Abstract 896: The airway field of injury reflects gene expression changes associated with the presence of lung squamous premalignant lesions
Notice bibliographique
Résumé
Abstract Lung squamous cell carcinoma (SCC) arises in the epithelial layer of the bronchial airways and is preceded by the development of premalignant lesions (PMLs). The molecular events involved in the progression of PMLs to lung SCC are not clearly understood as not all PMLs that develop go on to form carcinoma. In addition, the majority of lung cancer chemoprevention agents tested to date are ineffective. Molecular characterization of the airway field of injury in individuals with PMLs could provide novel insights into the earliest molecular events associated with carcinogenesis and identify biomarkers to guide lung cancer detection and chemoprevention. RNA-sequencing was conducted on cytologically normal airway brushings from current and former smokers with (n = 50) and without (n = 25) PMLs as part of the British Columbia Lung Health Study. Linear modeling strategies were used to identify 280 differentialy expressed genes at FDR<0.002 between subjects with and without PMLs. Pathway analysis using GSEA and ROAST revealed enrichment of genes involved in the electron transport chain and oxidative phosphorylation pathways in subjects with PMLs. These findings were validated by measuring the cellular bioenergetics of cultured epithelial cells from biopsies of PMLs and non-lesion areas. Baseline oxygen consumption rates were 2.5 fold higher (p<0.001) and the spare respiratory capacity was 1.5 fold higher (p<0.001) in PML cultures. These data suggest that metabolism-associated gene expression observed in the field of injury of PMLs is correlated with PMLs. In addition, there is a significant concordant enrichment (FDR<0.05) between the signature and gene expression in PMLs adjacent to SCC tumors, in SCC tumors, and in the field of individuals with lung cancer. This concordance led to the development of a 200-gene biomarker that accurately predicts the presence of PMLs (AUC = 0.90 n = 17 independent samples). Importantly, this biomarker was also predictive (AUC -.72) of progression/stability vs. regression of these premalignant lesions in an independent cohort of cytologically normal airway brushings collected as part of the RPCI screening clinic (n = 18). This is the first study to comprehensively profile gene expression changes in airway epithelial cells in the presence of PMLs. A subset of these changes reflects the earliest changes in the process of lung squamous cell carcinogenesis including the genes involved in cellular metabolism. However, the molecular alterations in the field of injury are dynamic as bronchial lesions either progress or regress these changes may be leveraged to monitor efficacy in chemoprevention trials. In addition monitoring molecular changes in high-risk smokers may identify smokers with PMLs that should receive lung cancer screening as well as lay the foundation for personalized lung cancer chemoprevention. Citation Format: Sarah A. Mazzilli, Ania Tassinari, Yaron Gethalter, Gang Lui, Mary Pine, Stephen Lam, Mary Reid, Suso Platero, Marc Lenburg, Avrum Spira, Jennifer Beane. The airway field of injury reflects gene expression changes associated with the presence of lung squamous premalignant lesions. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 896.
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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,005 | 0,001 |
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 ».