Dasatinib promotes the expansion of a therapeutically superior T-cell repertoire in response to dendritic cell vaccination against melanoma
Bibliographic record
Abstract
Dasatinib (DAS) is a potent inhibitor of the BCR-ABL, SRC, c-KIT, PDGFR, and ephrin tyrosine kinases that has demonstrated only modest clinical efficacy in melanoma patients. Given reports suggesting that DAS enhances T cell infiltration into the tumor microenvironment, we analyzed whether therapy employing the combination of DAS plus dendritic cell (DC) vaccination would promote superior immunotherapeutic benefit against melanoma. Using a M05 (B16.OVA) melanoma mouse model, we observed that a 7-day course of orally-administered DAS (0.1 mg/day) combined with a DC-based vaccine (VAC) against the OVA257–264 peptide epitope more potently inhibited tumor growth and extended overall survival as compared with treatment with either single modality. The superior efficacy of the combinatorial treatment regimen included a reduction in hypoxic-signaling associated with reduced levels of immunosuppressive CD11b+Gr1+ myeloid-derived suppressor cells (MDSC) and CD4+Foxp3+ regulatory T (Treg) populations in the melanoma microenvironment. Furthermore, DAS + VAC combined therapy upregulated expression of Type-1 T cell recruiting CXCR3 ligand chemokines in the tumor stroma correlating with activation and recruitment of Type-1, vaccine-induced CXCR3+CD8+ tumor-infiltrating lymphocytes (TILs) and CD11c+ DC into the tumor microenvironment. The culmination of this bimodal approach was a profound “spreading” in the repertoire of tumor-associated antigens recognized by CD8+ TILs, in support of the therapeutic superiority of combined DAS + VAC immunotherapy in the melanoma setting.
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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) | 0.003 | 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".