Abstract 741: Molecular and clinicopathological characteristics of Chinese non-small cell lung cancers with <i>ROS1</i> gene fusions identified by next-generation sequencing
Notice bibliographique
Résumé
Abstract Background ROS1 gene rearrangements are reported at a prevalence of 1-2% in unselected cancer populations including non-small cell lung cancer (NSCLC), and notably higher in lung adenocarcinoma. More than twenty ROS1 fusion partners have been identified, which continues to increase due to the adoption of next-generation sequencing (NGS) for genetic testing. The tyrosine kinase inhibitors (TKIs) including crizotinib, lorlatinib, and entrectinib, have demonstrated favorable efficacy in the treatment of ROS1-rearranged NSCLCs. Methods A total of 17,158 Chinese non-small cell lung cancer (NSCLC) patients whose tumor specimen and/or circulating cell-free DNA (cfDNA) underwent genomic profiling by hybridization capture-based targeted NGS of exons and introns of cancer related genes were retrospectively reviewed. Clinical characteristics, treatment history, and progression-free survival (PFS) for crizotinib of a subset of 37 patients were further evaluated. Results A total of 288 ROS1-positive NSCLC patients were identified in the cohort at a frequency of 1.7% (288/17158). ROS1 fusions retaining the intact ROS1 kinase domain were confirmed in a subset of 258 patients (1.5%) with the most common fusion partner being CD74 (104/258, 40%), followed by EZR (34/258, 13%), SDC4 (28/258, 11%), SLC34A2 (27/258, 11%), and other recurrent ones at low frequency including TPM3, GOPC, MYH9, and CCDC6. Most frequent breakpoints on ROS1 are spreading in introns 33 (37%), 31 (25%), 32 (17%) and 34 (11%) with no obvious hotspots. 5.4% of cases (14/258) were fused to intergenic regions (IGR), mainly on the same chromosome of ROS1, with unknown fusion products. Notably, CD74 (63%) and EZR (50%) are more frequently fused with ROS1 intron 33, while ROS1 intron 31 was the most common breakpoint region of fusion events involving SDC4 (79%) and SLC34A2 (81%). Median PFS (mPFS) was not significantly different between fusions involving breakpoints in ROS1 introns 31-34 for crizotinib treatment, whereas non-CD74 patients (n=23) trended to demonstrate longer mPFS than that of CD74-ROS1 cases (n=14) (12.0 versus 9.9 months). Previously reported acquired resistance to crizotinib in ROS1-rearranged patients, including ROS1 G2032R and S1986F mutations, were observed in 11 out of 37 patients upon disease progression. In addition, two out of three patients who had uncharacterized fusion partners (IGR-ROS1) achieved durable clinical benefit on crizotinib. Conclusion We hereby report the prevalence of ROS1 fusions of 1.7% in a large Chinses NSCLC population detected by NGS testing and the most frequent fusion partners including CD74, EZR, SDC4, and SLC34A2. Crizotinib has demonstrated robust response in treating ROS1-rearranged NSCLC, particularly in non-CD74 ROS1-positive patients. Keywords ROS1, gene fusion, non-small cell lung cancer, crizotinib, breakpoint, next-generation sequencing Citation Format: Dongling Gao, Yuchen Han, Zhihua Zhao, Qiuxiang Ou, Xiaoling Tong, Ruiying Zhao, Nan Dong, Xue Wu, Wencai Li, Guozhong Jiang. Molecular and clinicopathological characteristics of Chinese non-small cell lung cancers with ROS1 gene fusions identified by next-generation sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 741.
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 | 0,001 |
| 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 | 0,000 | 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 ».