Abstract 6740: Next-generation LNPs induce effective anti-tumor T cell responses
Bibliographic record
Abstract
Abstract mRNA immunotherapies have gained interest for the treatment of tumors from various origins, however, their ability to provide long -term durable tumor control has been underwhelming. Here we report that novel mRNA lipid nanoparticle (LNP) vaccinations induce robust expansion of antigen-specific T cells, although this does not always correlate with their ability to infiltrate and kill solid tumors. Using a portfolio of novel ionizable lipids and a robust in vivo model that reliably identifies LNPs with prominent anti-tissue T cell activity we have identified a putative LNP candidate that provides long term tumor control in a murine syngeneic tumor model. We utilized the model antigen glycoprotein (Gp), from the lymphocytic choriomeningitis virus (LCMV), and produced mRNA encoding the immunodominant epitopes gp3333-41, gp6161-80 and gp276276-286 (3GP-mRNA). This mRNA was encapsulated in various novel LNP formulations and administered i.m. into naïve C57Bl/6 mice to track the induction of a robust CD8+ T cell response identified using tetramers to gp33 and gp276. Successful candidate LNPs were then tested in the RIPgp model of autoimmune diabetes. RIPgp transgenic mice express LCMV-Gp in the pancreatic β cells and offer a robust assay for the ability to induce antigen-specific T cells capable of breaking self-tolerance and infiltrating and killing target tissue. Utilizing the RIPgp model to screen LNP candidates we identified a lead candidate, C2-5A, that both induced a robust expansion of antigen-specific T cells and tissue destruction. When utilized in a colon adenocarcinoma model expressing the LCMV-Gp antigen (MC38-Gp), therapeutic dosing of LNP C2-5A as a monotherapy lead to long term tumor clearance in 40% of the treated animals. We believe this screening platform allows the selection of LNP candidates capable of breaking self-tolerance, and one such candidate C2-5A provides a potent vehicle and adjuvant for mRNA based cancer vaccines. Citation Format: Douglas G. Millar, Matthew J. Gold, Kirsten Olsen, Yu Wu, Yury Karpov, Robert Nechanitzky, Haritha Menon, Rajesh Krishnan, Robert Georgantas, Pamela Ohashi, Natalia Martin-Orozco. Next-generation LNPs induce effective anti-tumor T cell responses [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 6740.
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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) | 0.002 | 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".