Outcomes of Estrogen Receptor Negative and Progesterone Receptor Positive Breast Cancer
Bibliographic record
Abstract
PURPOSE: To describe the clinical features and outcomes of estrogen receptor negative (ER-) and progesterone receptor positive (PgR+) breast cancer. METHODS: We retrospectively reviewed a well-characterized database of sequential patients diagnosed with early stage invasive breast carcinoma. Outcomes of interest were time to relapse (TTR) and overall survival (OS). Multivariable Cox proportional hazards analysis was conducted to assess the association of ER-/PgR+ with TTR and OS in comparison to ER+ and to ER- and PgR negative (ER-/PgR-) tumors irrespective of HER2 status. ER and PgR expression was conservatively defined as 10% or greater staining of cancer cells. RESULTS: 815 patients were followed for a median of 40.5 months; 56 patients (7%) had ER-/PgR+, 624 (77%) had ER+ and 136 (17%) had ER-/PgR- phenotypes. Compared with ER+ tumors, ER-/PgR+ tumors were associated with younger age (50 versus 59 years, p=0.03), high grade (50% versus 24%, p<0.001) and more frequent HER2 overexpression/amplification (43% versus 14%, p<0.001). TTR for ER-/PgR+ was intermediate between ER+ and ER-/PgR- tumors, but was not significantly different from ER+ tumors. Recurrences in the ER-/PgR+ and ER-/PgR- groups occurred early in follow-up while in ER+ tumors recurrences continued to occur over the duration of follow-up. OS of ER-/PgR+ was similar to ER+ tumors and better than that of ER-/PgR- tumors. CONCLUSIONS: The ER-/PgR+ phenotype is associated with higher grade with HER2 overexpression/amplification and occurs more commonly in younger women. Risk of relapse and death more closely resembles ER+ than ER-/PgR- tumors suggesting this phenotype represents a group of more aggressive hormone receptor positive tumors.
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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 | 0.000 | 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 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".