Abstract 5002: Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most prevalent and aggressive primary brain tumor type with an abysmal prognosis, featuring an immunologically dampened tumor microenvironment and limited neoantigen production. Novel therapeutic strategies are urgently needed. A well-characterized GBM-associated mutation that can elicit an anti-tumor immune response is EGFRvIII mutation. The cancer-driver mutation is detected in approximately 30% of patients at the time of diagnosis and plays a pivotal role in the emergence of GBM. EGFRvIII is an in-frame epidermal growth factor receptor (EGFR) deletion generating a constitutively active oncogenic protein. Its highly immunogenic nature represents an ideal focal point for cutting-edge mRNA-LNP candidate therapeutics. To evaluate proprietary lipid nanoparticle (LNP) compositions for their capacity to induce anti-GBM immunity, we developed a murine transplantable GBM tumor model by neonatal electroporation. The somatically engineered glioblastomas harbor two targeted CRISPR knockouts and stably overexpress EGFRvIII to yield transplantable cell lines for orthotopic tumor initiation. One week after intracranial tumor cell implantation of 6-8 week-old female mice (day 0; n=10), half the animals were administered either buffer (control cohort) or our novel anti-EGFRvIII mRNA-LNP vaccine (experimental cohort). The regimen involved four intramuscular injections before monitoring tumor progression by MRI on day 30. The imaging revealed a high tumor burden in all control group animals, compared to no detectable tumor cells in vaccine-protected mice. All control group animals were humanely euthanized by day 38 due to symptoms associated with the growing brain tumor mass. The asymptomatic experimental animals received a fifth anti-EGFRvIII vaccination before MRI-based examinations on day 43 and day 78 confirmed that no tumor cells could be detected in vaccine-protected mice. The results highlight the potency and potential of our proprietary anti-EGFRvIII mRNA-LNP vaccine as a novel anticancer therapeutic. In addition, the established tumor model represents a valuable tool to test future mRNA-LNP designs. Outstanding cellular and molecular investigations will reveal vaccine-mediated mechanistic insights, allowing for the co-advancement of the tumor model and next-generation multi-target mRNA-LNP vaccine candidates. Citation Format: Robert Nechanitzky, Shannon Snelling Snelling, Kristofor Ellestad, Xueqing Lun, Kirstin Olsen, Yu Wu, Yury Karpov, Jun Liu, Matthew Gold, Haritha Menon, Rajesh Krishnan, Robert Georgantas, Pamela S. Ohashi, Douglas J. Mahoney, Jennifer A. Chan, Natalia Martin-Orozco. Next-generation mRNA-LNP vaccine prototype achieves tumor clearance in a GBM mouse model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5002.
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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) | 0.000 | 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 | 0.001 | 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".