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The large diversity of central nervous system (CNS) tumor types in children and adolescents results in disparate patient outcomes and renders accurate diagnosis challenging. In this study, we prospectively integrated DNA methylation profiling and targeted gene panel sequencing with blinded neuropathological reference diagnostics for a population-based cohort of more than 1,200 newly diagnosed pediatric patients with CNS tumors, to assess their utility in routine neuropathology. We show that the multi-omic integration increased diagnostic accuracy in a substantial proportion of patients through annotation to a refining DNA methylation class (50%), detection of diagnostic or therapeutically relevant genetic alterations (47%) or identification of cancer predisposition syndromes (10%). Discrepant results by neuropathological WHO-based and DNA methylation-based classification (30%) were enriched in histological high-grade gliomas, implicating relevance for current clinical patient management in 5% of all patients. Follow-up (median 2.5 years) suggests improved survival for patients with histological high-grade gliomas displaying lower-grade molecular profiles. These results provide preliminary evidence of the utility of integrating multi-omics in neuropathology for pediatric neuro-oncology.

Pediatric neoplasms in the central nervous system (CNS) show extensive clinical and molecular heterogeneity and are fundamentally different from those occurring in adults. Molecular genetic testing contributes to accurate diagnosis and enables an optimal clinical management of affected children. Here, we investigated a rare, molecularly distinct type of pediatric high-grade neuroepithelial tumor (n = 18), that was identified through unsupervised visualization of genome-wide DNA methylation array data, together with copy number profiling, targeted next-generation DNA sequencing, and RNA transcriptome sequencing. DNA and/or RNA sequencing revealed recurrent fusions involving the capicua transcriptional repressor (CIC) gene in 10/10 tumor samples analyzed, with the most common fusion being CIC::LEUTX (n = 9). In addition, a CIC::NUTM1 fusion was detected in one of the tumors. Apart from the detected fusion events, no additional oncogenic alteration was identified in these tumors. The histopathological review demonstrated a morphologically heterogeneous group of high-grade neuroepithelial tumors with positive immunostaining for markers of glial differentiation in combination with weak and focal expression of synaptophysin, CD56 and CD99. All tumors were located in the supratentorial compartment, occurred during childhood (median age 8.5 years) and typically showed early relapses. In summary, we expand the spectrum of pediatric-type tumors of the CNS by reporting a previously uncharacterized group of rare high-grade neuroepithelial tumors that share a common DNA methylation signature and recurrent gene fusions involving the transcriptional repressor CIC. Downstream functional consequences of the fusion protein CIC::LEUTX and potential therapeutic implications need to be further investigated.

Medulloblastoma with extensive nodularity (MBEN) is one of the few central nervous system (CNS) tumor entities occurring in infants which is traditionally associated with good to excellent prognosis. Some MBEN, however, have been reported with an unfavorable clinical course. We performed an integrated DNA/RNA-based molecular analysis of a multi-institutional MBEN cohort (n = 41) to identify molecular events which might be responsible for variability in patients' clinical outcomes. RNA sequencing analysis of this MBEN cohort disclosed two clear transcriptome clusters (TCL) of these CNS tumors: "TCL1 MBEN" and "TCL2 MBEN" which were associated with various gene expression signatures, mutational landscapes and, importantly, prognosis. Thus, the clinically unfavorable "TCL1 MBEN" subset revealed transcriptome signatures composed of cancer-associated signaling pathways and disclosed a high frequency of clinically relevant germline PTCH1/SUFU alterations. In contrast, gene expression profiles of tumors from the clinically favorable "TCL2 MBEN" subgroup were associated with activation of various neurometabolic and neurotransmission signaling pathways, and germline SHH-pathway gene mutations were extremely rare in this transcriptome cluster. "TCL2 MBEN" also revealed strong and ubiquitous expression of VSNL1 (visinin-like protein 1) both at the mRNA and protein level, which was correlated with a favorable clinical course. Thus, combining mutational and epigenetic profiling with transcriptome analysis including VSNL1 immunohistochemistry, MBEN patients could be stratified into clinical risk groups of potential value for subsequent treatment planning.

Molecular markers have become pivotal in brain tumor diagnostics. Mutational analyses by targeted next-generation sequencing of DNA and array-based DNA methylation assessment with copy number analyses are increasingly being used in routine diagnostics. However, the broad variety of gene fusions occurring in brain tumors is marginally covered by these technologies and often only assessed by targeted assays. Here, we assessed the feasibility and clinical value of investigating gene fusions in formalin-fixed paraffin-embedded (FFPE) tumor tissues by next-generation mRNA sequencing in a routine diagnostic setting. After establishment and optimization of a workflow applicable in a routine setting, prospective diagnostic application in a neuropathology department for 26 months yielded relevant fusions in 66 out of 101 (65%) analyzed cases. In 43 (43%) cases, the fusions were of decisive diagnostic relevance and in 40 (40%) cases the fusion genes rendered a druggable target. A major strength of this approach was its ability to detect fusions beyond the canonical alterations for a given entity, and the unbiased search for any fusion event in cases with uncertain diagnosis and, thus, uncertain spectrum of expected fusions. This included both rare variants of established fusions which had evaded prior targeted analyses as well as the detection of previously unreported fusion events. While the impact of fusion detection on diagnostics is highly relevant, it is especially the detection of "druggable" fusions which will most likely provide direct benefit to the patients. The wider application of this approach for unbiased fusion identification therefore promises to be a major advance in identifying alterations with immediate impact on patient care.

Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly malignant neoplasms posing diagnostic challenge due to a lack of defining molecular markers. CNS neuroblastoma with forkhead box R2 (FOXR2) activation (CNS_NBL) emerged as a distinct pediatric brain tumor entity from a pool previously diagnosed as primitive neuroectodermal tumors of the central nervous system (CNS-PNETs). Current standard of identifying CNS_NBL relies on molecular analysis. We set out to establish immunohistochemical markers allowing safely distinguishing CNS_NBL from morphological mimics. To this aim we analyzed a series of 84 brain tumors institutionally diagnosed as CNS-PNET. As expected, epigenetic analysis revealed different methylation groups corresponding to the (1) CNS-NBL (24%), (2) glioblastoma IDH wild-type subclass H3.3 G34 (26%), (3) glioblastoma IDH wild-type subclass MYCN (21%) and (4) ependymoma with RELA_C11orf95 fusion (29%) entities. Transcriptome analysis of this series revealed a set of differentially expressed genes distinguishing CNS_NBL from its mimics. Based on RNA-sequencing data we established SOX10 and ANKRD55 expression as genes discriminating CNS_NBL from other tumors exhibiting CNS-PNET. Immunohistochemical detection of combined expression of SOX10 and ANKRD55 clearly identifies CNS_NBL discriminating them to other hemispheric CNS neoplasms harboring “PNET-like” microscopic appearance. Owing the rarity of CNS_NBL, a confirmation of the elaborated diagnostic IHC algorithm will be necessary in prospective patient series.

Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios. Electronic supplementary material The online version of this article (10.1007/s00401-018-1879-y) contains supplementary material, which is available to authorized users.