Introduction

The integrated use of whole-genome analysis technologies has revealed the site-specific molecular heterogeneity of ependymomas. The majority of supratentorial tumours contain oncogenic fusions between the genes C11orf95 and RELA (C11orf95–RELA) or YAP1 (YAP1-MAMLD1) [1, 2]. Notably, the gene C11orf95 is now called ZFTA (Zinc Finger Translocation Associated), and this name is used hereafter. In contrast, posterior fossa (PF) tumours lack recurrent somatic mutations and are most likely driven by epigenetic events. These tumours are classified according to gene expression or methylation profiling into two broad groups: group A (PFA) and group B (PFB) [2-4]. Tumour molecular characterisation is clinically relevant, as tumours with ZFTA–RELA translocation and PFA ependymomas are reportedly associated with poor prognosis [1, 3]. The diagnostic category of RELA fusion-positive ependymoma was therefore introduced in the most recent 4th edition of the WHO Classification of Tumours of the Central Nervous System [5].

Clinically relevant molecular subtypes of ependymoma have been identified using a variety of methods, including gene expression microarrays [3, 6], methylation profiling [3, 4, 7], reverse transcription PCR for ZFTA-RELA fusion mRNA [1], and immunohistochemistry [3]. We have applied a novel and potentially diagnostic approach for the identification of four molecular groups of ependymoma, based on transcription profiling of marker genes, using NanoString nCounter Technology (NanoString Technologies, Seattle, WA, USA). This method enables the analysis of degraded RNA, and is thus compatible with formalin-fixed paraffin-embedded (FFPE) tumour samples. It has previously been successfully tested for the identification of molecular subtypes in medulloblastoma, and the diagnosis of rare paediatric brain tumours [8, 9].

In the present study, we analysed 16 supratentorial and 50 PF tumours, and identified four molecular types of ependymoma. Analysis of the clinical characteristics of these patients confirmed previous findings for PFA and PFB tumours. However, the majority of patients in our series with ZFTA-RELA fusion exhibited a good prognosis, indicating a need for further clinical investigation of this molecular group.

Materials and methods Patients and tumour material

This analysis included paediatric patients diagnosed with ependymomas, CNS embryonal tumours ‘not otherwise specified’ (NOS), and CNS primitive neuroectodermal tumours (CNS-PNETs) category, as diagnosed before the WHO 2016 classification was introduced, at The Children's Memorial Health Institute in Warsaw, Poland, between 1996 and 2019. Analysis was performed using archived FFPE tumour material obtained at diagnosis. Two experienced neuropathologists and one paediatric pathologist performed histopathological evaluation of haematoxylin and eosin-stained slides to verify the original diagnosis of ependymoma and to determine tumour tissue content. Whole preparations were scanned using a Hamamatsu NanoZoomer 2.0 RS scanner (Hamamatsu Photonics, Hamamatsu, Japan) at an original magnification of ×40. Only samples that contained >70% of tumour cells were investigated.

After verification, our subsequent analysis included 16 supratentorial ependymomas, 50 PF ependymomas, and 7 CNS-PNETs or CNS embryonal tumours NOS. The supratentorial ependymomas included two reference tumours – one that was ZFTA-RELA fusion-positive and another that was ZFTA–RELA fusion-negative, as detected by next-generation sequencing (NGS) prior to NanoString analysis.

Tumours were retrospectively analysed and tissues were retrieved from the archives of the Pathomorphology Department of the Children's Memorial Health Institute's, Warsaw, Poland, under the agreement from The Bioethics Committee at the Children's Memorial Health Institute.

Identification of group-specific marker genes by microarray in silico analysis

We re-analysed a total of 345 CEL files deposited in the Gene Expression Omnibus (GEO) database. These included only paediatric cases (patients <18 years old) that were investigated using the Affymetrix Human Genome U133 Plus 2.0 platform (Affymetrix, Santa Clara, CA, USA).

We compared the 36 RELA fusion-positive (RELA+) and 5 YAP1 fusion-positive (YAP1+) ependymomas from the GSE64415 set [2] to other supratentorial tumours. These included 12 atypical teratoid rhabdoid tumours (ATRTs) from the GSE73038 [10] and GSE70678 [11] sets, as well as the following tumours from the GSE73038 set: 29 high-grade gliomas (HGGs), 6 embryonal tumours with multilayered rosettes (ETMRs), 9 CNS neuroblastomas with FOXR2 activation (CNS NB-FOXR2), 5 CNS Ewing sarcoma family tumours with CIC alteration (CNS EFT-CIC), 6 CNS high-grade neuroepithelial tumours with MN1 alteration (CNS HGNET-MN1), and 8 CNS high-grade neuroepithelial tumours with BCOR alteration (CNS HGNET-BCOR).

We compared 62 PFA and 7 PFB ependymomas from the GSE64415 set with 99 medulloblastomas from the GSE73038 and GSE10327 [12] sets, 21 PF ATRTs from the GSE73038 and GSE70678 sets, and 40 other infrequently found tumours in the PF from the GSE73038 set – namely, K27 HGGs, ETMRs, CNS HGNET-BCORs, and CNS HGNET-MN1 tumours.

CEL files were uploaded to the R environment and subsequently normalised with the MAS5 method using the ‘affy’ library. Next, quantile normalisation was performed separately on supratentorial and infratentorial tumours. Data were log2 transformed, and affy probes with the lowest variance (variance < 0.25) were filtered out. In a supervised approach, affy probes were selected using Student's t-test. A set of samples with ependymoma molecular subtype was compared 100 times to an equal-sized set drawn from patient samples with different diagnoses. The mean P value and mean fold-change from 100 t-tests were used as measures of a good marker candidate. We performed random selection of samples and repeated the t-test 100 times to assure the robustness of marker selection.

Detection of molecular groups at the RNA level

Total RNA was extracted from FFPE tumour samples using RNeasy kits (Qiagen, Hilden, Germany). For the identification of four molecular subgroups (RELA+, YAP1+, PFA, and PFB), we applied NanoString nCounter system analysis (NanoString Technologies) in a series of 66 ependymomas and 7 PNET or CNS embryonal tumours NOS. For group assignment, we applied a custom NanoString CodeSet that included marker genes and three housekeeping genes (ACTB, GAPDH, and TBP).

Probes were designed to target the regions of the marker genes (the sequences are presented in supplementary material, Table S1). Hybridisation of these probes to the tumour RNA samples was performed in the Clinical Research Centre, Medical University of Białystok, Poland, following NanoString Technologies procedures for hybridisation, detection, and scanning. The raw counts for each gene were subjected to technical and biological normalisation using nSolver 4.0 software (NanoString Technologies). Clustering of the samples was performed using Euclidean distance metrics and average settings.

Detection of RELA and YAP1 fusions via targeted NGS

RELA and YAP1 gene fusions were detected using targeted cancer panel sequencing – the Archer FusionPlex Solid Tumour Panel (Archer Dx, Boulder, CO, USA) and/or the Ampliseq Childhood Cancer Panel for the Illumina assay (Illumina, San Diego, CA, USA). Prior to library preparation, total RNA was extracted from FFPE tumour samples using the RNeasy Mini Kit (Qiagen), and then quantified with the QuantiFluor RNA system (Promega, Madison, WI, USA). Post-extraction analyses included additional quality control metrics to establish cut-offs for RNA concentration and quality. RNA purity was evaluated using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) for optical density (OD) 260/280 ratios. The percentage of RNA fragments >200 nucleotides (DV200) was calculated using an Agilent 2100 Bioanalyzer. Samples with an OD 260/280 ratio of >1.6 and with DV200 scores of >30% were considered acceptable for downstream processing. We used approximately 20 ng RNA for library construction, according to the manufacturer's protocols. Each library was assessed qualitatively using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and quantified using the QuantiFluor RNA system. Then, the obtained library was amplified using universal primers targeting the paired-end adapters. Clusters were generated and sequenced on the MiniSeq instrument (Illumina, San Diego, CA, USA) using the MiniSeq High-Output Kit (2 × 150 cycles). FASTQ files with base call and quality information of a minimum of 4.5 million paired-end sequence reads were processed using Archer Analysis Software (Suite_Analysis_v6.2.7) and/or the BaseSpace RNA Amplicon workflow (Illumina) to determine the presence of RELA and YAP1 fusion genes.

Statistical analysis

Statistical analyses were performed using the t-test and Fisher's exact test. Overall survival (OS) and progression-free survival (PFS) were calculated using Kaplan–Meier estimates, and group comparisons were made using the log-rank test. Analyses were performed using SPSS software (version 26; SPSS Inc., Chicago, IL, USA). All patients had a follow-up period of at least 2 years.

Results Identification of group-specific marker genes

Group-specific candidate marker genes were selected based on both fold-change and statistical significance. Table 1 presents the top up-regulated probes/genes for each category compared to all other types of tumour. This list includes probes/genes with at least a fivefold increase in expression level and statistical significance within the top 20 positions. For further analysis, in addition to the marker genes, 4–6 candidate genes were selected among the most significantly up-regulated probes for each group, excluding unknown and microRNA coding genes. We also investigated four genes for identification of the molecular subgroups PFA1 (SKAP2 and WIF1) and PFA2 (EN2 and CNPY1) [7].

Table 1. Marker genes selected for identification of molecular groups in ependymoma. Group Probe Gene Top fold Significance top position RELA+ 201783_s_at RELA Group marker 206844_at FBP2 716.4 13 223967_at ANGPTL6 132.0 1 221400_at MYO3A 49.5 10 241382_at PCP4L1 42.7 12 244364_at MYO3A 40.7 8 219517_at ELL3 26.1 7 228994_at CCDC24 20.9 9 219518_s_at ELL3 20.4 4 YAP1+ 231729_s_at CAPS 215.1 10 214652_at DRD1 113.6 4 209540_at IGF1 62.6 18 231728_at CAPS 43.8 3 209542_x_at IGF1 37.0 13 227848_at PEBP4 35.6 12 211577_s_at IGF1 27.0 15 1554097_a_at MIR31HG 17.6 1 236940_at NA 16.7 5 213085_s_at WWC1 13.8 8 1554044_a_at MRAP 12.5 2 PFA 205116_at LAMA2 Group marker 207695_s_at IGSF1 60.9 2 227848_at PEBP4 54.1 17 224339_s_at ANGPTL1 50.7 16 237058_x_at SLC6A13 38.9 5 239183_at ANGPTL1 36.7 20 231063_at NA 35.7 13 1555396_s_at CXorf67 26.8 1 244084_at AIFM3 20.6 7 203571_s_at ADIRF 19.3 12 1557286_at NA 18.7 11 226420_at MECOM 16.5 19 221884_at MECOM 16.1 8 205208_at ALDH1L1 10.0 3 PFB 203413_at NELL2 Group marker 232005_at DNAH1 45.2 15 1553133_at C9orf72 10.1 19 1569305_a_at NA 9.9 18 219644_at CEP83 7.9 7 225919_s_at C9orf72 6.2 17 1552816_at NXNL2 5.4 2 RELA+, RELA fusion positive; YAP1+, YAP1 fusion positive. NanoString probes hybridisation performance and final selection of marker genes

NanoString probes were produced for the selected candidate genes (see supplementary material, Table S1) and hybridised to ependymoma samples. The RELA+ and YAP1+ marker probes hybridised to supratentorial tumours, while the PFA, PFA1, PFA2, and PFB marker probes hybridised to PF ependymomas. One probe for the DRD1 gene exhibited uniform low hybridisation across all ependymoma samples, and a probe for ANGTPL6 showed high expression in both RELA fusion-positive and RELA fusion-negative reference samples. Therefore, the probes for DRD1 and ANGTPL6 were excluded from subsequent clustering analyses.

In summary, the following marker genes were used for subsequent analysis in supratentorial tumours: RELA, ELL3, FBP2, PCP4L1, and MYO3A for detection of RELA+ tumours; and MRAP, IGF1, CAPS, and WWC1 for detection of YAP1+ tumours. Likewise, the following marker genes were selected for analysis in PF tumours: LAMA2, ALDH1L1, SLC6A13, IGSF1, and CXorf67 for PFA; and NELL2, DNAH1, CEP83, C9orf72, and NXNL2 for PFB tumours.

Clustering of tumours according to the expression levels of marker genes Supratentorial ependymomas

Clustering analysis was performed using nine signature genes in 16 ependymomas. The results revealed two main clusters: cluster 1, which included nine tumours with high expression of RELA+ signature genes; and cluster 2, which contained six tumours without the expression of signature genes. Cluster 1 included the reference RELA fusion-positive sample, and cluster 2 included the RELA fusion-negative reference sample, as expected. One tumour showing expression of YAP1+ signature genes was separated from the remaining tumours. Tumours from the RELA+ cluster exhibited high RELA gene expression, with the exception of one sample that showed low RELA expression (Figure 1A).

Clustering of supratentorial tumours according to expression levels of marker genes using the NanoString method. (A) Clustering of 16 ependymomas using nine signature genes reveals nine tumours with a RELA fusion-positive signature, and one tumour with a YAP1 fusion-positive signature. RELA expression levels are presented below the clusters. (B) Clustering of the same cohort of 16 tumours using 15 signature genes reveals two tumours with presence of the CNS HGNET-MN1 signature. (C) Clustering of seven CNS-PNET or CNS embryonal tumours NOS reveals one tumour with a RELA fusion-positive signature. This tumour was diagnosed as CNS-PNET prior to introduction of the WHO 2016 classification. Red arrowheads indicate the reference ependymoma samples. Blue arrowheads indicate tumours without ZFTA-RELA fusion. Heatmap colours represent log2 gene expression differences. RELA+, RELA fusion-positive signature; YAP1+, YAP1 fusion-positive signature; NC, not classified; MN1, CNS HGNET-MN1 tumour.

Therefore, we designated nine samples from cluster 1 as RELA+, six samples from cluster 2 as not classified (NC), and one sample as YAP1+ ependymoma. Among the six NC samples, two tumours had been previously identified as CNS HGNET-MN1 by molecular analysis [9]. To confirm their distinctive molecular identity, we repeated the clustering analysis with an additional six probes representing the CNS HGNET-MN1 signature, which revealed clear separation of those two samples from the remaining tumours (Figure 1B). Therefore, NanoString analysis of histopathologically diagnosed ependymomas could molecularly classify 12 out of 16 tumours in our series, leaving four samples as NC ependymomas.

Supratentorial PNET or CNS embryonal tumours NOS

Ependymomas have previously been identified through gene methylation profiling among histologically diagnosed PNETs [10]. Therefore, we investigated whether NanoString-based expression profiling was also helpful for the detection of ependymomas and their molecular subtypes. The RELA+ and YAP1+ signature probes were used to analyse seven histopathologically diagnosed PNET or CNS embryonal tumours NOS that were not previously characterised at the molecular level. Clustering analysis, including one RELA+ and one YAP1+ ependymoma for comparison, revealed one PNET sample displaying clear expression of RELA+ signature genes (Figure 1C).

PF ependymomas

Clustering was performed on 50 ependymomas using 10 signature genes. We identified two major clusters based on the expression of five PFA and five PFB genes (Figure 2A). Cluster PFB, which comprised 7 tumours, was clearly separated from the 42 PFA tumours. One sample was classified as an outlier, probably not an ependymoma. Additional analyses were performed only within the PFA cluster, using two PFA1 and two PFA2 signature genes. Two distinct clusters were identified: PFA1 with 33 samples and PFA2 with 9 samples (Figure 2B).

Clustering of PF ependymomas using the NanoString method. (A) Clustering of 50 ependymomas reveals 7 tumours with a PFB signature, 43 tumours with a PFA signature, and one outlier. (B) Clustering of 42 PFA ependymomas reveals 33 tumours with PFA1 signatures and 9 tumours with PFA2 signatures. Heatmap colours represent log2 gene expression differences.

Subsequently, we clustered all samples using only two marker genes: LAMA2 and NELL2. This revealed three main clusters. The first showed seven tumours with expression of NELL2 but not LAMA2 (NELL2+/LAMA2−), which overlapped exactly with tumours from the PFB cluster. The second cluster included 23 tumours showing expression of LAMA2 but not NELL2 (NELL2−/LAMA2+). The third cluster included 18 tumours that showed expression of both NELL2 and LAMA2 (NELL2+/LAMA2+). The second and third clusters overlapped with the PFA tumours (Figure 3A).

Ependymoma tumours subdivided according to NELL2 and LAMA2 expression status. (A) Clustering of 49 ependymomas reveals three clusters: NELL+/LAMA−, NELL+/LAMA+, and NELL−/LAMA+ tumours. Heatmap colours represent log2 gene expression differences. (B) Kaplan–Meier curves according to NELL2 and LAMA2 expression status. P values were calculated using the log-rank test.

Detection of RELA and YAP1 fusion transcripts

NGS analysis was performed in all supratentorial tumours. In eight ependymomas and one PNET, which all showed the RELA+ NanoString signature, we detected the presence of the ZFTA-RELA fusion transcript. In one ependymoma that exhibited the RELA+ signature, but low RELA expression at the RNA level (Figure 1A), we did not detect the ZFTA-RELA fusion transcript. Among four ependymomas that were not classified by NanoString, and were analysed using the Archer FusionPlex Solid Tumour Panel, none exhibited the ZFTA-RELA fusion, but one tumour showed the presence of the ZFTA-MAML2 fusion. Therefore, in our series, the presence of the NanoString RELA+ signature was significantly associated with the ZFTA-RELA fusion (p = 0.005, Fisher's exact test). In the only sample showing the YAP1+ NanoString signature, we detected the YAP1-MAMLD1 fusion transcript. Table 2 presents the NGS results for individual patients.

Table 2. Characteristics of patients diagnosed with supratentorial tumours according to molecular group. ID Age (years) Sex NanoString diagnosis NGS fusion Original diagnosis/WHO stage Relapse months/location DOD (months) ADF (months) Primary treatment PPNG protocols RT Chemotherapy 1 12 M RELA+ ZFTA-RELA EPN III No No 168 Local EPN 2 12 F RELA+ ZFTA-RELA EPN III No No 168 Local EPN 3 5 M RELA+ ZFTA-RELA EPN III No No 120 Local EPN 4 3 M RELA+ ZFTA-RELA EPN III No No 60 Local EPN 5 6 M RELA+ ZFTA-RELA EPN III No No 77 Local EPN 6 2 F RELA+ ZFTA-RELA EPN III 134/distant No 168 No For children <3 years old 7 11 F RELA+ Not detected EPN III No No 72 Local EPN 8 12 M RELA+ ZFTA-RELA EPN III 46/local No 288 Local No but EPN on relapse 9 14 M RELA+ ZFTA-RELA EPN III No No 24 Local EPN 10 1 M RELA+ ZFTA-RELA PNET 22 42 – No MB/PNET 11 7 F YAP1+ YAP1-MAMLD1 EPN II No Yes – CSI EPN 12 1 M NC ZFTA-MAML2 EPN II No No 156 No For children <3 years old 13 17 F NC Not detected EPN III No No 168 Local EPN