Dual effects of metformin on breast cancer proliferation in a randomized trial.
Bibliographic record
Abstract
519 Background: Metformin lowers breast cancer risk in observational studies in diabetics, but evidence for its clinical activity is scanty. We studied the metformin antiproliferative effect in a pre-surgical study. Since the antidiabetic effect of metformin is heterogeneous according to obesity and insulin resistance (IR), we also determined whether its antiproliferative effect was modified by risk biomarkers. Methods: After tumor biopsy, we randomly allocated 200 non-diabetic women with breast cancer to either metformin, 850 mg/bid (n=100) or placebo (n=100) for 4 wks. The primary endpoint was the post-pretreatment change in Ki-67 between arms. We explored effect modifications by STEPP and tested biomarker thresholds that showed an interaction with treatment on Ki-67. Results: Overall, median (IQR) Ki-67 was 19 (14-31) at baseline and 21 (14-32) after 4 wks and 18 (12-29) at baseline and 20 (13-31) after 4 wks in the metformin and placebo arm, respectively with mean increase of 4.0% (95%CI, -5.6 to 14.4) on metformin versus placebo. Multivariate analyses showed an increase of Ki-67 after 4 wks placebo in the following subgroups: HOMA> IR threshold, IGFBP3> highest quartile, IGFBP1< lowest quintile, IGFratio (IGF1/IGFBP3)< median, CRP> inflection point at STEPP analysis, HER2+ve tumors (versus –ve). Metformin blunted the increase of Ki-67 noted on placebo in these subgroups. Conclusions: Overall, metformin did not affect Ki-67 in most subjects with breast cancer. However, in exploratory analysis we identified subgroups of patients where metformin showed antiproliferative effect. Further studies to a personalized approach are warranted with selection of study populations. [Table: see text]
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".