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Low migratory dendritic cell (DC) levels pose a challenge in cancer immune surveillance, yet their impact on tumor immune status and immunotherapy responses remains unclear. We present clinical evidence linking reduced migratory DC levels to immune-cold tumor status, resulting in poor patient outcomes. To address this, we develop an autologous DC-based nanovaccination strategy using patient-derived organoid or cancer cell lysate-pulsed cationic nanoparticles (cNPs) to load immunogenic DC-derived microvesicles (cNP cancer cell @MV DC ). This approach transforms immune-cold tumors, increases migratory DCs, activates T cells and natural killer cells, reduces tumor growth, and enhances survival in orthotopic pancreatic and lung cancer models, surpassing conventional methods. In vivo imaging reveals superior cNP cancer cell @MV DC accumulation in tumors and lymph nodes, promoting immune cell infiltration. Mechanistically, cNPs enrich mitochondrial DNA, enhancing cGAS-STING-mediated DC activation and migration. Our strategy shifts cold tumors to a hot state, enhancing antitumor immunity for potential personalized cancer treatments., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Gemcitabine is first-line chemotherapy for pancreatic cancer, however, the development of resistance limits its effectiveness. The tripartite motif-containing 11 (TRIM11) protein plays crucial roles in tumor development and undergoes auto-polyubiquitination to promote interactions in selective autophagy. Therefore, Understanding whether TRIM11 is involved in ferritinophagy and gemcitabine resistance in pancreatic cancer is critical in developing pancreatic cancer therapeutics. TRIM11 expression was validated by Western blot analysis, real-time polymease chain reaction, and immunohistochemical staining. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and Colony formation assays were performed to investigate pancreatic ductal adenocarcinomas (PDAC) cell viability. Mouse xenograft model of PDAC cells was established to verify the role of TRIM11 in vivo. Coimmunoprecipitation was used to identify the reciprocal regulation between TRIM11 and UBE2N. In this study, we found that TRIM11 expression were higher in PDAC cells and tissues. TRIM11 overexpression promotes PDAC cell proliferation in vitro and tumor growth in vivo. Decreased expression of TRIM11 in PDAC patients is associated with decreased UBE2N and increased TAX1BP1 expression. Coimmunoprecipitation established that TRIM11 interacts and colocalizes with UBE2N. Mechanistically, TRIM11 promoted gemcitabine resistance and suppressed ferritinophagy through UBE2N-TAX1BP1 signaling. Our findings identify TRIM11 as a key regulator of TAX1BP1 signaling with a crucial role in ferritinophagy and gemcitabine resistance in PDAC., (© 2021 Wiley Periodicals LLC.)

Genetic alterations in pancreatic tumors can usually be classified in: (1) Mutational activation of oncogenes; (2) Inactivation of tumor suppressor genes; and (3) Inactivation of genome maintenance genes controlling the repair of DNA damage. Endoscopic ultrasound-guided fine-needle aspiration has improved pre-operative diagnosis, but the management of patients with a pancreatic lesion is still challenging. Molecular testing could help mainly in solving these "inconclusive" specimens. The introduction of multi-gene analysis approaches, such as next-generation sequencing, has provided a lot of useful information on the molecular characterization of pancreatic tumors. Different types of pancreatic tumors ( e.g. , pancreatic ductal adenocarcinomas, intraductal papillary mucinous neoplasms, solid pseudopapillary tumors) are characterized by specific molecular alterations. The aim of this review is to summarize the main molecular alterations found in pancreatic tumors., Competing Interests: Conflict-of-interest statement: Authors have nothing to declare., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: Significant modifications have been made to the 8th edition of the American Joint Committee on Cancer (AJCC) distant metastases (M) stage classification for metastatic pancreatic neuroendocrine tumors (PanNETs). We aimed to validate this revised classification among metastatic PanNET patients using the Surveillance, Epidemiology, and End Results database. We further sought to evaluate the feasibility of applying this classification to metastatic pancreatic neuroendocrine carcinoma (PanNEC) and pancreatic ductal adenocarcinoma (PDAC) patients., Methods: Stage IV pancreatic neuroendocrine neoplasm (PanNEN, including G1/G2 PanNET and G3 PanNEC classified according to the World Health Organization [WHO] 2010 grading scheme) and PDAC patients with metastatic disease diagnosed between 2010 and 2015 were identified and restaged according to the revised M stage classification for PanNET. Overall survival (OS) was compared using Kaplan-Meier analysis and log-rank test. Uni- and multivariate Cox regression models were utilized to identify prognostic factors., Results: A total of 1,371 stage IV PanNEN and 634 PDAC patients were included. Among PanNEN patients, liver (75.0%) was the most common metastatic site, followed by distant lymph nodes (8.5%), lung (8.4%), bone (7.3%), and brain (1.0%). The 5-year OS for PanNET patients with M1a, M1b, and M1c stage was 44.15, 53.32, and 19.70%, respectively. However, survival comparison showed no significant difference between M1a and M1b stages among PanNET patients. Similar findings were noted after applying this classification to PanNEC patients. Multivariate analysis showed that the age at diagnosis and the number of distant metastatic sites were independent prognostic factors for metastatic PanNEN patients. Interestingly, excellent survival discrimination by M stage among stage IV PDAC patients was noted (M1a vs. M1b vs. M1c, 5-year OS: 5.42, 2.46, and 0%, respectively)., Conclusion: Our study is the first large sample-based validation of the AJCC 8th M stage classification for PanNET. The revised classification did not effectively stratify metastatic PanNEN patients. However, further study is warranted to validate this classification for PanNET patients according to the WHO 2017 classification. Interestingly, the revised M stage classification might be feasible for PDAC patients with metastatic disease., (© 2019 S. Karger AG, Basel.)

Background: Hepatitis B virus X-interacting protein (HBXIP) is associated with a variety of tumors. The purpose of this study was to investigate the clinicopathological significance of HBXIP expression in pancreatic ductal adenocarcinoma (PDAC) and to explore its potential as a biomarker for PDAC., Methods: Immunohistochemical (IHC) staining was performed on 126 PDAC tissues, 36 paraneoplastic tissues and 22 normal pancreatic tissues. The relationship between high levels of HBXIP expression and pathological features of PDAC patients was evaluated by chi-squared values., Results: The positive rate of HBXIP protein in PDAC tissues was 85.7% (108/126), which was significantly higher than that of adjacent pancreatic tissue (41.7%, 15/36) and normal pancreas (18.2%, 4/22). In addition, strong positive expression of HBXIP was associated with tumor size, positive lymph node metastasis, clinical stage and 80-month overall survival. Patient's age, gender, degree of differentiation, Ki-67 expression index, and calcification were, however, not associated with high levels of HBXIP expression., Conclusions: We present association between HBXIP expression and the pathological features of patients with PDAC., (Copyright © 2018 Elsevier GmbH. All rights reserved.)

Background: To explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC)., Materials and Methods: This single-center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast-enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves., Results: On the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively., Conclusion: Volumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs., (© 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

The unique tumour microenvironment (TM) of pancreatic ductal adenocarcinoma (PDA) including highly desmoplastic ECM and low tumour perfusion supports a considerable barrier for effective delivery of nanomedicines. Effectively modulating PDA microenvironment to enhance tumour drug delivery represents a pinpoint in the field of PDA treatment. In this study, it was the first time that biomimetic nanoparticles, which were designed in the form of erythrocyte membrane-camouflaged PLGA nanoparticles (MNP), were utilized for PDA microenvironment modulation. Cyclopamine (CYC), an inhibitor of Hedgehog pathway that contributed a lot to desmoplastic ECM of PDA, was selected as the model drug and successfully encapsulated into MNP. Advantages of CYC-loaded MNP (CMNP) included favourable biocompatibility, long circulation time, and powerful TM modulation effect. CMNP could effectively deliver CYC to the tumour site, disrupt tumour ECM, increase functional vessels, and improve tumour perfusion significantly. The combination treatment with CMNP and PTX-loaded MNP (PMNP) successfully improved PTX delivery to tumour, resulting in remarkable tumour growth inhibition in vivo. Therefore, biomimetic nanoparticles provide a new strategy for modulating PDA TM and will have great potential to improve the therapeutic effects of nanomedicines for PDA patients.

Objectives: To evaluate the value of dynamic enhancement patterns on contrast-enhanced MR images by adding signal intensity colour mapping (SICM) to differentiate mass-forming focal pancreatitis (MFFP) from pancreatic ductal adenocarcinoma (PDAC)., Methods: Forty-one clinicopathologically proven MFFPs and 144 surgically confirmed PDACs were enrolled. Laboratory and MR imaging parameters were used to differentiate MFFP from PDAC. In particular, enhancement patterns on MR images adding SICM were evaluated. By using classification tree analysis (CTA), we determined the predictors for the differentiation of MFFP from PDAC., Results: In the CTA, with all parameters except enhancement pattern on SICM images, ductal obstruction grade and T1 hypointensity grade of the pancreatic lesion were the first and second splitting predictor for differentiation of MFFP from PDAC, in order. By adding an enhancement pattern on the SICM images to CTA, the enhancement pattern was the only splitting predictor to differentiate MFFP from PDAC. The CTA model including enhancement pattern on SICM images has sensitivity of 78.0 %, specificity of 99.3 %, and accuracy of 94.6 % for differentiating MFFP from PDAC., Conclusion: The characterization of enhancement pattern for pancreatic lesions on contrast-enhanced MR images adding SICM would be helpful to differentiate MFFP from PDAC., Key Points: • SICM was useful to characterize enhancement pattern. • Enhancement pattern on SICM was the only splitting predictor on CTA. • This model may be useful for differentiating MFFP from PDAC.