Molecular subgrouping of atypical teratoid/rhabdoid tumors-a reinvestigation and current consensus

Abstract

Background: Atypical teratoid/rhabdoid tumors (ATRTs) are known to exhibit molecular and clinical heterogeneity even though SMARCB1 inactivation is the sole recurrent genetic event present in nearly all cases. Indeed, recent studies demonstrated 3 molecular subgroups of ATRTs that are genetically, epigenetically, and clinically distinct. As these studies included different numbers of tumors, various subgrouping techniques, and naming, an international working group sought to align previous findings and to reach a consensus on nomenclature and clinicopathological significance of ATRT subgroups.

Methods: We integrated various methods to perform a meta-analysis on published and unpublished DNA methylation and gene expression datasets of ATRTs and associated clinicopathological data.

Results: In concordance with previous studies, the analyses identified 3 main molecular subgroups of ATRTs, for which a consensus was reached to name them ATRT-TYR, ATRT-SHH, and ATRT-MYC. The ATRT-SHH subgroup exhibited further heterogeneity, segregating further into 2 subtypes associated with a predominant supratentorial (ATRT-SHH-1) or infratentorial (ATRT-SHH-2) location. For each ATRT subgroup we provide an overview of its main molecular and clinical characteristics, including SMARCB1 alterations and pathway activation.

Conclusions: The introduction of a common classification, characterization, and nomenclature of ATRT subgroups will facilitate future research and serve as a common ground for subgrouping patient samples and ATRT models, which will aid in refining subgroup-based therapies for ATRT patients.

Keywords: ATRT; consensus; meta-analysis; molecular subgroups.

Fig. 1

Overview flow charts on all analyses and samples.

Fig. 2

Methylation array analysis of the consensus dataset. (A) Unsupervised hierarchical clustering using the top 5000 most variable cytosine-guanine (CG) sites confirms the presence of 3 subgroups in the consensus dataset (325 samples). (B) t-SNE visualization of the analyzed dataset based on the 5000 most variable CG sites reproduces segregation into 3 main ATRT subgroups. Coloring of data points in the t-SNE plots displays the subgrouping as published by Johann et al (upper plot) or by Torchia et al (lower plot). Half transparent circles show the consensus subgroups used in this paper. (C) Sankey plot displaying the concordance between the subgroup calls using different methods (NMF, consensus clustering, and NMF + k-means based subgrouping). Numbers in each subgroup show the number of samples which have been assigned to the subgroup with the respective method.

Fig. 3

Cluster analysis based on Affymetrix array gene expression data. (A) Unsupervised hierarchical clustering using the top 1500 most variable genes in the consensus gene expression dataset. Annotations in the lower bar show the grouping as presented by Han et al and by Johann et al and the current methylation consensus calls. (B) Visualization of gene set enrichment analysis results as a radar plot. Axis displays the normalized enrichment values. Each ATRT subgroup is represented in the respective subgroup color.

Fig. 4

Clinical and genetic associations of ATRT subgroups. (A) Violin plots of age distribution in ATRT subgroups. (B) Frequency of CNS tumor location by consensus subgroup represented by pie charts. (C) Oncoprint Fig. displays the distribution of various types of SMARCB1 mutations among the consensus set. (D) Distribution of SMARCB1 alterations and chr22 changes in the consensus set as determined by methylation array analyses, represented by pie charts. Significance between subgroups was calculated with chi-square test.

Fig. 5

Consensus overview of ATRT subgroups. Schema of salient clinical and molecular characteristics of ATRT subgroups.