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.

The SAVE-MORE trial demonstrated that anakinra treatment in COVID-19 pneumonia with plasma soluble urokinase plasminogen activator (suPAR) levels of 6 ng/mL or more was associated with 0.36 odds for a worse outcome compared to placebo when expressed by the WHO-Clinical Progression Scale (CPS) at day 28. Herein, we report the results of subgroup analyses and long-term outcomes. This prospective, double-blind, randomised clinical trial, recruited patients with a confirmed SARS-CoV-2 infection, in need of hospitalisation, lower respiratory tract infection and plasma suPAR ≥6 ng/mL from 37 academic and community hospitals in Greece and Italy. Patients were 1:2 randomised to subcutaneous treatment with placebo or anakinra (100 mg) once daily for 10 days. Pre-defined subgroups of Charlson's comorbidity index (CCI), sex, age, level of suPAR, and time from symptom onset were analysed for the primary endpoint (overall comparison of distribution of frequencies of the scores from the WHO-CPS between treatments on day 28), by multivariable ordinal regression analysis in the intention to treat (ITT) population. This trial is registered with the EU Clinical Trials Register (2020-005828-11) and ClinicalTrials.gov (NCT04680949). Patients were enrolled between 23 December 2020 and 31 March 2021; 189 patients in the placebo arm and 405 patients in the anakinra arm were the ITT population. Multivariable analysis showed that anakinra treatment was accompanied by significantly lower odds for worse outcome compared to placebo at day 28 for all studied subgroups (CCI ≥ 2, OR: 0.34, 95% confidence intervals [CI] 0.22-0.50; CCI < 2, OR: 0.38, 95% CI 0.21-0.68; suPAR > 9 ng/mL, OR: 0.35, 95% CI 0.19-0.66; suPAR 6-9 ng/mL, OR: 0.35, 95% CI 0.24-0.52; patients ≥65 years, OR: 0.41, 95% CI 0.25-0.66; and patients <65 years, OR: 0.29, 95% CI 0.19-0.45). The benefit was uniform, irrespective of the time from start of symptoms until the start of the study drug. At days 60 and 90, anakinra treatment had odds of 0.40 (95% CI 0.28-0.57) and 0.46 (95% CI 0.32-0.67) respectively, for a worse outcome compared to placebo. The costs of general ward stay, ICU stay, and drugs were lower with anakinra treatment. Anakinra represents an important therapeutic tool in the management of COVID-19 that may be administered in all subgroups of patients; benefits are maintained until day 90. Hellenic Institute for the Study of Sepsis; Swedish Orphan Biovitrum AB.

Pancreatic cancer is one of the most aggressive cancers with a very poor 5-year survival rate and reduced therapeutic options when diagnosed in an advanced stage. The dismal prognosis of pancreatic cancer has guided significant efforts to discover novel biomarkers in order to anticipate diagnosis, increasing the population of patients who can benefit from curative surgical treatment. CA 19-9 is the reference biomarker that supports the diagnosis and guides the response to treatments. However, it has significant limitations, a low specificity, and is inefficient as a screening tool. Several potential biomarkers have been discovered in the serum, urine, feces, and pancreatic juice of patients. However, most of this evidence needs further validation in larger cohorts. The advent of advanced omics sciences and liquid biopsy techniques has further enhanced this field of research. The aim of this review is to analyze the historical evolution of the research on novel biomarkers for the early diagnosis of pancreatic cancer, focusing on the current evidence for the most promising biomarkers from different body fluids and the novel trends in research, such as omics sciences and liquid biopsy, in order to favor the application of modern personalized medicine.

(1) Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. The lack of validated disease biomarkers makes timely diagnosis challenging in most cases. Cell membrane and surface proteins play a crucial role in several routes of oncogenesis. The aim of this study was to evaluate the expression of six membrane antigens on PDAC (CA 19-9, mucin 1 and 4 (MUC1, MUC4), mesothelin (MSLN), Annexin A10 (ANXA10), Glypican-1 (GPC-1)) and their correlation with oncologic outcomes. (2) Methods: Immunohistochemical staining for CA 19.9, MUC1, MUC4, MSLN, ANXA10, and GPC-1 of surgical samples of 50 consecutive patients with PDAC was performed. Antigen expression for tumor, ductal, and acinar tissues was classified according to the histo-score (H-score) by two pathologists. (3) Results: Recurrence rate was 47% and 18 patients (36%) deceased (median follow-up 21.5 months). Immunostaining for CA 19-9 and MUC1 showed a significantly higher expression in the neoplastic tissue compared to non-tumor ductal and acinar tissues (p < 0.001). MUC4, MSLN, ANXA10, and GPC-1 were selectively expressed in the neoplastic tissue (p < 0.001). A CA 19-9 H-score value >270 was independently associated with a worse overall survival (p = 0.05) and disease-free survival (p = 0.05). (4) Conclusions: CA 19-9 and MUC1 are highly expressed in PDAC cells. The histological expression of CA 19-9 may predict prognosis. MUC4, MSLN, ANXA10, and GPC-1 are selectively expressed by neoplastic tissue and may represent a potential histological biomarker of disease.

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host–microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.