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Updated: Sep 19, 2025

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Comprehensive Characterization of Somatic Mutation Timing Reveals the Evolutionary Trajectory of Lung Adenocarcinoma
Na Qin1,2, Zhoufeng Wang3,4, Xianfeng Xu1
1Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Abstract:
Lung adenocarcinoma (LUAD) is a heterogeneous disease with substantial genomic differences between individuals of Chinese and European ancestries. Deciphering the timing of driver mutations may lead to insights into tumor evolution that can inform diagnostic and therapeutic approaches for LUAD. In this study, we conducted whole-genome sequencing on LUAD samples from 251 patients with Chinese ancestry to reconstruct the evolutionary trajectories of somatic alterations, especially those across the noncoding regions. Tobacco-related mutations preferentially occurred early and plateaued at 28 cigarettes per day. Well-known driver mutations (e.g., EGFR, TP53, and RB1) also occurred at the early stage, displaying ancestry heterogeneity among smokers. In contrast to exogenous mutagens, endogenous mutagen-related alterations (APOBEC) occurred late. The 3' untranslated region (UTR) was the most frequently altered noncoding element in LUAD, with recurrent disrupting mutations in the 3' UTR of SFTPB and SFTPA1. Unlike other cancer types, TERT promoter mutations were observed specifically among female patients with LUAD. Clustered mutations (e.g., doublet base substitutions, multi-base substitutions, and kataegis) influenced LUAD evolution and were overrepresented in driver genes. These findings provide insights into the dynamic nature of genomic alterations during lung tumorigenesis.
Significance:
Reconstruction of genome-wide evolutionary histories and characterization of genomic heterogeneity in Chinese lung adenocarcinoma provides insight into cancer evolution, which may contribute to improved treatment and diagnostic strategies for lung cancer patients. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.
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