Anti-melanoma vaccines engineered to simultaneously modulate cytokine priming and silence PD-L1 characterized using<i>ex vivo</i>myeloid-derived suppressor cells as a readout of therapeutic efficacy
Bibliographic record
Abstract
Efficacious antitumor vaccines strongly stimulate cancer-specific effector T cells and counteract the activity of tumor-infiltrating immunosuppressive cells. We hypothesised that combining cytokine expression with silencing programmed cell death ligand 1 (PD-L1) could potentiate anticancer immune responses of lentivector vaccines. Thus, we engineered a collection of lentivectors that simultaneously co-expressed an antigen, a PD-L1-silencing shRNA, and various T cell-polarising cytokines, including interferon γ (IFNγ), transforming growth factor β (TGFβ) or interleukins (IL12, IL15, IL23, IL17A, IL6, IL10, IL4). In a syngeneic B16F0 melanoma model and using tyrosinase related protein 1 (TRP1) as a vaccine antigen, we found that simultaneous delivery of IL12 and a PD-L1-silencing shRNA was the only combination that exhibited therapeutically relevant anti-melanoma activities. Mechanistically, we found that delivery of the PD-L1 silencing construct boosted T cell numbers, inhibited in vivo tumor growth and strongly cooperated with IL12 cytokine priming and antitumor activities. Finally, we tested the capacities of our vaccines to counteract tumor-infiltrating myeloid-derived suppressor cell (MDSC) activities ex vivo. Interestingly, the lentivector co-expressing IL12 and the PD-L1 silencing shRNA was the only one that counteracted MDSC suppressive activities, potentially underlying the observed anti-melanoma therapeutic benefit. We conclude that (1) evaluation of vaccines in healthy mice has no significant predictive value for the selection of anticancer treatments; (2) B16 cells expressing xenoantigens as a tumor model are of limited value; and (3) vaccines which inhibit the suppressive effect of MDSC on T cells in our ex vivo assay show promising and relevant antitumor activities.
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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 | 0.001 |
| 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".