Evidence of β-blockers drug repurposing for the treatment of triple negative breast cancer: A systematic review
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is a particularly aggressive subtype of breast cancer (BC) for which limited therapeutic options are available. Recently, β-blockers (BBs) have been suggested to have favorable effects in the treatment of BC. The aim of this systematic review was to collect evidence from preclinical and clinical studies concerning the scientific evidence for the repurposing of BBs in TNBC treatment. PubMed database was searched to retrieve studies of interest published up to 30/01/2018. All preclinical studies using TNBC in vitro and in vivo models and assessing the effect of any molecule with sympatholytic or sympathomimetic activity on adrenergic receptors were included. Clinical studies concerning BBs were considered eligible. The Newcastle-Ottawa scale was used for the quality assessment of clinical studies. A total of 614 study references were retrieved. Forty-six preclinical studies were included. In in vitro studies, propranolol, a non-selective BB, significantly decreased proliferation, migration and invasion of TNBC cells. Consistently, in in vivo studies, propranolol inhibited metastasis, angiogenesis and tumor growth. Clinical studies, reporting evidence from a total of four distinct retrospective observational cohort studies, showed a beneficial effect of BBs in TNBC treatment. The overall quality of the clinical evidence collected was low. Preclinical evidence collected in this systematic review are in line with the results reported in the clinical studies retrieved, pointing towards a beneficial effect of BB in the treatment of TNBC. However, given the overall low quality of available evidence, no definite conclusion may be drawn.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".