MiR-100 is a predictor of endocrine responsiveness and prognosis in patients with operable luminal breast cancer
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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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".