A Potent Cancer Vaccine Adjuvant System for Particleization of Short, Synthetic CD8<sup>+</sup> T Cell Epitopes
Bibliographic record
Abstract
Short major histocompatibility complex (MHC) class I (MHC-I)-restricted peptides contain the minimal biochemical information to induce antigen (Ag)-specific CD8+ cytotoxic T cell responses but are generally ineffective in doing so. To address this, we developed a cobalt–porphyrin (CoPoP) liposome vaccine adjuvant system that induces rapid particleization of conventional, short synthetic MHC-I epitopes, leading to strong cellular immune responses at nanogram dosing. Along with CoPoP (to induce particle formation of peptides), synthetic monophosphoryl lipid A (PHAD) and QS-21 immunostimulatory molecules were included in the liposome bilayer to generate the “CPQ” adjuvant system. In mice, immunization with a short MHC-I-restricted peptide, derived from glycoprotein 70 (gp70), admixed with CPQ safely generated functional, Ag-specific CD8+ T cells, resulting in the rejection of multiple tumor cell lines, with durable immunity. When cobalt was omitted, the otherwise identical peptide and adjuvant components did not result in peptide binding and were incapable of inducing immune responses, demonstrating the importance of stable particle formation. Immunization with the liposomal vaccine was well-tolerated and could control local and metastatic disease in a therapeutic setting. Mechanistic studies showed that particle-based peptides were better taken up by antigen-presenting cells, where they were putatively released within endosomes and phagosomes for display on MHC-I surfaces. On the basis of the potency of the approach, the platform was demonstrated as a tool for in vivo epitope screening of peptide microlibraries comprising a hundred peptides.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".