DNA methylation profiling defines clinically relevant biological subsets of non-small cell lung cancer

Non-small cell lung cancers (NSCLC) comprise multiple distinct biologic groups with different prognoses. For example, patients with epithelial-like tumors have a better prognosis and exhibit greater sensitivity to inhibitors of the epidermal growth factor receptor (EGFR) pathway than patients with mesenchymal-like tumors. Here, we test the hypothesis that epithelial-like NSCLCs can be distinguished from mesenchymal-like NSCLCs on the basis of global DNA methylation patterns.

To determine whether phenotypic subsets of NSCLCs can be defined on the basis of their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative reverse transcriptase PCR and methylation-specific PCR in formalin-fixed biopsies from patients with NSCLC who went on to fail front-line chemotherapy.

We show that patterns of methylation divide NSCLCs into epithelial-like and mesenchymal-like subsets as defined by gene expression and that these signatures are similarly correlated in NSCLC cell lines and tumors. We identify multiple differentially methylated regions, including one in ERBB2 and one in ZEB2, whose methylation status is strongly associated with an epithelial phenotype in NSCLC cell lines, surgically resected tumors, and formalin-fixed biopsies from patients with NSCLC who went on to fail front-line chemotherapy.

Our data show that patterns of DNA methylation can divide NSCLCs into two phenotypically distinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used as a platform for predictive biomarker discovery and development.