Atypical teratoid/rhabdoid tumors (ATRTs) are very aggressive childhood malignancies of the central nervous system. The underlying genetic cause are inactivating bi-allelic mutations in SMARCB1 or (rarely) in SMARCA4. ATRT-SMARCA4 have been associated with a higher frequency of germline mutations, younger age, and an inferior prognosis in comparison to SMARCB1 mutated cases. Based on their DNA methylation profiles and transcriptomics, SMARCB1 mutated ATRTs have been divided into three distinct molecular subgroups: ATRT-TYR, ATRT-SHH, and ATRT-MYC. These subgroups differ in terms of age at diagnosis, tumor location, type of SMARCB1 alterations, and overall survival. ATRT-SMARCA4 are, however, less well understood, and it remains unknown, whether they belong to one of the described ATRT subgroups. Here, we examined 14 ATRT-SMARCA4 by global DNA methylation analyses. We show that they form a separate group segregating from SMARCB1 mutated ATRTs and from other SMARCA4-deficient tumors like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) or SMARCA4 mutated extra-cranial malignant rhabdoid tumors. In contrast, medulloblastoma (MB) samples with heterozygous SMARCA4 mutations do not group separately, but with established MB subgroups. RNA sequencing of ATRT-SMARCA4 confirmed the clustering results based on DNA methylation profiling and displayed an absence of typical signature genes upregulated in SMARCB1 deleted ATRT. In summary, our results suggest that, in line with previous clinical observations, ATRT-SMARCA4 should be regarded as a distinct molecular subgroup.

The interaction of CNS tumors with infiltrating lymphocytes plays an important role in their initiation and progression and might be related to therapeutic responses. Gene expression-based methods have been successfully used to characterize the tumor microenvironment. However, methylation data are now increasingly used for molecular diagnostics and there are currently only few methods to infer information about the microenvironment from this data type. Using an approach based on differential methylation and principal component analysis, we developed DIMEimmune (Differential Methylation Analysis for Immune Cell Estimation) to estimate CD4 + and CD8 + T cell abundance as well as tumor-infiltrating lymphocytes (TILs) scores from bulk methylation data. Well-established approaches based on gene expression data and immunohistochemistry-based lymphocyte counts were used as benchmarks. The comparison of DIMEimmune to the previously published MethylCIBERSORT and MeTIL algorithms showed an improved correlation with both gene expression-based and immunohistological results across different brain tumor types. Further, we applied our method to large datasets of glioma, medulloblastoma, atypical teratoid/rhabdoid tumors (ATRTs) and ependymoma. High-grade gliomas showed higher scores of tumor-infiltrating lymphocytes than lower-grade gliomas. There were overall only few tumorinfiltrating lymphocytes in medulloblastoma subgroups. ATRTs were highly infiltrated by lymphocytes, most prominently in the MYC subgroup. DIMEimmune-based estimates of TILs were a significant prognostic factor in the overall cohort of gliomas and medulloblastomas, but not within methylation-based diagnostic subgroups. To conclude, DIMEimmune allows for robust estimates of TIL abundance and might contribute to establishing them as a prognostic or predictive factor in future studies of CNS tumors.