Neutralizing Antibodies Impair the Oncolytic Efficacy of Reovirus but Permit Effective Combination with T cell–Based Immunotherapies
Bibliographic record
Abstract
Reovirus type 3 Dearing (Reo), manufactured for clinical application as pelareorep, is an attractive anticancer agent under evaluation in multiple phase 2 clinical trials for the treatment of solid tumors. It elicits its anticancer efficacy by inducing both oncolysis and intratumoral T-cell influx. Because most people have been preexposed to Reo, neutralizing antibodies (NAb) are prevalent in patients with cancer and might present a barrier to effective Reo therapy. Here, we tested serum of patients with cancer and healthy controls (n = 100) and confirmed that Reo NAbs are present in >80% of individuals. To investigate the effect of NAbs on both the oncolytic and the immunostimulatory efficacy of Reo, we established an experimental mouse model with Reo preexposure. The presence of preexposure-induced NAbs reduced Reo tumor infection and prevented Reo-mediated control of tumor growth after intratumoral Reo administration. In B cell-deficient mice, the lack of NAbs provided enhanced tumor growth control after Reo monotherapy, indicating that NAbs limit the oncolytic capacity of Reo. In immunocompetent mice, intratumoral T-cell influx was not affected by the presence of preexposure-induced NAbs and consequently, combinatorial immunotherapy strategies comprising Reo and T-cell engagers or checkpoint inhibitors remained effective in these settings, also after a clinically applied regimen of multiple intravenous pelareorep administrations. Altogether, our data indicate that NAbs hamper the oncolytic efficacy of Reo, but not its immunotherapeutic capacity. Given the high prevalence of seropositivity for Reo in patients with cancer, our data strongly advocate for the application of Reo as part of T cell-based immunotherapeutic strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".