Specific Genetic Mutations Impact Chemotherapy Resistance and Therapeutic Efficacy of Oncolytic Viruses in Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecologic cancer, and those affected are in urgent need of new therapeutic strategies. Standard treatment is surgery followed by taxane- and platinum-based chemotherapy. However, the rate of relapse is high, and the 5-year survival is only 45%. Oncolytic viruses (OV) are a promising approach to EOC therapy through remodeling the immune composition of the tumor microenvironment. Treatment response in EOC tumors can differ based on the presence of key tumorigenic mutations. This study evaluated the impact of specific tumor mutations on the response to the current standard-of-care carboplatin, two promising OV candidates VSVΔM51 and MG1, an infected cell vaccine (ICV-MG1) regimen, and the antiangiogenic drug Fc3TSR. Mice with tumors harboring constitutive K-Ras activation showed an enhanced response to carboplatin and VSVΔM51 treatment. Additionally, VSVΔM51 treatment prolonged survival of syngeneic mice bearing tumors with mutations in Pten and Kras, Pten and Trp53, or Trp53 and Brca2 with increased activation of CD4+ and CD8+ T lymphocytes in the peritoneal tumor microenvironment. To enhance OV potency, an MG1-based infected cell vaccine inducing the expression of IL21 or IL15 + IL21 was developed and found to enable strong and long-lasting antitumoral immunity in two carboplatin-refractory syngeneic models, ID8-Trp53-/- and STOSE. VSVΔM51 combined with the antiangiogenic Fc3TSR enhanced efficacy in the ID8 model. In summary, OV-based immunotherapy has shown promise in diverse murine models of EOC-bearing clinically relevant mutations, thus laying the foundation for developing new OV-based strategies to target a large spectrum of EOC genotypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".