Recombinant Strains of Oncolytic Vaccinia Virus for Cancer Immunotherapy.

Cancer virotherapy is an alternative therapeutic approach based on the viruses that selectively infect and kill tumor cells. Vaccinia virus (VV) is a member of the Poxviridae, a family of enveloped viruses with a large linear double-stranded DNA genome. The proven safety of the VV strains as well as considerable transgene capacity of the viral genome, make VV an excellent platform for creating recombinant oncolytic viruses for cancer therapy. Furthermore, various genetic modifications can increase tumor selectivity and therapeutic efficacy of VV by arming it with the immune-modulatory genes or proapoptotic molecules, boosting the host immune system, and increasing cross-priming recognition of the tumor cells by T-cells or NK cells. In this review, we summarized the data on bioengineering approaches to develop recombinant VV strains for enhanced cancer immunotherapy.

Multiomic Profiling Identified EGF Receptor Signaling as a Potential Inhibitor of Type I Interferon Response in Models of Oncolytic Therapy by Vesicular Stomatitis Virus.

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Comparative Analysis of Heart Failure with Preserved Vs Reduced Ejection Fraction: Patient Characteristics, Outcomes, Mortality Prediction, and Machine Learning Model Development in the JoHFR.

Background: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction. Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction. Results: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study. Conclusion: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.