Abstract 4557: Tumor immune profiling identifies multiple unique therapeutic targets that improve vaccination + oncolytic virotherapy against metastatic ovarian cancer
Bibliographic record
Abstract
Abstract While spectacular responses to cancer immunotherapy have been observed in some patients, the majority of responses are short-lived with ultimate tumor relapse. Therefore, it is crucial to identify strategies that will effectively synergize with immunotherapy to improve treatment outcome. Using a pre-clinical mouse model, we explored the use of a potent heterologous prime/boost vaccine strategy for the treatment of metastatic intraperitoneal ovarian cancer. Priming with an adjuvant based vaccine followed by boosting with a novel oncolytic Maraba viral vector elicited robust tumor-specific CD8+ T cell responses, with high numbers of therapy-induced CD8+ T cells effectively trafficking to the tumor microenvironment. While this approach greatly improved tumor control and long-term survival compared to treatment with the priming vaccine alone, the combination therapy was not curative and cellular analysis suggested that T cells within the tumor microenvironment were functionally suppressed/exhausted. Transcriptional profiling of tumors following therapy revealed that prime/boost vaccination led to induction of numerous inflammatory processes, with gene signatures consistent with not only CD8+ T cell infiltration, but also upregulation of multiple co-stimulatory and immune checkpoint receptors, induction of chemokine networks associated with both lymphoid and myeloid cell trafficking, as well as infiltration of immunosuppressive myeloid cell populations. We reasoned that these gene signatures could be used to design rational combination therapies that would have the potential to further enhance tumor attack following prime/boost vaccination. Using this strategy, we observed that checkpoint blockade using αPD-1 in combination with prime/boost vaccination resulted in a dramatic improvement in tumor control, as did transient depletion of granulocytic myeloid cells (but not monocytes/macrophages) following treatment. Current studies are underway that combine prime/boost vaccination with relevant co-stimulatory agonist antibodies, as well as inhibitors of candidate chemokine networks identified through tumor profiling. These findings underscore the importance of designing treatment strategies that not only elicit robust anti-tumor T cell responses, but also improve the duration and/or magnitude of immune attack within the tumor microenvironment. Additionally, our data suggest that interrogating the tumor microenvironment during treatment can identify unique therapeutic targets that have the potential to further improve therapeutic impact. Citation Format: AJ Robert Mcgray, Cheryl Eppolito, Anthony Miliotto, Raya Huang, Kelly Singel, Jonathan Pol, Kyle Stephenson, Brahm H. Segal, Brian Lichty, Kunle Odunsi. Tumor immune profiling identifies multiple unique therapeutic targets that improve vaccination + oncolytic virotherapy against metastatic ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4557. doi:10.1158/1538-7445.AM2017-4557
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".