Direct Lymph Node Vaccination of Lentivector/Prostate-Specific Antigen is Safe and Generates Tissue-Specific Responses in Rhesus Macaques
Bibliographic record
Abstract
Anti-cancer immunotherapy is emerging from a nadir and demonstrating tangible benefits to patients. A variety of approaches are now employed. We are invoking antigen (Ag)-specific responses through direct injections of recombinant lentivectors (LVs) that encode sequences for tumor-associated antigens into multiple lymph nodes to optimize immune presentation/stimulation. Here we first demonstrate the effectiveness and antigen-specificity of this approach in mice challenged with prostate-specific antigen (PSA)-expressing tumor cells. Next we tested the safety and efficacy of this approach in two cohorts of rhesus macaques as a prelude to a clinical trial application. Our vector encodes the cDNA for rhesus macaque PSA and a rhesus macaque cell surface marker to facilitate vector titering and tracking. We utilized two independent injection schemas demarcated by the timing of LV administration. In both cohorts we observed marked tissue-specific responses as measured by clinical evaluations and magnetic resonance imaging of the prostate gland. Tissue-specific responses were sustained for up to six months-the end-point of the study. Control animals immunized against an irrelevant Ag were unaffected. We did not observe vector spread in test or control animals or perturbations of systemic immune parameters. This approach thus offers an "off-the-shelf" anti-cancer vaccine that could be made at large scale and injected into patients-even on an out-patient basis.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".