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Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost‐effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine‐learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large‐scale study using various machine‐learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine‐learning models and techniques (e.g., feature selection of RNA transcripts) in pan‐cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine‐learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open‐source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving‐platelet‐rna‐based‐diagnostics), which we hope will serve the community as a strong baseline. [ABSTRACT FROM AUTHOR]

Introduction: Data regarding patients with a previous medical record of immunosuppression treatment who have undergone transcatheter aortic valve implantation (TAVI) are limited and extremely inconclusive. Available studies are mostly short term observations; thus there is a lack of evidence on efficacy and safety of TAVI in this specific group of patients. Aim: To compare the in-hospital and long-term outcomes between patients with or without a medical history of immunosuppressive treatment undergoing TAVI for aortic valve stenosis (AS). Material and methods: We conducted a retrospective registry-based analysis including patients undergoing TAVI for AS at 5 centres between January 2009 and August 2017. The primary endpoint was long-term all-cause mortality. Secondary endpoints comprised major vascular complications, life-threatening or disabling bleeding, stroke and new pacemaker implantation. Results: Of 1451 consecutive patients who underwent TAVI, two propensity-matched groups including 25 patients with a history of immunosuppression and 75 patients without it were analysed. No differences between groups in all-cause mortality were found in a median follow-up time of 2.7 years following TAVI (p = 0.465; HR = 0.73; 95% CI: 0.30-1.77). The rate of major vascular complications (4.0% vs. 5.3%) was similar in the two groups (p = 1.000). There were no statistically significant differences in the composite endpoint combining life-threatening or disabling bleeding, major vascular complications, stroke and new pacemaker implantation (40.0% vs. 20.0%, p = 0.218). Conclusions: Patients who had undergone TAVI for AS had similar long-term mortality regardless of whether they had a previous medical record of immunosuppression. Procedural complication rates were comparable between the groups. [ABSTRACT FROM AUTHOR]

Background: IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients., Methods: We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics., Results: We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting)., Conclusions: Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy., Competing Interests: Dr. Bart Westerman reports a relationship with Health~Holland that includes: funding grants. Dr. Bart Westerman received a public-private partnership fund from Health Holland on a peer-reviewed project where IOTA Pharmaceuticals Ltd and NTRC Therapeutics B.V. are the private parties and contributors to the project. The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)