MiR-100 is a predictor of endocrine responsiveness and prognosis in patients with operable luminal breast cancer
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Bibliographic record
Abstract
PURPOSE: Overexpression of miR-100 in stem cells derived from basal-like breast cancers causes loss of stemness, induction of luminal breast cancer markers and response to endocrine therapy. We, therefore, explored miR-100 as a novel biomarker in patients with luminal breast cancer. METHODS: miR-100 expression was studied in 90 patients with oestrogen-receptor-positive/human-epidermal growth factor receptor 2-negative breast cancer enrolled in a prospective study of endocrine therapy given either preoperatively, or for the treatment of de novo metastatic disease. Response was defined as a Ki67 ≤2.7% after 21±3 days of treatment. The prognostic role of miR-100 expression was evaluated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) breast cancer datasets. Additionally, we explored the correlation between miR-100 and the expression its targets reported as being associated with endocrine resistance. Finally, we evaluated whether a signature based on miR-100 and its target genes could predict the luminal A molecular subtype. RESULTS: Baseline miR-100 was significantly anticorrelated with baseline and post-treatment Ki67 (p<0.001 and 0.004, respectively), and independently associated with response to treatment (OR 3.329, p=0.047). In the METABRIC dataset, high expression of miR-100 identified women with luminal A tumours treated with adjuvant endocrine therapy with improved overall survival (HR 0.55, p<0.001). miR-100 was negatively correlated with PLK1, FOXA1, mTOR and IGF1R expression, potentially explaining its prognostic effect. Finally, a miR-100-based signature developed in patients enrolled in the prospective study outperformed Ki67 alone in predicting the luminal A phenotype. CONCLUSIONS: Our findings suggest that miR-100 should be further explored as a biomarker in patients with luminal breast cancer.
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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) | 0.000 | 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) | 0.000 | 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 it