Complementary dual-virus strategy drives synthetic target and cognate T-cell engager expression for endogenous-antigen agnostic immunotherapy
Bibliographic record
Abstract
Targeted antineoplastic immunotherapies have achieved remarkable clinical outcomes. However, resistance to these therapies due to target absence or antigen shedding limits their efficacy and excludes tumours from candidacy. To address this limitation, here we engineer an oncolytic rhabdovirus, vesicular stomatitis virus (VSVΔ51), to express a truncated targeted antigen, which allows for HER2-targeting with trastuzumab. The truncated HER2 (HER2T) lacks signaling capabilities and is efficiently expressed on infected cell surfaces. VSVΔ51-mediated HER2T expression simulates HER2-positive status in tumours, enabling effective treatment with the antibody-drug conjugate trastuzumab emtansine in vitro, ex vivo, and in vivo. Additionally, we combine VSVΔ51-HER2T with an oncolytic vaccinia virus expressing a HER2-targeted T-cell engager. This dual-virus therapeutic strategy demonstrates potent curative efficacy in vivo in female mice using CD3+ infiltrate for anti-tumour immunity. Our findings showcase the ability to tailor the tumour microenvironment using oncolytic viruses, thereby enhancing compatibility with “off-the-shelf” targeted therapies. Oncolytic viruses (OVs) represent a treatment option for patients with cancer. Here the authors propose a tumour-agnostic dual-virus strategy for cancer therapy by generating a vesicular stomatitis virus encoding a truncated version of HER2, combined with a vaccinia virus as a delivery platform for a HER2-targeted T-cell engager.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".