Erratum To: Activin receptor‐like kinase5 inhibition suppresses mouse melanoma by ubiquitin degradation of Smad4, thereby derepressing eomesodermin in cytotoxic T lymphocytes
Bibliographic record
Abstract
Varieties of transforming growth factor-β (TGF-β) antagonists have been developed to intervene with excessive TGF-β signalling activity in cancer. Activin receptor-like kinase5 (ALK5) inhibitors antagonize TGF-β signalling by blocking TGF-β receptor-activated Smad (R-Smad) phosphorylation. Here we report the novel mechanisms how ALK5 inhibitors exert a therapeutic effect on a mouse B16 melanoma model. Oral treatment with a novel ALK5 inhibitor, EW-7197 (2.5 mg/kg daily) or a representative ALK5 inhibitor, LY-2157299 (75 mg/kg bid) suppressed the progression of melanoma with enhanced cytotoxic T-lymphocyte (CTL) responses. Notably, ALK5 inhibitors not only blocked R-Smad phosphorylation, but also induced ubiquitin-mediated degradation of the common Smad, Smad4 mainly in CD8(+) T cells in melanoma-bearing mice. Accordingly, T-cell-specific deletion of Smad4 was sufficient to suppress the progression of melanoma. We further identified eomesodermin (Eomes), the T-box transcription factor regulating CTL functions, as a specific target repressed by TGF-β via Smad4 and Smad3 in CD8(+) T cells. Thus, ALK5 inhibition enhances anti-melanoma CTL responses through ubiquitin-mediated degradation of Smad4 in addition to the direct inhibitory effect on R-Smad phosphorylation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".