Early detection and imaging strategies to reveal and target developing pancreatic cancer

Pharmacological mechanisms underlying the interaction of the nucleoside analogue gemcitabine with the c-MET inhibitor tivantinib in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with limited treatment options, highlighting the urgent need for innovative approaches. A promising target for new anticancer therapies across various tumor types is the receptor tyrosine kinase c-MET. Here, we examined the impact of the c-MET inhibitor tivantinib in combination with gemcitabine on both primary and immortalized PDAC cells, and we investigated the mechanism underlying this combined treatment's effects. Our findings demonstrate that tivantinib is synergistic with gemcitabine, which is not related to cytidine deaminase but to inhibition of the polymerization of tubulin. Moreover, these drugs affected the expression of microRNAs miR-21 and miR-34, which regulate key oncogenic pathways. These findings might have an impact on the selection of patients for future trials.

Roles of differently polarized macrophages in the initiation and progressionof pancreatic cancer

During development of pancreatic cancer macrophage-mediated inflammatory processes and the formation of cancerous lesions are tightly connected. Based on insight from mouse models we provide an overview on the functions of classically-activated pro-inflammatory and alternatively-activated anti-inflammatory macrophages in the initiation and progression of pancreatic cancer. We highlight their roles in earliest events of tumor initiation such as acinar-to-ductal metaplasia (ADM), organization of the fibrotic lesion microenvironment, and growth of low-grade (LG) lesions. We then discuss their roles as tumor-associated macrophages (TAM) in progression to high-grade (HG) lesions with a cancerous invasive phenotype and an immunosuppressive microenvironment. Another focus is on how targeting these macrophage populations can affect immunosuppression, fibrosis and responses to chemotherapy, and eventually how this knowledge could be used for novel therapy approaches for patients with pancreatic ductal adenocarcinoma (PDA).

Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma

PURPOSE

We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.

RESULTS

A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls.

CONCLUSION

Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.

Diagnostic and Prognostic Role of Extracellular Vesicles in Pancreatic Cancer: Current Evidence and Future Perspectives

Pancreatic cancer is one of the most aggressive tumors, with a dismal prognosis due to poor detection rates at early stages, rapid progression, post-surgical complications, and limited effectiveness of conventional oncologic therapies. There are no consistently reliable biomarkers or imaging modalities to accurately diagnose, classify, and predict the biological behavior of this tumor. Therefore, it is imperative to develop new and improved strategies to detect pancreatic lesions in the early stages of cancerization with greater sensitivity and specificity. Extracellular vesicles, including exosome and microvesicles, are membrane-coated cellular products that are released in the outer environment. All cells produce extracellular vesicles; however, this process is enhanced by inflammation and tumorigenesis. Based on accumulating evidence, extracellular vesicles play a crucial role in pancreatic cancer progression and chemoresistance. Moreover, they may represent potential biomarkers and promising therapy targets. The aim of the present review is to review the current evidence on the role of extracellular vesicles in pancreatic cancer.

Prognostic value of eight immune gene signatures in pancreatic cancer patients

BACKGROUND

Pancreatic cancer is one of the most common malignant tumors of the digestive tract, and it has a poor prognosis. Traditional methods are not effective to accurately assess the prognosis of patients with pancreatic cancer. Immunotherapy is a new promising approach for the treatment of pancreatic cancer; however, some patients do not respond well to immunotherapy, which may be related to tumor microenvironment regulation. In this study, we use gene expression database to mine important immune genes and establish a prognostic prediction model for pancreatic cancer patients. We hope to provide a feasible method to evaluate the prognosis of pancreatic cancer and provide valuable targets for pancreatic cancer immunotherapy.

RESULTS

We used univariate COX proportional hazard regression analysis, the least absolute shrinkage and selection operator, and multivariate COX regression analysis to screen 8 genes related to prognosis from the 314 immune-related genes, and used them to construct a new clinical prediction model in the TCGA pancreatic cancer cohort. Subsequently, we evaluated the prognostic value of the model. The Kaplan-Meier cumulative curve showed that patients with low risk scores survived significantly longer than patients with high risk scores. The area under the ROC curve (AUC value) of the risk score was 0.755. The univariate COX analysis showed that the risk score was significantly related to overall survival (HR 1.406, 95% CI 1.237-1.598, P < 0.001), and multivariate analysis showed that the risk score was an independent prognostic factor (HR 1.400, 95% CI 1.287-1.522, P < 0.001). Correlation analysis found that immune genes are closely related to tumor immune microenvironment.

CONCLUSIONS

Based on the TCGA-PAAD cohort, we identified immune-related markers with independent prognostic significance, validated, and analyzed their biological functions, to provide a feasible method for the prognosis of pancreatic cancer and provide potentially valuable targets for pancreatic cancer immunotherapy.