Phospho-Serine-118 Estrogen Receptor-α Detection in Human Breast Tumors <b> <i>in Vivo</i> </b>
Bibliographic record
Abstract
PURPOSE: To determine whether estrogen receptor (ER)-alpha specifically phosphorylated at Ser(118) is detectable in multiple human breast cancer biopsy samples. To gain insight into possible roles for P-Ser(118)-ERalpha in human breast cancer in vivo. EXPERIMENTAL DESIGN: A specific antibody for P-Ser(118)-ERalpha was validated for immunohistochemistry (IHC), and Western blot analysis confirmed IHC results. IHC was used to determine the relationship of P-Ser(118)-ERalpha to known prognostic markers and active mitogen-activated protein kinase (MAPK; erk1/2) expression. RESULTS: P-Ser(118)-ERalpha was significantly correlated with the expression of total ER, determined by ligand binding assay (r = 0.442, P = 0.002), but not with progesterone receptor expression or nodal status. P-Ser(118)-ERalpha was inversely correlated with histological grade (r = -0.34, P = 0.023), reflecting a similar trend for total ER (r = -0.287, P = 0.056). Categorical contingency analysis confirmed that P-Ser(118)-ERalpha was more frequently associated with lower than higher grade breast tumors (P = 0.038). In addition P-Ser(118)-ERalpha was significantly associated with detection of active MAPK (Erk1/2; Spearman r = 0.649, P < 0.0001; Fisher's exact test, P = 0.0004). CONCLUSIONS: P-Ser(118)-ERalpha detection is associated with a more differentiated phenotype and other markers of good prognosis in human breast cancer. P-Ser(118)-ERalpha is correlated with active MAPK in human breast tumor biopsies, suggesting the possibility that active MAPK either directly or indirectly has a role in the regulation of P-Ser(118)-ERalpha expression in vivo. These data provide evidence for a role of P-Ser(118)-ERalpha in human breast cancer in vivo.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".