Smoking, sex, and nonâSmall cell lung cancer: Steroid hormone receptors in tumor tissue (S0424)
Bibliographic record
Abstract
© The Author(s) 2018. Background: To what extent steroid hormones contribute to lung cancer in male and female never smokers and smokers is unclear. We examined expression of hormone receptors in lung tumors by sex and smoking. Methods: Patients with primary non–small cell lung cancer were recruited into an Intergroup study in the United States and Canada, led by SWOG (S0424). Tumors from 813 cases (450 women and 363 men) were assayed using immunohistochemistry for estrogen receptor (ER)–a, ER-b, progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Linear regression was used to examine differences in expression by sex and smoking status. Cox proportional hazard models were used to estimate survival associated with the receptors. All statistical tests were two-sided. Results: In ever smokers, postmenopause and oral contraceptive use were associated with lower nuclear ER-b (P ¼ .02) and total (nuclear þ cytoplasmic) PR expression (P ¼ .02), respectively. Women had lower cytoplasmic ER-a (regression coefficient [b], or differences in H-scores ¼ –15.8, P ¼ .003) and nuclear ER-b (b ¼ –12.8, P ¼ .04) expression than men, adjusting for age, race, and smoking. Ever smokers had both higher cytoplasmic ER-a (b ¼ 45.0, P < .001) and ER-b (b ¼ 25.9, P < .001) but lower total PR (b ¼ –42.1, P < .001) than never smokers. Higher cytoplasmic ER-a and ER-b were associated with worse survival (hazard ratio ¼ 1.73, 95% confidence interval [CI] ¼ 1.15 to 2.58, and HR ¼ 1.59, 95% CI ¼ 1.08 to 2.33, respectively; quartiles 4 vs 1). Conclusions: Lower expression of nuclear ER-b in women supports the estrogen hypothesis in lung cancer etiology. Increasing cytoplasmic ER-a and ER-b and decreasing PR protein expression May be mechanisms whereby smoking disrupts hormone pathways.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".