Atypical teratoid/rhabdoid tumor (ATRT) is one of the most common brain tumors in infants. Although the prognosis of ATRT patients is poor, some patients respond favorably to current treatments, suggesting molecular inter-tumor heterogeneity. To investigate this further, we genetically and epigenetically analyzed 192 ATRTs. Three distinct molecular subgroups of ATRTs, associated with differences in demographics, tumor location, and type of SMARCB1 alterations, were identified. Whole-genome DNA and RNA sequencing found no recurrent mutations in addition to SMARCB1 that would explain the differences between subgroups. Whole-genome bisulfite sequencing and H3K27Ac chromatin-immunoprecipitation sequencing of primary tumors, however, revealed clear differences, leading to the identification of subgroup-specific regulatory networks and potential therapeutic targets.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.