Abstract B88: Combination therapy of reovirus and checkpoint inhibition
Bibliographic record
Abstract
Abstract We have developed the use of reovirus as a systemically delivered oncolytic agent in both pre-clinical models and in Phase I and II clinical trials. Reovirus has direct oncolytic activity against many human/murine tumor cells, partly because of disruption of the PKR-mediated anti-viral response in malignant cells. In addition however, we have shown that anti-tumor therapy is directly associated with immune activation by virus replication in tumors. The immune mechanisms of therapy include both innate immune activation against virally infected tumor cells, as well as the generation of adaptive anti tumor immune responses as a result of in vivo priming against tumor associated antigens released during that killing. Therefore, to exploit the immune components of reovirus anti-tumor therapy, we tested the combination of oncolytic therapy with systemic checkpoint inhibition. To do this, we used our murine, immune-competent, model and a protocol in which injection of reovirus into subcutaneous (s.c.) B16 melanomas generates moderate therapy following intra-tumoral injection of reovirus into 5d established subcutaneous B16 melanomas growing in C57Bl/6 mice. In this model, provision of systemic anti-PD-1 antibody along with i.t. reovirus, significantly enhanced survival compared to i.t. reovirus alone (p<0.01) and led to >40% of mice being cured long term. Immune analysis suggests that the enhanced therapeutic benefit of reovirus plus checkpoint inhibition is contributed by at least two factors. Thus, blockade of PD-1 significantly enhanced the ability of NK cells to recognize (TNF-α secretion), and kill, reovirus-infected target tumor cells. Second, antiPD-1 antibody led to a significant reduction in Treg activity in reovirus-treated mice, with the overall effect of increasing the adaptive CD8+ anti tumor T cell response. We also showed that timing of the checkpoint inhibitor antibody was significant in balancing toxicity with anti tumor therapy. These data suggest that combination of checkpoint inhibition therapy with reovirus oncolytic/immunotherapy represents a readily translatable method to enhance the therapeutic value of either alone. Citation Format: Shane Zaidi, Kevin Shim, Karishma Rajani, Rosa Diaz, Elizabeth Ilett, Timothy Kottke, Jill Thompson, Peter Selby, Kevin Harrington, Hardev Pandha, Alan Melcher, Matt Coffey, Richard Vile. Combination therapy of reovirus and checkpoint inhibition. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr B88.
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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 | 0.002 | 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 | 0.001 |
| 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".