Identifying gastric cancer molecular subtypes by integrating DNA-based hierarchical classification strategy and clinical stratification

BackgroundMolecular subtyping has been introduced to better understand the genetic landscape of gastric cancer (GC), but current subtyping methods only had limited success because of the mixed use of molecular features, a lack of strategy optimization, and the limited availability of GC samples. The community urgently calls for a precise, and easily adoptable subtyping method to enable DNA-based early screening and treatment. MethodsBased on TCGA subtypes, we developed a novel classifier, termed Hierarchical DNA-based Classifier for Gastric Cancer Molecular Subtyping (HCG), leveraging all DNA-level alterations as predictors, including gene mutations, copy number aberrations and methylations. By adding the closely related esophageal adenocarcinomas (EA) dataset, we expanded the TCGA GC dataset for training and testing HCG (n=453). We optimized HCG with three hierarchical strategies evaluated by their overall accuracy using Lasso-Logistic regression, and by their clinical stratification capacity using multivariate survival analysis. We used difference tests to identify subtype-specific DNA alteration biomarkers based on HCG defined subtypes. ResultsOur HCG classifier achieved an overall AUC score of 0.95 and significantly improved the clinical stratification of patients (overall p-value=0.032). 25 subtype-specific DNA alterations were identified by difference tests, including high level of mutations in SYNE1, ITGB4 and COL22A1 genes for the MSI subtype, high level of methylations of ALS2CL, KIAA0406 and RPRD1B genes for the EBV subtype. ConclusionsHCG is an accurate and robust classifier for DNA-based GC molecular subtyping with high-performing clinical stratification capacity. The training and test datasets and analysis programs of HCG are available at https://github.com/labxscut/HCG.