The influence of insulin‐like Growth Factor‐1‐Receptor expression and endocrine treatment on clinical outcome of postmenopausal hormone receptor positive breast cancer patients: A Dutch TEAM substudy analysis
Bibliographic record
Abstract
BACKGROUND: Signaling via the Insulin-like Growth Factor type 1 Receptor (IGF1R) plays a crucial role in cancer development. In breast cancer (BC), IGF1R and estrogen receptor expression are correlated. In this current study we explored the hypothesis that postmenopausal hormone receptor positive (HR+ve) BC patients with high IGF1R tumor expression still have estrogen driven IGF1R stimulated tumor growth when treated with tamoxifen, resulting in detrimental clinical outcome compared to patients treated with exemestane. Additionally, we assessed the added value of metformin as this drug may lower IGF1R stimulation. METHODS: Of 2,446 Dutch TEAM patients, randomized to either exemestane for 5 years or sequential treatment (tamoxifen for 2-3 years followed by exemestane for another 3-2 years) tumor tissue microarray sections were immunohistochemically stained for IGF1R. Overall Survival (OS), Breast Cancer specific Survival (BCSS) and Relapse-Free Survival (RFS) were assessed in patient subgroups with low and high IGF1R expression, and in patients with or without metformin use. RESULTS: High IGF1R tumor expression was significantly associated with exemestane therapy for RFS (Hazard Ratio (HR) 0.74, 95% Confidence Interval (CI) 0.58-0.95, p = 0.02). In addition, the combination of metformin with exemestane resulted in improved efficacy, yielding a 5-yrs RFS of 95% (HR 0.32, 95% CI 0.10-1.00, p = 0.02, compared to sequential treatment). No relation was observed in tumors with low IGF-1R expression. CONCLUSION: This study suggests IGF1R as a potential biomarker of improved clinical outcome in HR+ve BC patients treated with exemestane. Adding metformin to exemestane treatment may add to this effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".