EXTH-15. AN EGFRVIII RNA-LNP VACCINE PROTOTYPE ACHIEVES TUMOUR CLEARANCE IN A GBM MOUSE MODEL
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is an aggressive high-grade glioma that is fatal despite standard-of-care therapy. Recurrent somatic alterations that form neoantigens represent opportunities for targeted therapeutic intervention. Using human EGFRvIII as a prototypical structural alteration and neoantigen, we tested the potential of a mRNA-LNP vaccine approach to improve the survival of mice with EGFRvIII-driven GBM. To generate the model, we used in vivo electroporation to introduce genetic alterations (EGFRvIII-OE/CDKN2A-KO/PTEN-KO) into periventricular cells of early postnatal C3H mice, then propagated cells from the subsequent tumours to create implantable cell lines. Following orthotopic implantation, the resulting syngeneic tumours faithfully recapitulate histologic features of GBM with infiltrative growth, mitoses, necrosis, and vascular proliferation. Also like human GBM, the models are sensitive to but not cured by temozolomide chemotherapy and are resistant to immune checkpoint blockade. We delivered mRNA-LNP vaccines against EGFRvIII into the thigh muscle of tumour-bearing mice on days 7, 10, 14, 21, and 36 after tumour cell implant. We observed no tumour in vaccine-treated mice compared to significant tumour burden in control vector-treated animals by MR imaging at day 30 post-tumour cell implant. By day 37, all control-treated mice had died, whereas vaccine-treated mice cleared their tumours with no tumour burden detectable by histology at day 150 (N= 5 per group, **p= 0.0023). In a follow-up experiment with vaccine or control treatment on days 10, 14, 18, and 25 post-cell implants, all control mice had died by day 39. Further, vaccine-treated surviving animals were re-challenged with tumour cell implant into the contralateral hemisphere on day 42 and remain alive as of day 90. Our ongoing proof-of-principle work demonstrates that neoantigen driver mutations in high-grade glioma models can be targeted with an mRNA lipid vaccine. We hope this pre-clinical work will lead to early-phase clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".