Beta‐blockers exert potent anti‐tumor effects in cutaneous and uveal melanoma
Bibliographic record
Abstract
BACKGROUND: Melanoma is a life-threatening group of cancers mainly affecting the skin (cutaneous melanoma, CM) and the eyes (uveal melanoma, UM). Nearly half of patients with UM develop liver metastases regardless of the primary treatment. For this reason, adjuvant therapy to prevent disease progression is essential to improve survival of patients with melanoma. Beta-adrenoceptors (β-AR) have emerged as novel targets to inhibit tumor growth and dissemination in CM, but have not been investigated in UM. METHODS: The aim of this study was to comprehensively evaluate the effects of a non-selective β-blocker in UM and CM. Propranolol was tested on four UM and two CM cell lines to determine the effects of this beta-blocker. The expression of β-AR in UM was assessed in enucleated eyes of 36 patients. RESULTS: The results showed that propranolol exerts potent anti-proliferative effects, attenuates migration, reduces VEGF and induces cell cycle arrest and apoptosis in both UM and CM in a dose-dependent manner. Furthermore, levels of cell-free DNA released from the cells correlated to propranolol treatment and may be an indicator of treatment response. Finally, immunohistochemical analysis revealed the expression of β1 and β2 adrenoceptors in all UM patients, with higher expression seen in the more aggressive epithelioid versus less aggressive spindle cells. CONCLUSIONS: Collectively our data suggest that a nonselective beta-blocker may be effective against melanoma. For the first time, we show potent anti-tumor effects in UM cells following propranolol administration and expression of β1 and β2 adrenoceptors in patient tissue.
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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 | 0.000 |
| 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".