Abstract LB-274: Whole-exome sequencing reveals tyrosine kinase-resistant mutations in pretreatment EGFR-mutant lung adenocarcinomas
Notice bibliographique
Résumé
Abstract Lung cancer is the most lethal malignancy around the world. In Hong Kong, chronic tobacco use is prevalent in male but <20% of female patients smoke. Epidermal growth factor receptor (EGFR) mutations affect 30% and 67% of male and female lung adenocarcinomas, respectively. Treatment responses to tyrosine kinase inhibitors (TKI) are not uniform and all patients eventually develop drug resistance. Mutation screening of immediate pre-treatment tumors is the most ideal for assessing drug targets but in practice, excision specimens of primary or metastatic sites are more efficient in terms of tumor quantity, timeliness and patient acceptance. However, information on the applicability of this approach and correlation with the clinical outcome is limited. For this purpose, we compared the whole exome mutation profiles of 39 excised EGFR mutant cancers treated with first line TKI with the clinical response type, including 15 stage I/II and 24 stage III/IV tumors. They included 16 non-responders (NR) defined clinically as those with stable or progressive disease, and 23 responders (R) defined by partial or complete tumor shrinkage. Considering only the non-synonymous SNV and INDEL mutations of coding regions of known actionable targets and genes listed in both the CGC and COSMIC cancer databases, the tumors harbored 54 mutated genes including 26 recurrently mutated and 28 genes involving only 1 tumor. Excluding EGFR, TP53 was the most commonly mutated gene occurring in 25/39 (64.1%) cases. Most known resistant genes involved in the EGFR and bypass signaling network showed mutations only in the NR group including recurrent mutations of PTEN (3/16), PIK3CA (2/16) and NF1 (2/16), and single case mutations of AKT1, ALK, RAF1 and KDR. Two EGFR network candidates showed mutations in both the NR and R groups, including HGF (2/16 NR, 1/23 R) and ROS1 (1 case in either group). Four post-treatment (post-TKI) tumors of acquired resistance were also analyzed, all of which showed EGFR T790M while no pre-treatment NR or R tumor harbored this mutation. Notably, mutations in the β-catenin pathway were prominent in the NR and post-TKI tumors, including APC (2/16 NR), CTNNB1 (1/16 NR, 1/4 post-TKI) and c-MYC (1/16 NR, 1/4 post-TKI) while they were not detected in the R tumors. Also, nonsense mutations of ARID1A was observed in 2/16 NR but none of the R tumors. In summary, our findings revealed candidate TKI resistant mutations involving the EGFR and bypass signaling networks in pre-treatment excision specimens particularly in non-responding patients. The EGFR T790M was not detectable in pre-treatment samples but was prevalent in post-TKI treated cancers. While this study is limited by its small cohort size, the findings indicate deep sequencing analysis of pre-treatment excision specimens of EGFR-mutant lung adenocarcinomas is warranted for detection of resistant mutations and predicting treatment response. Note: This abstract was not presented at the meeting. Citation Format: Xu-yuan Gao, Hang Xu, James CM Ho, Oscar SH Chan, Feng Xu, Junwen Wang, Victor HF Lee, Vicky PC Tin, Zhijie Xiao, Siqi Wang, Judy WP Yam, Maria P. Wong. Whole-exome sequencing reveals tyrosine kinase-resistant mutations in pretreatment EGFR-mutant lung adenocarcinomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-274. doi:10.1158/1538-7445.AM2017-LB-274
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 | 0,001 |
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 ».