TMIC-120. Investigating EGFRvIII mRNA LNP vaccine-induced re-programming of the GBM TME using the 10x Genomics Visium HD platform
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a lethal brain tumor refractory to standard therapies, highlighting the need for alternative treatments. Immunotherapies, particularly cancer vaccines targeting neoantigens, offer a compelling approach to enhance anti-tumor immunity. To this end, we developed a preclinical mRNA lipid nanoparticle (LNP) vaccine designed to target human EGFRvIII, a GBM-specific oncogenic variant, and evaluated its impact on survival in an EGFRvIII-driven tumor model, utilizing EGFRvIII-OE/CDKN2A-KO/PTEN-KO syngeneic mice. Following vaccine administration on days 7, 10, 14, 21, and 36 post-tumor cell implantations, vaccinated mice showed complete tumor clearance by day 30, whereas controls exhibited significant tumor burden. To elucidate the vaccine’s therapeutic mechanisms and its impact on the tumor microenvironment (TME), we performed Visium HD spatial transcriptomic profiling on coronal brain sections from four mouse groups: buffer control and luciferase vaccine at day 21, and EGFRvIII mRNA-LNP vaccine-treated tumors at days 21 and 32. Using an unsupervised deconvolution approach, we identified 29 distinct transcriptional programs localized within the tumor lesion, comprising diverse glial, myeloid, and lymphocyte signatures. Notably, we observe a significant enrichment of pro-inflammatory and antigen-processing tumor-associated macrophage populations, alongside activated cytotoxic T-cells within the EGFRvIII vaccine-treated tumors compared to controls. Furthermore, a differential neighborhood analysis between the EGFRvIII vaccine-treated and control samples revealed a close spatial association of these activated immune signatures, indicating a vaccine-induced re-programming of the TME to facilitate a coordinated anti-tumor response. Our results thus demonstrate the EGFRvIII mRNA-LNP vaccine as a potent therapeutic that drives GBM regression in preclinical models by modulating the TME, paving the way for its clinical translation.
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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".