CD137L promotes immune surveillance in melanoma via HLTF regulation
Bibliographic record
Abstract
Immune checkpoint blockers (ICBs) have demonstrated substantial efficacy across various malignancies, yet the benefits of ICBs are limited to a subset of patients. Therefore, it is essential to identify novel therapeutic targets. By integrating multi-omics data from cohorts of patients with melanoma treated with ICBs, a positive correlation is observed between tumor CD137L expression and the efficacy of PD-1 blockade. Functionally, CD137L induction in cancer cells significantly enhances anti-tumor immunity by promoting CD8+ T cell survival, both in vivo and in vitro. Mechanistically, helicase-like transcription factor (HLTF) is identified as a pivotal transcriptional regulator of CD137L, controlling its expression through phosphorylation of serine at position 398. Therapeutically, the AMPK agonist AICAR (acadesine) as an inducer of CD137L, exhibiting synergistic effects with PD-1 or CTLA-4 blockade. In summary, our findings elucidate a mechanism controlling CD137L expression and highlight a promising combination therapy to enhance the efficacy of ICBs in melanoma. One Sentence Summary: Inducing co-stimulatory immune checkpoint CD137L expression in melanoma cells enhances T cell-mediated anti-tumor immunity. Immune checkpoint blockade is an effective therapy in melanoma, but response is highly variable between patients. Here, the authors show that Inducing co-stimulatory immune checkpoint CD137L expression in melanoma cells enhances T cell-mediated anti-tumor immunity.
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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) | 0.002 | 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".