Aromatase Inhibitors in Human Lung Cancer Therapy
Bibliographic record
Abstract
Lung cancer is the most common cancer in the world. It is a highly lethal disease in women and men, and new treatments are urgently needed. Previous studies implicated a role of estrogens and estrogen receptors in lung cancer progression, and this steroidal growth-stimulatory pathway may be promoted by tumor expression and activity of aromatase, an estrogen synthase. We found expression of aromatase transcripts and protein in human non-small cell lung cancer (NSCLC) cells using reverse transcription-PCR and Western immunoblots, respectively. Aromatase staining by immunohistochemistry was detected in 86% of archival NSCLC tumor specimens from the clinic. Further, biological activity of aromatase was determined in NSCLC tumors using radiolabeled substrate assays as well as measure of estradiol product using ELISA. Significant activity of aromatase occurred in human NSCLC tumors, with enhanced levels in tumor cells compared with that in nearby normal cells. Lung tumor aromatase activity was inhibited by anastrozole, an aromatase inhibitor, and treatment of tumor cells in vitro with anastrozole led to significant suppression of tumor cell growth. Similarly, among ovariectomized nude mice with A549 lung tumor xenografts, administration of anastrozole by p.o. gavage for 21 days elicited pronounced inhibition of tumor growth in vivo. These findings show that aromatase is present and biologically active in human NSCLCs and that tumor growth can be down-regulated by specific inhibition of aromatase. This work may lead to development of new treatment options for patients afflicted with NSCLC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".