Abstract 1607: Airway gene expression alterations associated with lung cancer chemoprevention using green tea extract
Notice bibliographique
Résumé
Abstract Lung cancer is the leading cause of cancer death in the United States and in the world. Identifying an effective chemopreventive agent for lung cancer would reduce lung cancer mortality by reversing, preventing, or delaying carcinogenic progression. Several randomized, controlled lung cancer chemoprevention trials have produced neutral or harmful results; with the exception of oral iloprost that demonstrated a small, but significant improvement in histology after 6 months of treatment in former smokers. One of the challenges in lung cancer chemoprevention trials have been the lack of surrogate endpoints to establish drug efficacy. In this study we use molecular profiling to study alterations assoicated with changes in airway histology and treatment with a promising chemopreventative, green tea extract. Airway epithelial cells were collected from patients with bronchial dysplasia during bronchoscopy at baseline, on-treatment, and post-treatment with green tea extract or placebo ranging from 2 to 6 months (n=27 patients, n=63 samples). RNA from the samples was processed and hybridized to Affymetrix Human Gene 1.0 ST arrays. Gene-level expression data was obtained using the Robust Multiarray Average (RMA) algorithm and ANOVA and linear modeling strategies were used to identify gene expression alterations associated with dysplasia regression and green tea extract treatment. Cancer and smoking-related pathway gene expression signatures were used to predict the pathway activation of each sample using a binary regression model. Gene set enrichment analysis (GSEA) was used to identify important biological pathways. Airway gene expression alterations associated with dysplasia regression were identified and pathways such as p53 and mTOR signaling were enriched among genes up-regulated in airways with dysplasia. The E2F3 oncogenic pathway was also found to be significantly altered in airways with dysplasia (p<0.05). The effect of green tea extract on airway gene expression was more pronounced among former versus current smokers. As a result, the degree to which pathways were modulated by green tea extract varied with smoking status. Pathways related to metabolism of xenobiotics, and glutathione, and retinol were decreased among treated current smokers while genes related to oxidative phosphorylation and DNA repair were increased in treated former smokers (FDR q-value<0.05). Our studies suggest that airway gene expression is altered in high-risk smokers with premalignant airway lesions and that this airway “field of injury” can be modulated by treatment with green tea extract especially among former smokers. We are currently investigating whether airway gene-expression can serve as an intermediate biomarker of response to green tea extract and identify those smokers who are most likely to benefit from this type of chemopreventive strategy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1607. doi:1538-7445.AM2012-1607
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».