Pulmonary enteric adenocarcinoma is a rare non-small cell lung cancer subtype. It is poorly characterized and cannot be distinguished from metastatic colorectal or upper gastrointestinal adenocarcinomas by means of routine pathological methods. As DNA methylation patterns are known to be highly tissue specific, we aimed to develop a methylation-based algorithm to differentiate these entities. To this end, genome-wide methylation profiles of 600 primary pulmonary, colorectal, and upper gastrointestinal adenocarcinomas obtained from The Cancer Genome Atlas and the Gene Expression Omnibus database were used as a reference cohort to train a machine learning algorithm. The resulting classifier correctly classified all samples from a validation cohort consisting of 680 primary pulmonary, colorectal and upper gastrointestinal adenocarcinomas, demonstrating the ability of the algorithm to reliably distinguish these three entities. We then analyzed methylation data of 15 pulmonary enteric adenocarcinomas as well as four pulmonary metastases and four primary colorectal adenocarcinomas with the algorithm. All 15 pulmonary enteric adenocarcinomas were reliably classified as primary pulmonary tumors and all four metastases as well as all four primary colorectal cancer samples were identified as colorectal adenocarcinomas. In a t-distributed stochastic neighbor embedding analysis, the pulmonary enteric adenocarcinoma samples did not form a separate methylation subclass but rather diffusely intermixed with other pulmonary cancers. Additional characterization of the pulmonary enteric adenocarcinoma series using fluorescence in situ hybridization, next-generation sequencing and copy number analysis revealed KRAS mutations in nine of 15 samples (60%) and a high number of structural chromosomal changes. Except for an unusually high rate of chromosome 20 gain (67%), the molecular data was mostly reminiscent of standard pulmonary adenocarcinomas. In conclusion, we provide sound evidence of the pulmonary origin of pulmonary enteric adenocarcinomas and in addition provide a publicly available machine learning-based algorithm to reliably distinguish these tumors from metastatic colorectal cancer.
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We gratefully acknowledge the excellent technical assistance of Daniel Teichmann, Alexandra Förster, Vera Arnemann and Didier Nana-Kouegoua.
Conflict of interest
The authors declare that they have no conflict of interest.
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