MIR-203A-3P AND MMP-2 PROTEINS ARE HIGHLY EXPRESSED IN CIRCULATING TUMOR CELLS FROM PATIENTS WITH PANCREATIC CARCINOMA

OBJECTIVES

Ductal adenocarcinoma of the pancreas is the fourth most common cancer-associated cause of death in the Western world. The presence of circulating tumor cells (CTCs) can be considered a potential prognostic factor, as these cells represent tumor progression, allowing monitoring of therapeutic efficacy. The objectives of this study were to explore the morphological, molecular, and phenotypic characteristics of CTCs from the blood of patients with pancreatic carcinoma and to correlate the findings with response to treatment, progression-free survival, overall survival (OS), and deep vein thrombosis (DVT).

METHODS

Peripheral blood (10 mL) was analyzed before the beginning of treatment after 60 and 120 days. CTCs were detected by using ISET® and characterized by immunocytochemistry. For microRNAs (miRNAs) analysis, peripheral leukocytes from the same patients and healthy individuals (controls) were collected in parallel at baseline. The expression of miRNAs was evaluated (in pool) using TaqMan® Array Human MicroRNA Cards v2.0.

RESULTS

Only nine patients were included. The proteins, namely, matrix metalloproteinase-2 (MMP2) and TGFβ-RI, were highly expressed (77.7%) in CTCs at baseline; at the first follow-up, MMP2 was predominant (80%) and, at the second follow-up, MMP2 and vimentin were predominant (50%). Circulating tumor microemboli (CTMs) were found in two patients and both presented DVT. The miR-203a-3p was highly expressed in CTCs. The miR-203a-3p is involved in the stimulation of epithelial-to-mesenchymal transition (EMT) and is related to worse OS in pancreatic cancer (TCGA data).

CONCLUSION

Due to the low number of patients and short follow-up, we did not observe a correlation between CTCs and response to treatment. However, there was a correlation between CTM and DVT and also miR-203a-3p was highly expressed in CTCs, corroborating the findings of EMT proteins. This study opens the perspectives concerning the dynamic change in the pattern of proteins expressed along with treatment and the use of miRNAs as new targets in pancreatic carcinoma.

Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans

The increasing incidence of pancreatic cancer will make it the second deadliest cancer in 2030. Imaging based early diagnosis and image guided treatment are emerging potential solutions. Artificial intelligence (AI) can help provide and improve widespread diagnostic expertise and accurate interventional image interpretation. Accurate segmentation of the pancreas is essential to create annotated data sets to train AI, and for computer assisted interventional guidance. Automated deep learning segmentation performance in pancreas computed tomography (CT) imaging is low due to poor grey value contrast and complex anatomy. A good solution seemed a recent interactive deep learning segmentation framework for brain CT that helped strongly improve initial automated segmentation with minimal user input. This method yielded no satisfactory results for pancreas CT, possibly due to a sub-optimal neural network architecture. We hypothesize that a state-of-the-art U-net neural network architecture is better because it can produce a better initial segmentation and is likely to be extended to work in a similar interactive approach. We implemented the existing interactive method, iFCN, and developed an interactive version of U-net method we call iUnet. The iUnet is fully trained to produce the best possible initial segmentation. In interactive mode it is additionally trained on a partial set of layers on user generated scribbles. We compare initial segmentation performance of iFCN and iUnet on a 100CT dataset using dice similarity coefficient analysis. Secondly, we assessed the performance gain in interactive use with three observers on segmentation quality and time. Average automated baseline performance was 78% (iUnet) versus 72% (FCN). Manual and semi-automatic segmentation performance was: 87% in 15 min. for manual, and 86% in 8 min. for iUNet. We conclude that iUnet provides a better baseline than iFCN and can reach expert manual performance significantly faster than manual segmentation in case of pancreas CT. Our novel iUnet architecture is modality and organ agnostic and can be a potential novel solution for semi-automatic medical imaging segmentation in general.

The hepatic pre-metastatic niche in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive malignancies to date, largely because it is associated with high metastatic risk. Pancreatic tumors have a characteristic tendency to metastasize preferentially to the liver. Over the past two decades, it has become evident that the otherwise hostile milieu of the liver is selectively preconditioned at an early stage to render it more conducive to the engraftment and growth of disseminated cancer cells, a concept defined as pre-metastatic niche (PMN) formation. Pancreatic cancer cells exploit components of the tumor microenvironment to facilitate their migration out of the primary tumor, which often involves conversion of pancreatic cancer cells from an epithelial to a mesenchymal phenotype via the epithelial-to-mesenchymal transition. Pancreatic stellate cells and matrix stiffness have been put forward as major drivers of invasiveness in PDAC. Even before the onset of pancreatic cancer cell dissemination, soluble factors and extracellular vesicles secreted by the primary tumor, and possibly even premalignant lesions, help shape a supportive niche in the liver by providing vascular docking sites for circulating tumor cells, enhancing vascular permeability, remodeling the extracellular matrix and recruiting immunosuppressive inflammatory cells. Emerging evidence suggests that some of these tumor-derived factors may represent powerful diagnostic or prognostic biomarkers. Though our understanding of the mechanisms driving PMN formation in PDAC has expanded considerably, many outstanding questions and challenges remain. Further studies dissecting the molecular and cellular events involved in hepatic PMN formation in PDAC will likely improve diagnosis and open new avenues from a therapeutic standpoint.