pancreatic intraepithelial neoplasia
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Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6188
Author(s):  
Friederike V. Opitz ◽  
Lena Haeberle ◽  
Alexandra Daum ◽  
Irene Esposito

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors with a poor prognosis. A characteristic of PDAC is the formation of an immunosuppressive tumor microenvironment (TME) that facilitates bypassing of the immune surveillance. The TME consists of a desmoplastic stroma, largely composed of cancer-associated fibroblasts (CAFs), immunosuppressive immune cells, immunoregulatory soluble factors, neural network cells, and endothelial cells with complex interactions. PDAC develops from various precursor lesions such as pancreatic intraepithelial neoplasia (PanIN), intraductal papillary mucinous neoplasms (IPMN), mucinous cystic neoplasms (MCN), and possibly, atypical flat lesions (AFL). In this review, we focus on the composition of the TME in PanINs to reveal detailed insights into the complex restructuring of the TME at early time points in PDAC progression and to explore ways of modifying the TME to slow or even halt tumor progression.


2021 ◽  
Vol 99 (1) ◽  
pp. 146-148
Author(s):  
Hironori Tanei ◽  
Reina Tanaka ◽  
Takayoshi Tsuchiya ◽  
Kentaro Ishii ◽  
Ryosuke Tonozuka ◽  
...  

2021 ◽  
Vol 4 (8) ◽  
pp. e202000979
Author(s):  
Hemanth Kumar Kandikattu ◽  
Murli Manohar ◽  
Alok Kumar Verma ◽  
Sandeep Kumar ◽  
Chandra Sekhar Yadavalli ◽  
...  

Reports indicate that accumulated macrophages in the pancreas are responsible for promoting the pathogenesis of chronic pancreatitis (CP). Recently, macrophage-secreted cytokines have been implicated in promoting pancreatic acinar-to-ductal metaplasia (ADM). This study aims to establish the role of accumulated macrophage-activated NLRP3-IL-18-eosinophil mechanistic pathway in promoting several characteristics of pancreatic malignancy in CP. We report that in a murine model of pancreatic cancer (PC), accumulated macrophages are the source of NLRP3-regulated IL-18, which promotes eosinophilic inflammation-mediated accumulation to periductal mucin and collagen, including the formation of ADM, pancreatic intraepithelial neoplasia (PanINs), and intraductal papillary mucinous neoplasm. Most importantly, we show improved malignant characteristics with reduced levels of oncogenes in an anti–IL-18 neutralized and IL-18 gene deficient murine model of CP. Last, human biopsies validated that NLRP3-IL-18–induced eosinophils accumulate near the ducts, showing PanINs formation in PC. Taken together, we present the evidence on the role of IL-18–induced eosinophilia in the development of PC phenotype like ADM, PanINs, and ductal cell differentiation in inflammation-induced CP.


2021 ◽  
Vol 22 (10) ◽  
pp. 5385
Author(s):  
Mark Kriegsmann ◽  
Katharina Kriegsmann ◽  
Georg Steinbuss ◽  
Christiane Zgorzelski ◽  
Anne Kraft ◽  
...  

Identification of pancreatic ductal adenocarcinoma (PDAC) and precursor lesions in histological tissue slides can be challenging and elaborate, especially due to tumor heterogeneity. Thus, supportive tools for the identification of anatomical and pathological tissue structures are desired. Deep learning methods recently emerged, which classify histological structures into image categories with high accuracy. However, to date, only a limited number of classes and patients have been included in histopathological studies. In this study, scanned histopathological tissue slides from tissue microarrays of PDAC patients (n = 201, image patches n = 81.165) were extracted and assigned to a training, validation, and test set. With these patches, we implemented a convolutional neuronal network, established quality control measures and a method to interpret the model, and implemented a workflow for whole tissue slides. An optimized EfficientNet algorithm achieved high accuracies that allowed automatically localizing and quantifying tissue categories including pancreatic intraepithelial neoplasia and PDAC in whole tissue slides. SmoothGrad heatmaps allowed explaining image classification results. This is the first study that utilizes deep learning for automatic identification of different anatomical tissue structures and diseases on histopathological images of pancreatic tissue specimens. The proposed approach is a valuable tool to support routine diagnostic review and pancreatic cancer research.


Gut ◽  
2021 ◽  
pp. gutjnl-2020-321112
Author(s):  
Dror Kolodkin-Gal ◽  
Lior Roitman ◽  
Yossi Ovadya ◽  
Narmen Azazmeh ◽  
Benjamin Assouline ◽  
...  

ObjectiveCellular senescence limits tumourigenesis by blocking the proliferation of premalignant cells. Additionally, however, senescent cells can exert paracrine effects influencing tumour growth. Senescent cells are present in premalignant pancreatic intraepithelial neoplasia (PanIN) lesions, yet their effects on the disease are poorly characterised. It is currently unknown whether senolytic drugs, aimed at eliminating senescent cells from lesions, could be beneficial in blocking tumour development.DesignTo uncover the functions of senescent cells and their potential contribution to early pancreatic tumourigenesis, we isolated and characterised senescent cells from PanINs formed in a Kras-driven mouse model, and tested the consequences of their targeted elimination through senolytic treatment.ResultsWe found that senescent PanIN cells exert a tumour-promoting effect through expression of a proinflammatory signature that includes high Cox2 levels. Senolytic treatment with the Bcl2-family inhibitor ABT-737 eliminated Cox2-expressing senescent cells, and an intermittent short-duration treatment course dramatically reduced PanIN development and progression to pancreatic ductal adenocarcinoma.ConclusionsThese findings reveal that senescent PanIN cells support tumour growth and progression, and provide a first indication that elimination of senescent cells may be effective as preventive therapy for the progression of precancerous lesions.


2021 ◽  
Vol 18 (2) ◽  
pp. 133-146
Author(s):  
MASARU TERASAKI ◽  
TAKUYA INOUE ◽  
WATARU MURASE ◽  
ATSUHITO KUBOTA ◽  
HIROYUKI KOJIMA ◽  
...  

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