A phosphorylation code for oestrogen receptor-α predicts clinical outcome to endocrine therapy in breast cancer
Bibliographic record
Abstract
To determine the relationship of the multiple sites of oestrogen receptor alpha (ERalpha) phosphorylation to clinical outcome after tamoxifen therapy, sections from tissue microarrays representing over 300 ER+ breast cancers from patients who were treated with surgery+radiation and then tamoxifen were used for immunohistochemical determination of total ERalpha, p-S104/106-ERalpha, p-S118-ERalpha, p-S167-ERalpha, p-S282-ERalpha, p-S294-ERalpha, p-T311-ERalpha and p-S559-ERalpha. Relationships of phosphorylated ERalpha to overall and relapse-free survival (RFS; breast cancer death or recurrence) were tested using single (univariate) and multiple (multivariate) predictor statistical models. Large tumour size, node positivity, high grade, progesterone receptor (PR) negative status and low levels of p-S282-ERalpha were significantly associated with reduced overall survival (OS). Along with tumour size and node status, a novel phosphorylation score (P(7) score > or = 3), taking into account all seven p-ERalpha sites, was significantly associated with reduced OS in univariate and multivariate analyses (hazard ratio (HR)=2.24, 95% confidence interval (CI) 1.15-4.34, n=335; P=0.018). Along with tumour size, node status, grade and PR status, a high P(7) score (> or = 3) was significantly associated with reduced RFS in univariate and multivariate analyses (HR=1.71, 95% CI 1.03-2.86, n=332; P=0.039). Since ERalpha is the site at which integration of diverse signals occurs to regulate breast cancer growth and survival, the ERalpha phosphorylation score may be a surrogate marker of the balance between oestrogen-dependent and crosstalk-dependent receptor activity, and is potentially a prognostic marker of clinical outcome in a tamoxifen-treated population of patients.
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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 | 0.000 | 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".