Back

Retargeted adenoviruses for local IgA and CD47 blocker production as a novel cancer therapy

Chernyavska, M.; Hartmann, K. P.; Jansen, M.; Baumann, N.; Kolibius, J.; Bruecher, D.; Kristoforus, T.; Peters, R. H. W.; Huijs, L.; Laarveld, D.; Weiss, F.; Burger, R.; Lustig, M.; Gimenez de Assis, N.; Schmid, M.; Leusen, J. H. W.; Valerius, T.; Plueckthun, A.; Verdurmen, W. P. R.

2025-11-07 immunology
10.1101/2025.11.07.686998 bioRxiv
Show abstract

Despite advances in IgG-based cancer immunotherapy, challenges remain in effectively engaging innate immune responses against solid tumors. Here, IgA antibodies hold promise due to their ability to activate neutrophils and macrophages. We present a novel retargeted adenovirus-mediated approach that transforms cancer cells into "biofactories" for localized production of monomeric or dimeric IgA antibodies and a CD47 blocker to potentiate the effect of IgA antibodies. With our approach tumor cells effectively produced IgA antibodies against tumor antigens such as EGFR or EpCAM and a soluble SIRP-Fc fusion protein, which blocks the CD47-SIRP axis. In a perfused tumor-on-a-chip model, locally produced IgA triggered neutrophil- and macrophage-mediated tumor cell killing, further potentiated by SIRP-Fc co-production. In FcRI-transgenic, tumor-bearing mice, intratumoral adenoviral injection induced strong local IgA and SIRP-Fc expression, immune cell infiltration, and more than 50% tumor volume reduction after a single treatment. We found that dimeric IgA exerts stronger effects than monomeric IgA, which is of particular interest since dimeric IgA necessitates a local production approach. Together, these results demonstrate that adenovirus-mediated, tumor-restricted delivery of IgA antibodies and CD47 blockade effectively engages innate immune mechanisms, providing a promising new avenue to enhance cancer immunotherapy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/686998v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@183dd88org.highwire.dtl.DTLVardef@452624org.highwire.dtl.DTLVardef@1cb10e8org.highwire.dtl.DTLVardef@c2fa85_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in EMBO Molecular Medicine (predicted rank #29) · training set

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.