Genomic and epigenomic profiles distinguish pulmonary enteric adenocarcinoma from lung metastatic colorectal cancer

Abstract

Background: As a rare subtype of lung adenocarcinoma, the diagnosis of pulmonary enteric adenocarcinoma (PEAC) remains challenging due to overlapping morphologic spectrum with lung metastatic colorectal cancer (lmCRC). However, the molecular features of PEAC as a separate lung cancer entity are poorly understood.

Methods: We performed whole-exome sequencing and targeted bisulfite sequencing of 32 PEAC and 30 lmCRC to improve differential molecular characterization of the two diseases. We used machine learning methods to select key markers and developed a diagnostic classifier. In addition, we validated the classifier in the internal test cohort and an independently recruited external validation cohort with 17 PEAC and 7 lmCRC.

Findings: Our results showed that EGFR was the key driver mutation in PEAC but at a lower prevalence compared to typical lung adenocarcinomas, whereas ERBB2 and KRAS were more frequently observed in PEAC. By contrast, we observed significant enrichment of KRAS and APC mutations in lmCRC compared with PEAC. At the chromosome arm level, copy number variations in 13q, 14q, and 18p were the major chromosomal differences observed between PEAC and lmCRC. Furthermore, by comparing differentially methylated regions (DMRs), we established a neat DNA methylation-based classifier consisting of eight DMRs. This classifier correctly classified all samples in the training cohort and 95% of the samples in the internal test cohort. An external validation cohort of 24 cases recruited from multiple centers in China also reliably agreed with pathological diagnosis.

Interpretation: These results provide solid evidence of PEAC-specific genomic characteristics and demonstrate the potential utility of DNA methylation markers for auxiliary diagnosis of PEAC and lmCRC.

Funding: This work was supported by National key research and development project 2019YFC1315700, CAMS Key Laboratory of Translational Research on Lung Cancer (2018PT31035), and Beijing Natural Science Foundation (7222144).

Keywords: Lung metastatic colorectal cancer; Machine learning model; Pulmonary enteric adenocarcinoma.