Abstract A15: Improving immune recognition of shared tumor-associated antigens in pediatric tumors using a multimodal oncolytic virus
Bibliographic record
Abstract
Abstract Immunotherapy is an attractive treatment approach for children because of its precision and reduced toxicity. Unfortunately, pediatric solid tumors often evade immune recognition. Like adult cancers, the immunosuppressive tumor microenvironment can restrict immune activity. Further complicating this, pediatric cancers have low mutation rates, creating fewer antigenic targets for the immune response. We therefore developed a multimodal oncolytic virus that harnesses the antiviral immune response and redirects it against shared tumor-associated antigen (TAA) in the tumor. Engineering a TAA, Ephrin A2 (EphA2), into the oncoviral (OV) genome circumvents transcriptional/translational arrest and allows TAA overexpression during infection. Our results show that virus-based EphA2 expression (C57BL/6 sequence) induces an immune-mediated antitumor response in syngeneic C57BL/6-based brain and peripheral tumor models, improving survival (e.g., CT2A brain tumor median survival: 43d-TAA virus vs. 30d-parent virus, 29d-Saline; overall survival: 44.4%-TAA virus vs. 12%-parent virus vs 0%-Saline, *p=0.0233). TAA-virus treatment increases CD8 memory effector-like cells in tumor infiltrates (CD62L+, CD44+) and rechallenge studies show abscopal effect in TAA-virus treated survivors. Peptide pulsing studies using splenocytes from survivors show that viral TAA expression induces a circulating EphA2-specific CD8 effector-like (CD8+, CD25+, Granzyme B+) population (14.1%-TAA virus vs 2.6%-parent virus, 0.6% Saline, **p≤0.0098). This multimodal viral platform harnesses the oncolytic and immunostimulatory properties of a next-generation OV to enhance immune activity against tumors expressing shared tumor antigens. Our results suggest that this flexible viral-based platform provides an effective in situ antitumor vaccination approach and could be engineered against multiple antigens for low mutational load tumors. Citation Format: Mohammed G. Ghonime, Justin C. Roth, Naomi J. Barker, Katherine E. Miller, Elaine R. Mardis, Christopher M. Walker, Kevin A. Cassady. Improving immune recognition of shared tumor-associated antigens in pediatric tumors using a multimodal oncolytic virus [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A15.
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 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) | 0.001 | 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".