Aromatase Expression Predicts Survival in Women with Early-Stage Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Estrogen signaling is critical in the progression of tumors that bear estrogen receptors. In most patients with breast cancer, inhibitors that block interactions of estrogen with its receptors or suppress the production of endogenous estrogens are important interventions in the clinic. Recent evidence now suggests that estrogen also contributes to the pathogenesis of non-small cell lung cancer (NSCLC). We used a human lung cancer xenograph model system to analyze the effect of aromatase or estradiol on tumor growth. We further examined the level of protein expression of aromatase in 422 patients with NSCLC using a high-density tissue microarray. Results were confirmed and validated on an independent patient cohort (n = 337). Lower levels of aromatase predicted a greater chance of survival in women 65 years and older. Within this population, the prognostic value of aromatase was greatest in earlier stage lung cancer (stage I/II). In addition, for women with no history of smoking, lower aromatase levels were a strong predictor of survival. Our findings implicate aromatase as an early-stage predictor of survival in some women with NSCLC. We predict that women whose lung cancers have higher levels of aromatase might be good candidates for targeted treatment with aromatase inhibitors.
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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 | 0.000 | 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) | 0.002 | 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".