Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome.

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Abstract

Purpose: Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer remains unclear. Experimental Design: Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), and three small cell carcinomas (SCLC). Unsupervised bootstrap clustering was performed to identify DNA methylation subgroups, which were validated in 695 adenocarcinomas and 122 SqCCs. Subgroups were characterized by clinicopathological factors, whole-exome sequencing data, and gene expression profiles. Results: Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, remaining four epitypes were associated with unsupervised and supervised gene expression phenotypes, and differences in molecular features including global hypomethylation, promoter hypermethylation, genomic instability, expression of proliferation-associated genes, and mutations in KRAS, TP53, KEAP1, SMARCA4, and STK11. Furthermore, these epitypes were associated with clinicopathological features such as smoking history, and patient outcome. Conclusions: Our findings highlight one neuroendocrine and four adenocarcinoma epitypes associated with molecular and clinicopathological characteristics, including patient outcome. This study highlights the possibility to further subgroup lung cancer, and more specifically adenocarcinomas, based on epigenetic/molecular classification that could lead to more accurate tumor classification, prognostication, and tailored patient therapy.
Original languageEnglish
Pages (from-to)6127-6140
JournalClinical Cancer Research
Volume20
Issue number23
DOIs
Publication statusPublished - 2014

Bibliographical note

The information about affiliations in this record was updated in December 2015.
The record was previously connected to the following departments: Pathology, (Lund) (013030000), Thoracic Surgery (013230027), Oncology, MV (013035000)

Subject classification (UKÄ)

  • Cancer and Oncology

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