Rittner, K. | bioRxiv Science

bioRxiv2025

Therapeutic poxviruses induce the secretion of immunostimulating and anti-tumoral extracellular vesicles

Poxvirus-based vectors provide a versatile cancer immunotherapy platform, enabling the expression of immunostimulatory molecules and cancer-specific antigens. While infections with pathogenic viruses are well known to modulate extracellular vesicle (EV) biogenesis and function, the extent to which therapeutic poxviral vectors influence EV secretion by immune cells and thereby affect therapeutic efficacy remains underexplored. In this study, we showed that poxviruses, including the clinically relevant Modified Vaccinia Ankara (MVA), stimulate the secretion of small EVs (sEVs) containing viral proteins and immune-related signatures from peripheral blood mononuclear cells (PBMCs). Using an engineered MVA vector, we demonstrated the transfer of virus-encoded therapeutic payloads to sEVs, including the model ovalbumin (OVA)-derived peptide SIINFEKL presented by the class I major histocompatibility complex (MHC I) and the immune activators interleukin-12 (IL-12) and CD40 ligand (CD40L). Depending on the isolation method, these sEVs stimulated SIINFEKL-specific CD8 T cells with varying efficiencies in vitro. Remarkably, intravenous injection of these sEVs into E.G7-OVA lymphoma-bearing mice reduced tumor growth to an extent comparable to the virus itself. Taken together, our findings indicate that EVs released from immune cells infected with engineered therapeutic poxviruses exert potent antitumor activity. These vesicles represent actionable mediators whose secretion and functionalization can be harnessed to improve viral vector-based immunotherapies, as well as being considered as therapeutic vectors in their own. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/677320v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1eae18borg.highwire.dtl.DTLVardef@17d6fb5org.highwire.dtl.DTLVardef@311577org.highwire.dtl.DTLVardef@7839c7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

BERTMHC: Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning

MotivationIncreasingly comprehensive characterisation of cancer associated genetic alteration has paved the way for the development of highly specific therapeutic vaccines. Predicting precisely binding and presentation of peptides by MHC alleles is an important step towards such therapies. Recent data suggest that presentation of both class I and II epitopes is critical for the induction of a sustained effective immune response. However, the prediction performance for MHC class II has been limited compared to class I. ResultsWe present a transformer neural network model which leverages on self-supervised pretraining from a large corpus of protein sequences. We also propose a multiple instance learning (MIL) framework to deconvolve mass spectrometry data where multiple potential MHC alleles may have presented each peptide. We show that pretraining boosted the performance for these tasks. Combining pretraining and the novel MIL approach, our model outperforms state-of-the-art models for both binding and mass spectrometry presentation predictions. AvailabilityOur model is available at https://github.com/s6juncheng/BERTMHC Contactjun.cheng@neclab.eu, brandon.malone@neclab.eu

bioinformatics↗