Methylation-Based Signatures for Gastroesophageal Tumor Classification

Nikolay Alabi; Dropen Sheka; Ashar Siddiqui; Edwin Wang

doi:10.3390/cancers12051208

Methylation-Based Signatures for Gastroesophageal Tumor Classification

Cancers (Basel). 2020 May 11;12(5):1208. doi: 10.3390/cancers12051208.

Authors

Nikolay Alabi¹, Dropen Sheka¹, Ashar Siddiqui², Edwin Wang²

Affiliations

¹ Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada.
² Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada.

Abstract

Contention exists within the field of oncology with regards to gastroesophageal junction (GEJ) tumors, as in the past, they have been classified as gastric cancer, esophageal cancer, or a combination of both. Misclassifications of GEJ tumors ultimately influence treatment options, which may be rendered ineffective if treating for the wrong cancer attributes. It has been suggested that misclassification rates were as high as 45%, which is greater than reported for junctional cancer occurrences. Here, we aimed to use the methylation profiles of GEJ tumors to improve classifications of GEJ tumors. Four cohorts of DNA methylation profiles, containing ~27,000 (27k) methylation sites per sample, were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Tumor samples were assigned into discovery (n_EC = 185, n_GC = 395; EC, esophageal cancer; GC gastric cancer) and validation (n_EC = 179, n_GC = 369) sets. The optimized Multi-Survival Screening (MSS) algorithm was used to identify methylation biomarkers capable of distinguishing GEJ tumors. Three methylation signatures were identified: They were associated with protein binding, gene expression, and cellular component organization cellular processes, and achieved precision and recall rates of 94.7% and 99.2%, 97.6% and 96.8%, and 96.8% and 97.6%, respectively, in the validation dataset. Interestingly, the methylation sites of the signatures were very close (i.e., 170-270 base pairs) to their downstream transcription start sites (TSSs), suggesting that the methylations near TSSs play much more important roles in tumorigenesis. Here we presented the first set of methylation signatures with a higher predictive power for characterizing gastroesophageal tumors. Thus, they could improve the diagnosis and treatment of gastroesophageal tumors.

Keywords: MSS; Multi-Survival Screening Algorithm; esophageal cancer; gastric cancer; gastroesophageal cancer diagnosis; gastroesophageal junction cancer; methylation array-based profile; methylation signature; predictor; tumor characterization; tumor classification.