부산대학교 도서관

Simple Summary: Thymomas are rare malignant epithelial tumors of the thymus. They show a spectrum of microscopic appearances (histotypes) that correlate with the risk of killing patients. The various high- and low-risk thymoma histotypes must be distinguished from each other (i.e., classified) to make correct treatment decisions. However, classification is often difficult, even for expert pathologists, since unequivocal microscopic and genetic features may not be present in each thymoma. Therefore, the current study had the aim to improve the classification of thymomas through the application of a novel method—artificial intelligence-assisted methylation profiling—that measures and compares the absence or presence of methyl groups (a chemical modification of the genetic material, i.e., DNA) across cohorts of tumors. The analysis of 113 thymomas revealed that most cases of each microscopically defined thymoma histotype shared a distinct methylation profile. However, tumor cases with overlapping profiles ('borderliners') and cases with quite different profiles ('outliers') were also detected. In conclusion, this type of methylation profiling is a valuable new tool to improve therapeutic decision-making through the refined classification of thymomas. It holds promise for identifying new thymoma variants and opening novel, personalized therapeutic perspectives. Background: Thymomas are malignant thymic epithelial tumors that are difficult to diagnose due to their rarity and complex diagnostic criteria. They represent a morphologically heterogeneous class of tumors mainly defined by "organo-typical" architectural features and cellular composition. The diagnosis of thymoma is burdened with a high level of inter-observer variability and the problem that some type-specific morphological alterations are more on the continuum than clear-cut. Methylation pattern-based classification may help to increase diagnostic precision, particularly in borderline cases. Methods and Results: We applied array-based DNA methylation analysis to a set of 113 thymomas with stringent histological annotation. Unsupervised clustering and t-SNE analysis of DNA methylation data clearly segregated thymoma samples mainly according to the current WHO classification into A, AB, B1, B2, B2/B3, B3, and micronodular thymoma with lymphoid stroma. However, methylation analyses separated the histological subgroups AB and B2 into two methylation classes: mono-/bi-phasic AB-thymomas and conventional/"B1-like" B2-thymomas. Copy number variation analysis demonstrated methylation class-specific patterns of chromosomal alterations. Interpretation: Our study demonstrates that the current WHO classification is generally well reflected at the methylation level but suggests that B2- and AB-thymomas are (epi)genetically heterogeneous. Methylation-based classifications could help to refine diagnostic criteria for thymoma classification, improve reproducibility, and may affect treatment decisions. [ABSTRACT FROM AUTHOR]