Estrogen receptor-beta (ER-beta) expression and non-small cell lung cancer (NSCLC) outcome.
Bibliographic record
Abstract
e21092 Background: Although women have an increased susceptibility to lung cancer, they also have a favorable clinical outcome. This is likely in part due to female specific genetic and hormonal factors. Methods: In the present study, expression of ER-beta, EGFR and HER2 was studied by immuno-histo chemistry using tissue samples from two cohorts: NSCLC diagnosed in 1999 in Manitoba and stage IV NSCLC patients from the NCIC-CTG BR 18 trial. Results: Tissue samples were available in 79 patients (32 females and 47 males) and the majority (75%) had resectable early stage disease in the Manitoba cohort. Forty- eight percent of patients expressed high levels of ER- beta (defined by > = 60, the median H-score) and its expression was comparable in males and females. The three-year overall survival of the group was 53% and females had significantly better survival compared to males. Three separate regression analysis were performed to test each biomarker in gender and stage adjusted model. A higher ER-beta level was associated with better survival in univariate (HR = 0.41, p = 0.009, 95%CI 0.21-0.80) and in multivariate (HR = 0.37, p = 0.008, 95%CI 0.18-0.77) analysis. Expression of EGFR and HER2 did not have significant impact on survival. In the 48 cases of stage IV NSCLC coming from the NCIC-CTG cohort, HER2 expression correlated with better outcome (HR = 0.39, p = 0.013) while higher ER-beta expression correlated with poorer survival (HR = 1.94, p = 0.047). Conclusions: These results suggest a differential impact of ER-beta expression on clinical outcome by stage of the disease, which needs to be explored further and may explain contradictory observations reported in the literature. No significant financial relationships to disclose.
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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 | 0.001 | 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) | 0.003 | 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".