Does propranolol have a role in cancer treatment? A systematic review of the epidemiological and clinical trial literature on beta-blockers
Bibliographic record
Abstract
PURPOSE: Beta-blockers, originally developed for cardiovascular conditions, have been explored for their potential role in cancer treatment. Propranolol, a non-selective beta-blocker, has shown promise in inhibiting stress-induced signalling pathways associated with cancer progression. This systematic review aims to assess the evidence for the repurposing of propranolol as a treatment for various cancers, particularly breast cancer to answer the research question: Does propranolol improve cancer outcomes, including survival and recurrence? METHODS: We conducted a systematic search of MEDLINE, EMBASE, Global Health, Web of Science, and the Cochrane Library, including studies up to July 2024. Randomised Controlled Trials (RCTs), systematic reviews, and meta-analyses were included if they assessed the effects of propranolol on cancer outcomes such as mortality, survival, recurrence, or biomarkers of tumour regression. A narrative synthesis was performed to summarise the findings. RESULTS: Thirty-one studies were included, consisting of 7 RCTs, 4 systematic reviews and 20 meta-analyses. The evidence suggests that propranolol may improve cancer outcomes, especially when administered perioperatively, by reducing recurrence risk. However, the results remain inconclusive regarding its use in combination with chemotherapy or radiotherapy, as studies showed mixed results. The timing of propranolol administration, alongside its combination with other cancer therapies, appears to be a key factor in its effectiveness. CONCLUSION: Propranolol has potential as an adjunctive therapy in cancer treatment, particularly in reducing recurrence risk during the perioperative period. However, further clinical trials are needed to better define its role in cancer therapy, particularly regarding optimal treatment regimens and patient populations.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".