Archaeosomes as Self-adjuvanting Delivery Systems for Cancer Vaccines<sub>*</sub>
Bibliographic record
Abstract
Archaeal ether glycerolipid vesicles (archaeosomes) efficiently deliver exogenous antigen for induction of humoral and cell-mediated immunity. Because induction of CD8 cytotoxic T cells is critical for protective vaccination against tumors, we compared the ability of various archaeosome lipid compositions to evoke a strong CD8 CTL response to entrapped antigen. Subcutaneous immunization of mice with ovalbumin (OVA) entrapped in all archaeosome lipid compositions evoked a primary (day 10) splenic CTL response indicating processing for MHC class I presentation. Interestingly, several polar lipid compositions from halophilic archaea were very potent to adjuvant this early CTL response. Despite this, the lytic units reduced substantially by weeks 6-7. More importantly, at >50 weeks, only Methanobrevibacter smithii and Thermoplasma acidophilum both rich in bipolar membrane-spanning caldarchaeols, demonstrated recall memory CTLs. Immunization of mice with OVA entrapped in M. smithii, Halobacterium salinarum or T. acidophilum vesicles provided prophylactic protection against challenge with OVA-expressing solid tumors at 6 weeks. Even a dose of 3 microg OVA in archaeosomes significantly delayed tumor growth. Tumor protection was also noted in a therapeutic design wherein OVA-archaeosomes were injected concurrent with the tumor challenge. Interestingly, antigen-free T. acidophilum but not antigen-free H. salinarum archaeosomes provided innate therapeutic protection. Vaccination with a CTL peptide epitope from the melanoma differentiation antigen, tyrosinase-related protein 2, in archaeosomes induced a protective CD8 response against B16OVA metastasis, indicating potential for targeting self, tumor antigens. Thus, lipid structural properties of archaea may differentially modulate primary, long-term and/or innate immunity, impacting adjuvant choice for vaccine design.
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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".