Investigating the Effects of Olaparib on the Susceptibility of Glioblastoma Multiforme Tumour Cells to Natural Killer Cell-Mediated Responses.

Glioblastoma multiforme (GBM) is the most common adult primary brain malignancy, with dismal survival rates of ~14.6 months. The current standard-of-care consists of surgical resection and chemoradiotherapy, however the treatment response is limited by factors such as tumour heterogeneity, treatment resistance, the blood-brain barrier, and immunosuppression. Several immunotherapies have undergone clinical development for GBM but demonstrated inadequate efficacy, yet future combinatorial approaches are likely to hold more promise. Olaparib is FDA-approved for BRCA-mutated advanced ovarian and breast cancer, and clinical studies have revealed its utility as a safe and efficacious radio- and chemo-sensitiser in GBM. The ability of Olaparib to enhance natural killer (NK) cell-mediated responses has been reported in prostate, breast, and lung cancer. This study examined its potential combination with NK cell therapies in GBM by firstly investigating the susceptibility of the GBM cell line T98G to NK cells and, secondly, examining whether Olaparib can sensitise T98G cells to NK cell-mediated responses. Here, we characterise the NK receptor ligand profile of T98G cells and demonstrate that Olaparib does not dampen T98G susceptibility to NK cells or elicit immunomodulatory effects on the function of NK cells. This study provides novel insights into the potential combination of Olaparib with NK cell therapies for GBM.

The Influence of MicroRNA-31 on Oxidative Stress and Radiosensitivity in Pancreatic Ductal Adenocarcinoma.

Radioresistance remains a significant challenge in treating pancreatic ductal adenocarcinoma (PDAC), contributing to the poor survival rates of this cancer. MicroRNAs (miRs) are small non-coding RNA molecules that may play an essential role in regulating radioresistance by altering the levels of oxidative stress. In this study, we investigated the role and potential mechanisms linking miR-31 to PDAC radioresistance. A pCMV-miR vector containing a miR-31 mimic was stably expressed into a miR-31-deficient PDAC cell line, BxPC-3. Additionally, a pmiRZip lentivector suppressing miR-31 was stably expressed in a miR-31 abundant PDAC cell line, Panc-1. Clonogenic assays were conducted to explore the role of miR-31 manipulation on radiosensitivity. Fluorometric ROS assays were performed to quantify ROS levels. The expression of potential miR-31 targets was measured by Western blot analysis. It was found that the manipulation of miR-31 altered the radiosensitivity in PDAC cells by regulating oxidative stress. Using online bioinformatics tools, we identified the 3'UTR of GPx8 as a predicted target of miR-31. Our study demonstrates, for the first time, that manipulating miR-31 alters GPx8 expression, regulating ROS detoxification and promoting either a radioresistant or radiosensitive phenotype. MiR-31 may represent a promising therapeutic target for altering radiosensitivity in PDAC cells.

Diagnostic Accuracy of Blood-based Biomarkers for Pancreatic Cancer: A Systematic Review and Meta-analysis.

Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate below 5%. Carbohydrate antigen 19-9 (CA19-9) is the most commonly used blood-based biomarker for PDAC in current clinical practice, despite having been shown repeatedly to be inaccurate and have poor diagnostic performance. This review aims to assess the reported diagnostic accuracy of all blood-based biomarkers investigated to date in PDAC, by directly comparing individual biomarkers and multi-biomarker panels, both containing CA19-9 and not (novel). A systematic review was conducted in accordance with PRISMA standards in July 2020. Individualized search strategies for three academic databases identified 5,885 studies between the years 1973 and 2020. After two rounds of screening, 250 studies were included. Data were extracted and assessed for bias. A multivariate three-level meta-analysis with subgroup moderators was run in R using AUC values as effect size. On the basis of this model, the pooled AUC value for all multi-biomarker panels (AUC = 0.898; 95% confidence interval (CI): 0.88-0.91) was significantly higher than all single biomarkers (AUC = 0.803; 95% CI: 0.78-0.83; P < 0.0001). The pooled AUC value for CA19-9 alone was significantly lower compared with the multi-biomarker panels containing CA19-9 (P < 0.0001). For the novel biomarkers, the pooled AUC for single biomarkers was also significantly lower compared with multi-biomarker panels (P < 0.0001). Novel biomarkers that have been repeatedly examined across the literature, such as TIMP-1, CEA, and CA125, are highlighted as promising. These results suggest that CA19-9 may be best used as an addition to a panel of biomarkers rather than alone, and that multi-biomarker panels generate the most robust results in blood-based PDAC diagnosis. Significance: In a systematic review and three-level multivariate meta-analysis, it is shown for the first time that blood-based multi-biomarker panels for the diagnosis of PDAC exhibit superior performance in comparison with single biomarkers. CA19-9 is demonstrated to have limited utility alone, and to perform poorly in patient control cohorts of both healthy and benign individuals. Multi-biomarker panels containing CA19-9 produce the best diagnostic performance overall.

Multi-Omic Biomarkers as Potential Tools for the Characterisation of Pancreatic Cystic Lesions and Cancer: Innovative Patient Data Integration.

Pancreatic cancer (PC) is regarded as one of the most lethal malignant diseases in the world, with GLOBOCAN 2020 estimates indicating that PC was responsible for almost half a million deaths worldwide in 2020. Pancreatic cystic lesions (PCLs) are fluid-filled structures found within or on the surface of the pancreas, which can either be pre-malignant or have no malignant potential. While some PCLs are found in symptomatic patients, nowadays many PCLs are found incidentally in patients undergoing cross-sectional imaging for other reasons-so called 'incidentalomas'. Current methods of characterising PCLs are imperfect and vary hugely between institutions and countries. As such, there is a profound need for improved diagnostic algorithms. This could facilitate more accurate risk stratification of those PCLs that have malignant potential and reduce unnecessary surveillance. As PC continues to have such a poor prognosis, earlier recognition and risk stratification of PCLs may lead to better treatment protocols. This review will focus on the importance of biomarkers in the context of PCLs and PCand outline how current 'omics'-related work could contribute to the identification of a novel integrated biomarker profile for the risk stratification of patients with PCLs and PC.