Oncolytic virotherapy (OVT) is now understood to be an immunotherapy that uses viral infection to liberate tumor antigens in an immunogenic context to promote the development of anti-tumor immune responses. The only currently FDA approved oncolytic virotherapy, T-Vec™, is a modified herpes simplex virus type I (HSV-1). While T-Vec™ is associated with limited response rates its modest efficacy supports the continued development of novel OVT viruses. Herein, we test the efficacy of a recombinant HSV-1, VC2, as an OVT in a syngeneic B16F10-derived mouse model of melanoma. VC2 possesses mutations that block its ability to enter neurons via axonal termini. This greatly enhances its safety profile by precluding the virus’s ability to establish latent infection. VC2 has been shown to be a safe, effective vaccine against both HSV-1 and HSV-2 infection in mice, guinea pigs, and non-human primates. We found that VC2 slows tumor growth rates and that VC2 treatment significantly enhances survival of tumor-engrafted, VC2-treated mice over control treatments. VC2-treated mice that survived initial tumor engraftment were resistant to a second engraftment as well as colonization of lungs by intravenous introduction of tumor cells. We found that VC2 treatment induced substantial increases in intratumoral T-cells and a decrease in immunosuppressive T-regulatory cells. This immunity was critically dependent on CD8+ T-cells and less dependent on CD4+ T-cells. Our data provide significant support for the continued development of VC2 as an OVT for the treatment of human and animal cancers. Importance Current oncolytic virotherapies possess limited response rates. However, when certain patient selection criteria are used, oncolytic virotherapy response rates have been shown to increase. This, in addition to the increased response rates of oncolytic virotherapy in combination with other immunotherapies, suggests that oncolytic viruses possess significant therapeutic potential for the treatment of cancer. As such, it is important to continue to develop novel oncolytic viruses as well as support basic research into their mechanisms of efficacy. Our data demonstrate significant clinical potential for VC2, a novel Type 1 oncolytic herpes simplex virus. Additionally, due to the high rates of survival and the dependence on CD8+ T-cells for efficacy, our model will enable study of the immunological correlates of protection for VC2 oncolytic virotherapy and oncolytic virotherapy in general. Understanding the mechanisms of efficacious oncolytic virotherapy will inform the rational design of improved oncolytic virotherapies.

The development of cancer causes disruption of anti-tumor immunity required for surveillance and elimination of tumor cells. Immunotherapeutic strategies aim for the restoration or establishment of these anti-tumor immune responses. Cancer immunotherapies include immune checkpoint inhibitors (ICIs), adoptive cellular therapy (ACT), cancer vaccines, and oncolytic virotherapy (OVT). The clinical success of some of these immunotherapeutic modalities, including herpes simplex virus type-1 derived OVT, resulted in Food and Drug Administration (FDA) approval for use in treatment of human cancers. However, a significant proportion of patients do not respond or benefit equally from these immunotherapies. The creation of an immunosuppressive tumor microenvironment (TME) represents an important barrier preventing success of many immunotherapeutic approaches. Mechanisms of immunosuppression in the TME are a major area of current research. In this review, we discuss how oncolytic HSV affects the tumor microenvironment to promote anti-tumor immune responses. Where possible we focus on oncolytic HSV strains for which clinical data is available, and discuss how these viruses alter the vasculature, extracellular matrix and immune responses in the tumor microenvironment.

Human metapneumovirus (HMPV) is one of the leading causes of respiratory diseases in infants and children worldwide. Although this pathogen infects mainly young children, elderly and immunocompromised people can be also seriously affected. To date, there is no commercial vaccine available against it. Upon HMPV infection, the host innate arm of defense produces interferons (IFNs), which are critical for limiting HMPV replication. In this review, we offer an updated landscape of the HMPV mediated-IFN response in different models as well as some of the defense tactics employed by the virus to circumvent IFN response.

Background Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching. Methods CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion. Results Selected CancerOmicsNet predictions obtained for “unseen” data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03. Conclusions CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Maternal smoking during pregnancy and exposure of infants to cigarette smoke are strongly associated with adverse health effects in childhood including higher susceptibility to respiratory viral infections. Human respiratory syncytial virus (HRSV) is the most important cause of lower respiratory tract infection among young infants. Exacerbation of respiratory disease, including HRSV bronchiolitis and higher susceptibility to HRSV infection, is well correlated with previous smoke exposure. The mechanisms of recurrence and susceptibility to viral pathogens after passive smoke exposure are multifactorial and include alteration of the structural and immunologic host defenses. In this work, we used a well-established mouse model of in utero smoke exposure to investigate the effect of in utero smoke exposure in HRSV-induced pathogenesis. Sample analysis indicated that in utero exposure led to increased lung inflammation characterized by an increased influx of neutrophils to the airways of the infected mice and a delayed viral clearance. On the other hand, decreased HRSV-specific CD8+ T-cell response was observed. These findings indicate that cigarette smoke exposure during pregnancy alters HRSV-induced disease as well as several aspects of the neonatal immune responses.

Previous work has highlighted the complicated and distinctive dynamics that set signal evolution during a train of spin echoes, especially with nonuniform echo spacing applied to complex molecules like fats. The work presented here regards those signal patterns as codes that can be used as a contrast mechanism, capable of distinguishing mixtures of molecules with an imaging sequence, sidestepping many challenges of spectroscopy. For particular arrays of echo spacings, non-monotonic and distinctive signal evolution can be enhanced to improve contrast between target species. This work presents simulations that show how contrast between two molecules: (a) depends on the specific sequence of echo spacing, (b) is directly linked to the presence of J-coupling, and (c) can be relatively insensitive to variations in B0, T2 and B1. Imaging studies with oils demonstrate this phenomenon experimentally and also show that spin echo codes can be used for quantification. Finally, preliminary experiments apply the method to human liver in vivo , verifying that the presence of fat can lead to nonmonotonic codes like those seen in vitro . In summary, nonuniformly spaced echo trains introduce a new approach to molecular imaging of J-coupled species, such as lipids, which may have implications diagnosing metabolic diseases.

Influenza virus is a major respiratory viral pathogen responsible for the deaths of hundreds of thousands worldwide each year. Current vaccines provide protection primarily by inducing strain-specific antibody responses with the requirement of a match between vaccine strains and circulating strains. It has been suggested that anti-influenza T-cell responses, in addition to antibody responses may provide the broadest protection against different flu strains. Therefore, to address this urgent need, it is desirable to develop a vaccine candidate with an ability to induce balanced adaptive immunity including cell mediated immune responses. A live viral vector technology should exhibit safety, immunogenicity, effectiveness in the presence of pre-existing immunity, and the ability to induce mucosal immune responses. Here, we used VC2, an established Herpes Simplex Virus type 1 vaccine vector, to express the influenza HA protein. We show that this virus is capable of generating potent and specific anti-influenza humoral and cell-mediated immune responses. We further show that a single vaccination with the VC2-derived influenza vaccine protects mice from lethal challenge with influenza virus. Our data support the continued development of VC2-derived influenza vaccines for protection of human populations from both seasonal and pandemic strains of influenza. Finally, our results support the potential of VC2-derived vaccines as a platform for the rapid development of vaccines against emerging and established pathogens, particularly respiratory pathogens.

Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases. We recently developed CancerOmicsNet, an artificial intelligence model employing graph-based algorithms to predict the cancer cell response to treatment with kinase inhibitors. The performance of this approach has been evaluated in large-scale benchmarking calculations, followed by the experimental validation of selected predictions against several cancer types. To shed light on the decision-making process of CancerOmicsNet and to better understand the role of each kinase in the model, we employed a customized saliency map with adjustable channel weights. The saliency map, functioning as an explainable AI tool, allows for the analysis of input contributions to the output of a trained deep-learning model and facilitates the identification of essential kinases involved in tumor progression. The comprehensive survey of biomedical literature for essential kinases selected by CancerOmicsNet demonstrated that it could help pinpoint potential druggable targets for further investigation in diverse cancer types.