Progress in lung cancer chemoprevention.
Notice bibliographique
Résumé
ED07-03 Lung cancer is the most common cause of cancer death world-wide with more than 1.3 million people die of lung cancer annually. The 5-year survival rates after the diagnosis of lung cancer have improved only marginally in the last three decades. While early detection and chemoprevention has been shown to be effective in reducing the incidence and mortality of other epithelial cancer such as breast cancer, there are unique challenges applying the same cancer control strategy in lung cancer because of difficulty in localizing intra-epithelial neoplastic lesions (IEN) and studying the natural history of these lesions. Unlike other epithelial organs, the lung is an internal organ consisting of a complex branching system of conducting airways leading to gas exchange units. Lung cancer consists of several cell types instead of a single cell type. They are preferentially located in different parts of the bronchial tree. There is no single method that can detect IEN lesions in the entire bronchial epithelium and allow tissue sampling for pathological diagnosis and molecular profiling. Autofluorescence bronchoscopy is a major advance to improve detection of pre-invasive lesions in the central airways to guide biopsies. It has contributed to improved histopathological classification and molecular profiling of IEN lesions. However, the biopsy procedure may remove these small lesions making it difficult to study their natural history. As well, false positive fluorescence can occur in areas of inflammation. Progress has been made in the use of biophotonic imaging methods such as optical coherence tomography (OCT) to characterize these lesions without removal them. Using a prototype endoscopic OCT system (Pentax Corp., Japan), we were able to discriminate high-grade dysplasia and carcinoma in-situ from lower grade IEN lesions and normal epithelium in-vivo during a standard bronchoscopic procedure. Further development is in progress to improve the resolution and to include Doppler measurement to evaluate the vascular changes in IEN lesions. Electromagnetic navigational system using virtual CT as a road map is now available to localize spiral CT detected lesions in the lung as part of a bronchoscopic procedure. Further refinement is needed to improve the accuracy of localization of small peripheral preneoplastic lesions in the lung for biopsy. In order to develop effective chemoprevention agents, a better understanding of the cancer development process and the differences between histological cell types are needed. Sequential biopsy material obtained from the same site as the lesion progress from a low grade IEN lesion to carcinoma in-situ or early invasive cancer provides an extremely valuable tissue resource for genome wide studies. Significant differences have been found in genomic pathways between small cell and non-small cell lung cancer as well as between squamous cell carcinoma and adenocarcinoma. Abnormal gene expression has also been observed in long term heavy smokers despite smoking cessation. Armed with this new knowledge, it is now possible to begin evaluation of novel chemopreventive agents using a modified Phase I-IIa clinical trial design to determine if these agents can modulate pathways that are important in the development of lung cancer. As an example, in an open study of Polyphenon E (Mitsui Norin, Japan), a biological effect can be demonstrated in bronchial epithelial cells retrieved by bronchial brushing before and one month after treatment with Polyphenon E using gene expression profiling. A different gene expression profile was observed between current and former smokers. Changes in inflammatory biomarkers in the BAL and plasma were also observed in this short term clinical trial. Measurements of tea catechins levels in the plasma allow comparison of these effects in human versus animal or cell culture models of lung cancer. This strategy, if confirmed, would enable rapid screening of new chemopreventive agents before proceeding to more time consuming and more costly Phase IIb study. Supported by NIH-NCI grant 1PO1-CA96964, U01CA96109 and Genome Canada
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,019 |
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 ».