Pre-Existing HSV-1 Immunity Enhances Anticancer Efficacy of a Novel Immune-Stimulating Oncolytic Virus
Bibliographic record
Abstract
Oncolytic viruses (OVs) can specifically replicate in the host and cause cancer cell lysis while inducing an antitumor immune response. The aim of this study is to investigate the impact of either pre-existing immunity against herpes simplex virus type-1 (HSV-1) or multicycle treatment with OVs on anticancer efficacy of VG161, an HSV-1 OV in phase 2 clinical trial. VG161 efficacy was tested in CT26 mouse models by comparing the efficacy and immune response in naïve mice or in mice that were immunized with VG161. Moreover, VG161 efficacy in HLA-matched CD34+ humanized intrahepatic cholangiocarcinoma (ICC) patient-derived xenograft (PDX) models was also tested in multicycle treatment and was compared to standard chemotherapy for this type of cancer (gemcitabine). The HSV-1-immunized mice significantly inhibited tumor growth in VG161-treated mice compared to control naïve treated mice. RNA expression profiling and ELISPOT analyses indicated changes in the tumor's immune profile in the immunized and treated group compared to naïve and treated mice, as well as enhanced T cell function depicted by higher numbers of tumor specific lymphocytes, which was enhanced by immunization. In the ICC PDX model, repeated treatment of VG161 with 2 or 3 cycles seemed to increase the anticancer efficacy of VG161. In conclusion, the anticancer efficacy of VG161 can be enhanced by pre-immunization with HSV-1 and multicycle administration when the virus is given intratumorally, indicating that pre-existing antiviral immunity might enhance OV-induced antitumor immunity. Our results suggest potential clinical benefits of HSV-1-based OV therapy in HSV-1-seropositive patients and multicycle administration of VG161 for long-term maintenance treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".