Abstract A53: Combining oncolytic virotherapy with immunotherapy for ovarian cancer treatment.
Bibliographic record
Abstract
Abstract While spectacular responses have been observed in some patients following cancer immunotherapy, the majority of responses are short-lived with ultimate tumor relapse. Therefore, it is crucial to identify strategies that will effectively synergize with immunotherapies to improve clinical outcome. Oncolytic viruses (OV) are replication-competent viruses that can infect and kill tumor cells, while also acting as potent immune adjuvants. Additionally, as OV can be engineered to express tumor antigens, we are investigating the use of OV as vaccine vectors with the capacity to enhance immune responses to defined tumor antigen targets. Based on these unique attributes of OV, we hypothesized that OV could be effectively combined with multiple immunotherapeutic strategies currently in clinical testing as a means of enhancing anti-tumor immunity and improving tumor growth control/treatment outcome. Targeting a model tumor antigen in a pre-clinical ovarian cancer model, we observed that delivery of oncolytic Maraba virus (MRB) following vaccination enhanced tumor-specific CD8+ T cell expansion and trafficking to the tumor microenvironment, reduced intratumoral Tregs and MDSC frequencies, and resulted in improved survival compared to vaccination alone. Additionally, when MRB was combined with adoptive T cell transfer (ACT) of tumor-specific CD8+ T cells, this combination resulted in improved persistence of the transferred T cells and enhanced tumor control compared to treatment with MRB or ACT alone. Lastly, a dramatic improvement in treatment efficacy was observed when MRB was combined with checkpoint blockade (anti-PD-L1) compared to either monotherapy. These data suggest that OV can effectively synergize with multiple cancer immunotherapies currently being explored. Our current studies are focused on combining OV with either vaccines or ACT strategies that target clinically relevant ovarian cancer antigens (WT1, Sp17, and NY-ESO-1) using syngeneic and humanized mouse models. Additionally, we are exploring the transcriptional profile of tumors treated with immunotherapy + OV to identify gene signatures that correlate with improved therapeutic efficacy. Citation Format: AJ Robert McGray, Cheryl Eppolito, Kyle Stephenson, Jonathan Pol, Brian Lichty, Kunle Odunsi. Combining oncolytic virotherapy with immunotherapy for ovarian cancer treatment. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A53.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".