Viro-antibody therapy: engineering oncolytic viruses for genetic delivery of diverse antibody-based biotherapeutics
Bibliographic record
Abstract
Cancer therapeutics approved for clinical application include oncolytic viruses and antibodies, which evolved by nature, but were improved by molecular engineering. Both facilitate outstanding tumor selectivity and pleiotropic activities, but also face challenges, such as tumor heterogeneity and limited tumor penetration. An innovative strategy to address these challenges combines both agents in a single, multitasking therapeutic, i.e., an oncolytic virus engineered to express therapeutic antibodies. Such viro-antibody therapies genetically deliver antibodies to tumors from amplified virus genomes, thereby complementing viral oncolysis with antibody-defined therapeutic action. Here, we review the strategies of viro-antibody therapy that have been pursued exploiting diverse virus platforms, antibody formats, and antibody-mediated modes of action. We provide a comprehensive overview of reported antibody-encoding oncolytic viruses and highlight the achievements of 13 years of viro-antibody research. It has been shown that functional therapeutic antibodies of different formats can be expressed in and released from cancer cells infected with different oncolytic viruses. Virus-encoded antibodies have implemented direct tumor cell killing, anti-angiogenesis, or activation of adaptive immune responses to kill tumor cells, tumor stroma cells or inhibitory immune cells. Importantly, numerous reports have shown therapeutic activity complementary to viral oncolysis for these modalities. Also, challenges for future research have been revealed. Established engineering technologies for both oncolytic viruses and antibodies will enable researchers to address these challenges, facilitating the development of effective viro-antibody therapeutics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".