A Single-Molecule Bioelectronic Portable Array for Early Diagnosis of Pancreatic Cancer Precursors

A cohort of 47 patients is screened for pancreatic cancer precursors with a portable 96-well bioelectronic sensing-array for single-molecule assay in cysts fluid and blood plasma, deployable at point-of-care (POC). Pancreatic cancer precursors are mucinous cysts diagnosed with a sensitivity of at most 80% by state-of-the-art cytopathological molecular analyses (e.g., KRASmut DNA). Adding the simultaneous assay of proteins related to malignant transformation (e.g., MUC1 and CD55) is deemed essential to enhance diagnostic accuracy. The bioelectronic array proposed here, based on single-molecule-with-a-large-transistor (SiMoT) technology, can assay both nucleic acids and proteins at the single-molecule limit-of-identification (LOI) (1% of false-positives and false-negatives). It comprises an enzyme-linked immunosorbent assay (ELISA)-like 8 × 12-array organic-electronics disposable cartridge with an electrolyte-gated organic transistor sensor array, and a reusable reader, integrating a custom Si-IC chip, operating via software installed on a USB-connected smart device. The cartridge is complemented by a 3D-printed sensing gate cover plate. KRASmut, MUC1, and CD55 biomarkers either in plasma or cysts-fluid from 5 to 6 patients at a time, are multiplexed at single-molecule LOI in 1.5 h. The pancreatic cancer precursors are classified via a machine-learning analysis resulting in at least 96% diagnostic-sensitivity and 100% diagnostic-specificity. This preliminary study opens the way to POC liquid-biopsy-based early diagnosis of pancreatic-cancer precursors in plasma.</p

A Single-Molecule Bioelectronic Portable Array for Early Diagnosis of Pancreatic Cancer Precursors

A cohort of 47 patients is screened for pancreatic cancer precursors with a portable 96-well bioelectronic sensing-array for single-molecule assay in cysts fluid and blood plasma, deployable at point-of-care (POC). Pancreatic cancer precursors are mucinous cysts diagnosed with a sensitivity of at most 80% by state-of-the-art cytopathological molecular analyses (e.g., KRASmut DNA). Adding the simultaneous assay of proteins related to malignant transformation (e.g., MUC1 and CD55) is deemed essential to enhance diagnostic accuracy. The bioelectronic array proposed here, based on single-molecule-with-a-large-transistor (SiMoT) technology, can assay both nucleic acids and proteins at the single-molecule limit-of-identification (LOI) (1% of false-positives and false-negatives). It comprises an enzyme-linked immunosorbent assay (ELISA)-like 8 × 12-array organic-electronics disposable cartridge with an electrolyte-gated organic transistor sensor array, and a reusable reader, integrating a custom Si-IC chip, operating via software installed on a USB-connected smart device. The cartridge is complemented by a 3D-printed sensing gate cover plate. KRASmut, MUC1, and CD55 biomarkers either in plasma or cysts-fluid from 5 to 6 patients at a time, are multiplexed at single-molecule LOI in 1.5 h. The pancreatic cancer precursors are classified via a machine-learning analysis resulting in at least 96% diagnostic-sensitivity and 100% diagnostic-specificity. This preliminary study opens the way to POC liquid-biopsy-based early diagnosis of pancreatic-cancer precursors in plasma.</p

Tumor Microenvironment in Pancreatic Intraepithelial Neoplasia

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

Pancreatic ductal adenocarcinoma concomitant with pancreatic metastases of clear-cell renal cell carcinoma: a case report

Background!#!Metastatic spread to the pancreas is a rare event. Renal cell carcinoma represents one possible site of origin of pancreatic metastases. Renal cell carcinoma often metastasizes late and exclusively to the pancreas, suggesting a special role of renal cell carcinoma among primaries metastasizing to the pancreas. Even rarer, renal cell carcinoma may occur simultaneously with pancreatic ductal adenocarcinoma.!##!Case presentation!#!We present the case of a 78-year-old male Caucasian patient with a history of clear-cell renal cell carcinoma treated with oncological left nephrectomy 20 years before. The patient was diagnosed with pancreatic ductal adenocarcinoma by fine-needle aspiration cytology. At our institution, he received neoadjuvant therapy with folic acid, fluorouracil, irinotecan, oxaliplatin for borderline-resectable pancreatic ductal adenocarcinoma, and subsequently underwent total pancreatectomy. Upon resection, pancreatic ductal adenocarcinoma as well as two metachronous metastases of clear-cell renal cell carcinoma occurring simultaneously and cospatially with pancreatic ductal adenocarcinoma were diagnosed in the pancreatic body.!##!Conclusions!#!Renal cell carcinoma metastases of the pancreas are rare and often occur decades after the initial diagnosis of renal cell carcinoma. The combination of renal cell carcinoma metastases and pancreatic ductal adenocarcinoma is even rarer. However, the possibility should be considered by clinicians, radiologists, and pathologists. The special role of renal cell carcinoma as a site of origin of pancreatic metastasis should be further elucidated

Genetic Alterations Predict Long-Term Survival in Ductal Adenocarcinoma of the Pancreatic Head

Background: Survival of patients with adenocarcinoma of the pancreas (PDAC) is poor and has remained almost unchanged over the past decades. The genomic landscape of PDAC has been characterized in recent years. The aim of this study was to identify a genetic profile as a possible predictor of prolonged survival in order to tailor therapy for PDAC patients. Methods: Panel next generation sequencing (NGS) and immunohistochemistry (IHC) were performed on paraffin-embedded tumor tissues from curatively treated PDAC patients. Tumor slides were re-evaluated with a focus on the histomorphology. Patients were subgrouped according to short and long overall (4 years) and disease-free (2 years) survival. Results: Thirty-nine patients were included in the study. Clinicopathological staging variables as well as the histomorphological subgroups were homogenously distributed between short- and long-term overall and disease-free survivors. In survival analysis, patients with the KRAS G12D mutation and patients with TP53 nonsense and splice-site mutations had a significantly worse overall survival (OS) and disease-free survival (DFS). Patients with long-term OS and DFS showed no KRAS G12D, no TP53 nonsense or splice-site mutations. Rare Q61H/D57N KRAS mutations were only found in long-term survivors. The allele frequency rate of KRAS and TP53 mutations in tumor cells was significantly higher in short-term disease-free survivors and overall survivors, respectively. Conclusions: NGS of PDAC revealed significant differences in survival outcome in a patient collective with homogenously distributed clinicopathological variables. Further multi-institutional studies are warranted to identify more long-term survivors to detect genetic differences suitable for targeted therapy

Stroma composition and proliferative activity are related to therapy response in neoadjuvant treated pancreatic ductal adenocarcinoma

Background. Tumor regression grading (TRG) based on histopathology is the main tool to assess therapy effects after neoadjuvant therapy (NAT) of pancreatic ductal adenocarcinoma (PDAC). However, reliable markers to distinguish therapy effects from preexisting tumor features are lacking. The aim of this study was the characterization of PDAC after NAT, focusing on the stroma. Material and Methods. Tissue samples from patients resected for PDAC after NAT (n=27) were analyzed. TRG was assessed using the Royal North Shore (RNS) system. Stromal composition was evaluated by Movat's stain. Immunohistochemistry (IH) for Ki-67 and five previously established stroma markers (alpha-Crystallin B, alpha-Smooth muscle actin (alpha-SMA), Neurotrophin-3 (NT-3), SPARC and Tenascin C) was also performed. Results were compared with therapynaïve PDACs (n=10). Results. Most cases showed a moderate response (RNS 2; 74%), while 15% displayed a poor response (RNS 3), and 11% a good response (RNS 1). No complete response was observed. Poor regression was associated with mucin-rich stroma, while good regression was associated with collagen-rich stroma. Cases with poorer therapy response had significantly higher proliferation. Higher peritumoral staining intensity for alpha-SMA and Tenascin C also showed a trend towards an association with poor regression. Conclusions. Similar to the stroma in therapy-naïve PDAC, the stroma of PDAC after NAT is heterogeneous. Distinguishing between desmoplastic stroma and therapy-induced fibrosis by single markers is not possible. Movat's pentachrome stain, IH for Ki-67, and to some extent for Tenascin C and alpha-SMA, can help detect poor histopathological response to NAT

Neoadjuvant Treatment Lowers the Risk of Mesopancreatic Fat Infiltration and Local Recurrence in Patients with Pancreatic Cancer

Background: Survival following surgical treatment of ductal adenocarcinoma of the pancreas (PDAC) remains poor. The recent implementation of the circumferential resection margin (CRM) into standard histopathological evaluation lead to a significant reduction in R0 rates. Mesopancreatic fat infiltration is present in ~80% of PDAC patients at the time of primary surgery and recently, mesopancreatic excision (MPE) was correlated to complete resection. To attain an even higher rate of R0(CRM−) resections in the future, neoadjuvant therapy in patients with a progressive disease seems a promising tool. We analyzed radiographic and histopathological treatment response and mesopancreatic tumor infiltration in patients who received neoadjuvant therapy prior to MPE. The aim of our study was to evaluate the need for MPE following neoadjuvant therapy and if multi-detector computed tomographically (MDCT) evaluated treatment response correlates with mesopancreatic (MP) infiltration. Method: Radiographic, clinicopathological and survival parameters of 27 consecutive patients who underwent neoadjuvant therapy prior to MPE were evaluated. The mesopancreatic fat tissue was histopathologically analyzed and the 1 mm-rule (CRM) was applied. Results: In the study collective, both the rate of R0 resection R0(CRM−) and the rate of mesopancreatic fat infiltration was 62.9%. Patients with MP infiltration showed a lower tumor response. Surgical resection status was dependent on MP infiltration and tumor response status. Patients with MDCT-predicted tumor response were less prone to MP infiltration. When compared to patients after upfront surgery, MP infiltration and local recurrence rate was significantly lower after neoadjuvant treatment. Conclusion: MPE remains warranted after neoadjuvant therapy. Mesopancreatic fat invasion was still evident in the majority of our patients following neoadjuvant treatment. MDCT-predicted tumor response did not exclude mesopancreatic fat infiltration

Clinicopathological and Prognostic Value of Survivin Expression in Surgically Resected Pancreatic Ductal Adenocarcinoma

Background: Survival after surgery for pancreatic ductal adenocarcinoma (PDAC) remains poor. Thus, novel therapeutic concepts focus on the development of targeted therapies. In this context, inhibitor of apoptosis protein (IAP) survivin is regarded as a promising oncotherapeutic target. However, its expression and prognostic value in different tumour compartments of PDAC have not been studied. Methods: Immunohistochemical analysis of survivin in different PDAC tumour compartments from 236 consecutive patients was correlated with clinicopathological variables and survival. Results: In comparison to healthy pancreatic tissue high nuclear (p p p = 0.016) and UICC III/IV stages (p = 0.009), while high cytoplasmic expression at the invasion front grouped with venous infiltration (p = 0.022). Increased nuclear survivin along the invasion front correlated with perineural invasion (p = 0.035). High nuclear survivin in tumour centres represented an independent prognostic factor for overall survival of pancreatic tail carcinomas (HR 13.5 95%CI (1.4–129.7)) and correlated with a limited disease-free survival in PDAC (HR 1.80 95%CI (1.04–3.12)). Conclusion: Survivin is associated with advanced disease stages and poor prognosis. Therefore, survivin will help to identify patients with aggressive tumour phenotypes that could benefit from the inclusion in clinical trials incorporating survivin inhibitors in PDAC

Communicator-Driven Data Preprocessing Improves Deep Transfer Learning of Histopathological Prediction of Pancreatic Ductal Adenocarcinoma

Pancreatic cancer is a fatal malignancy with poor prognosis and limited treatment options. Early detection in primary and secondary locations is critical, but fraught with challenges. While digital pathology can assist with the classification of histopathological images, the training of such networks always relies on a ground truth, which is frequently compromised as tissue sections contain several types of tissue entities. Here we show that pancreatic cancer can be detected on hematoxylin and eosin (H&E) sections by convolutional neural networks using deep transfer learning. To improve the ground truth, we describe a preprocessing data clean-up process using two communicators that were generated through existing and new datasets. Specifically, the communicators moved image tiles containing adipose tissue and background to a new data class. Hence, the original dataset exhibited improved labeling and, consequently, a higher ground truth accuracy. Deep transfer learning of a ResNet18 network resulted in a five-class accuracy of about 94% on test data images. The network was validated with independent tissue sections composed of healthy pancreatic tissue, pancreatic ductal adenocarcinoma, and pancreatic cancer lymph node metastases. The screening of different models and hyperparameter fine tuning were performed to optimize the performance with the independent tissue sections. Taken together, we introduce a step of data preprocessing via communicators as a means of improving the ground truth during deep transfer learning and hyperparameter tuning to identify pancreatic ductal adenocarcinoma primary tumors and metastases in histological tissue sections