Abstract 2777: Smoking, clonal evolution, and lung cancer risk
Notice bibliographique
Résumé
Lung cancer is the leading cause of cancer mortality in the U.S. and in the world. Although tobacco use has steadily declined with a corresponding decrease in lung cancer incidence, the impact of smoking on the progression to lung cancer and its mechanisms are largely unresolved. This is further highlighted by the fact that the presence of oncogenic mutations persists in histologically normal tissue for years, even decades. Therefore, we sought to understand the mechanisms by which smoking exposure alters the somatic mutational landscape to result in lung oncogenesis. To observe somatic mutations, we used a rare-mutation detection technique, DuplexSeq, to analyze somatic variants across a 50kb panel of 31 genes associated with lung cancer from 400+ lung samples. We acquired DNA from lung cells from bronchoscopy brushings (BDRE cohort) and surgical samples of histologically normal tissue (PEACE and TRACERx cohorts) from people who currently, formerly, or never smoked cigarettes. We characterized mutations using bioinformatic pipelines and various databases. For each cohort, we analyzed the mutational landscape at the panel, gene, and nucleotide level. Across the panel, we observed higher summations of the variant allele frequencies (VAFs) from those with a smoking history than those who abstained from smoking, indicating that smoking selected for larger clones in cancer-associated genes. Despite the different nature of the samples across cohorts, numerous genes showed increased VAFs and altered dN/dS measures of selection independent of smoking status when compared to the control TIAM2 gene, suggesting that selection is already at play in histologically normal tissue. When looking at the nucleotide level, the most deleterious and cancer-associated mutations and drivers (based on the Cancer Genome Atlas and COSMIC) are most prevalent in the people who smoked. We also conducted Principal Component Analysis on the mutations from multiple lung regions of the same individual. Here, we observe higher intra-lung similarity of mutational landscape than similarity based on smoking status, highlighting the uniqueness of mutational landscapes to each individual. Using CRISPR technology, we recapitulated mutations observed in the human tissue samples in air-liquid interface and mouse models of the lung, where we observed disruptions in cell proliferation and differentiation. With the duality of characterizing the mutational landscape in the lung and modeling the observed mutations, we can begin to elucidate the mechanisms by which smoking can induce oncogenesis and understand the intermediate phenotypes in the progression of lung cancer. Altogether, these results show that while lung tissue exhibits mutation-driven clonal expansions independent of smoking history, that smoking enhances selection for particular cancer-associated mutations consistent with the increased risk of lung cancers, emphasizing the value of precision diagnoses and prevention. Citation Format: Edward J. Evans, Fabio Marongiu, Emilia Lim, Faiz Jabbar, Ferriol Calvet, Shi Biao Chia, Amy Briggs, Nuria Lopez-Bigas, Moumita Ghosh, Mariam Jamal-Hanjani, York Miller, Charles Swanton, James V. DeGregori. Smoking, clonal evolution, and lung cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2777.
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 | 0,001 | 0,001 |
| 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) | 0,008 | 0,002 |
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 ».