in IPWF was 36.42 based on a receiver operating characteristic curve. The sensitivity, specificity, and accuracy for molecular diagnosis were 100%, 80.0%, and 85.2%, respectively. Peritoneal dissemination recurrence was significantly more frequent in patients with a positive molecular diagnosis than in those with a negative diagnosis (38.9 vs. 8.0%, P = 0.013). The genomic approach might be clinically valuable for a more precise tumor cell detection in IPWF.

Case of Undifferentiated Carcinoma with Osteoclast-like Giant Cells Associated with a Mucinous Cystic Neoplasm of the Pancreas: A Diagnostic Conundrum.

Undifferentiated carcinoma with osteoclast-like giant cells (UC-OGC) is a rare histological subtype of pancreatic ductal adenocarcinoma according to the World Health Organization classification of digestive system tumors. This subtype is exceptionally uncommon, accounting for less than 1% of pancreatic malignant tumors. This paper presents a rare case of a 62-year-old female patient diagnosed with UC-OGC. The patient initially presented with symptoms, including epigastric pain and the presence of an abdominal mass, which led to further investigation and the eventual diagnosis of this unusual and challenging form of pancreatic cancer.

Focal Autoimmune Pancreatitis Morphologically Mimicking Pancreatic Cancer: A Case Report and Literature Review.

BACKGROUND Autoimmune pancreatitis (AIP) is identified as an outlier in the clinical practice of chronic pancreatitis caused by autoimmune system dysfunction. AIP is classified into 3 subtypes: AIP type 1 and AIP type 2, which are both sensitive to corticosteroids, and the recently introduced AIP type 3. CASE REPORT We present a case of a patient who presented with painless obstructive jaundice. Computed tomography (CT) revealed hyperdense gallbladder material, further dilatation of intrahepatic bile ducts, and distention of the bile duct (15 mm). Based on the available clinical data, which were strongly compatible with pancreatic cancer, Whipple surgery was selected as the treatment for this case. The consequent histopathological report revealed areas of pancreatic parenchyma with fibrous connective tissue development and dense inflammatory cell infiltration with lymphocytes and plasmacytes, which showcased IgG4 positivity. The clinical results suggested a diagnosis of AIP type 1, and the patient was referred to his treating physician for further treatment of AIP. Preoperative histological examination of the pancreas, along with evaluation of the radiological and serological features, could have aided in determining the diagnosis of AIP type 1 pancreatitis despite the unique abnormality of this particular case. CONCLUSIONS Given the aforementioned conditions, AIP, even as a rare clinical entity, emerges as a canonical ailment and should be considered a viable possibility in clinical practice since it can exclude the patient from an unnecessary surgery.

Hallmarks of pancreatic cancer: spotlight on TAM receptors.

Pancreatic ductal adenocarcinoma (PDAC) represents the most prevalent type of pancreatic cancer and ranks among the most aggressive tumours, with a 5-year survival rate of less than 11%. Projections indicate that by 2030, it will become the second leading cause of cancer-related deaths. PDAC presents distinctive hallmarks contributing to its dismal prognosis: (i) late diagnosis, (ii) heterogenous and complex mutational landscape, (iii) high metastatic potential, (iv) dense fibrotic stroma, (v) immunosuppressive microenvironment, and (vi) high resistance to therapy. Mounting evidence has shown a role for TAM (Tyro3, AXL, MerTK) family of tyrosine kinase receptors in PDAC initiation and progression. This review aims to describe the impact of TAM receptors on the defining hallmarks of PDAC and discuss potential future directions using these proteins as novel biomarkers for early diagnosis and targets for precision therapy in PDAC, an urgent unmet clinical need.

A neuroendocrine biomarker revolution from monoanalyte to multianalyte biomarkers in non-functioning gastro-entero-pancreatic neuroendocrine neoplasms.

Neuroendocrine neoplasms (NENs) arise from neuroendocrine cells in a wide variety of organs. One of the most affected disease sites is the gastrointestinal system, which originates the gastro-entero-pancreatic NENs (GEP-NENs), a heterogenous group of malignancies that are rapidly increasing in incidence. These tumors can be functioning, with secretory activity leading to identifiable clinical syndromes, or non-functioning, with no secretory activity but with local symptoms of tumor growth and metastasis. A limitation in biomarkers is a crucial unmet need in non-secretory NEN management, as clinical decision-making is made more difficult by obstacles in tumor classification, prognostic evaluation, assessment of treatment response and surveillance. The objective of this review is to present existing and novel biomarkers for NENs that can function as prognostic factors and monitor disease progression or regression longitudinally, with a special emphasis on innovative research into novel multianalyte biomarkers.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.

BACKGROUND: The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. METHODS: 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. RESULTS: The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively). CONCLUSION: Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.