FOXA1 Expression in Breast Cancer—Correlation with Luminal Subtype A and Survival
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: FOXA1, a forkhead family transcription factor, is essential for optimum expression of approximately 50% of estrogen receptor alpha (ERalpha):estrogen responsive genes. FOXA1 is expressed in breast cancer cells. It segregates with genes that characterize the luminal subtypes in DNA microarray analyses. The utility of FOXA1 as a possible independent prognostic factor has not been determined in breast cancers. MATERIALS AND METHODS: A tissue microarray comprising tumors from 438 patients with 15.4 years median follow-up was analyzed for FOXA1 expression by immunohistochemistry. Interpretable FOXA1 expression obtained in 404 patients was analyzed along with other prognostic factors like tumor grade, size, nodal status, ER, progesterone receptor (PR), and HER2/neu. RESULTS: FOXA1 expression (score >3) was seen in 300 of 404 breast cancers and it correlated with ER (P = 0.000001), PR (P = 0.00001), and luminal A subtype (P = 0.000001). Loss of expression was noted with worsening tumor grade (P = 0.001). Univariate analysis showed nodal status (P = 0.0000012), tumor size (P = 0.00001), FOXA1 (P = 0.0004), and ER (P = 0.012) to be predictors of breast cancer-specific survival. Multivariate analysis showed only nodal status (P = 0.001) and tumor size (P = 0.039) to be significant prognostic factors, whereas FOXA1 (P = 0.060) and ER (P = 0.131) were not significant. In luminal subtype A patient subgroup, FOXA1 expression was associated with better cancer-specific survival (P = 0.024) and in ER-positive subgroup, it was better predictor of cancer-specific survival (P = 0.009) than PR (P = 0.213). CONCLUSION: FOXA1 expression correlates with luminal subtype A breast cancer and it is significant predictor of cancer-specific survival in patients with ER-positive tumors. Prognostic ability of FOXA1 in these low-risk breast cancers may prove to be useful in clinical treatment decisions.
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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 | 0.002 | 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