1. Whole exome sequencing in Chinese mucinous pulmonary adenocarcinoma uncovers specific genetic variations different from lung adenocarcinoma
- Author
-
Chenyue Zhang, Kai Wang, Wenjie Liu, Jiamao Lin, Zhenxiang Li, Hui Wang, Chenglong Zhao, Yanhua Chen, Shuangxiu Wu, Airong Yang, Jiayan Wu, and Haiyong Wang
- Subjects
whole exome sequencing ,mucinous pulmonary adenocarcinoma ,genomics ,immunological features ,therapeutics ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
BackgroundAs a rare subtype of primary lung adenocarcinoma (LUAD), mucinous pulmonary adenocarcinoma (MPA) was considered a distinctive entity with unfavorable outcomes. Therefore, there is a great need for a better understanding of the genomic and immunological landscape of this rare tumor type, which would inform improved therapeutic strategies.MethodsA total of 96 patients histologically confirmed with MPA were recruited from Shandong Cancer Hospital and Institute (SCH). Single nucleotide variation (SNV), copy number variation (CNV), genomic instability, and immunological landscape insights into 96 MPA patients were identified using WES.ResultsWe demonstrated that MPAs had marked different genomic alterations and were more complex in genomic profiles than LUADs. Mutations in Tumor Protein 53 (TP53) and CYP7A Promoter-Binding Factor (CPF) pathways significantly shortened survival whereas mutations in Notch and Wnt pathways significantly prolonged survival in MPA. Besides, we demonstrated that mutations in immune-related genes influenced outcomes, with mutations in TP53, Ataxia Telangiectasia Mutated (ATM), Polymerase (DNA) Delta 1 (POLD1), and Epidermal Growth Factor Receptor (EGFR) correlated with worsened survival.ConclusionsWe not only depicted the genetic and immunologic landscape of Chinese MPA but also reveal its distinction from LUAD in genomic and immune context. Our findings may provide opportunities for therapeutic susceptibility among Chinese MPA patients.
- Published
- 2022
- Full Text
- View/download PDF