Abstract

The identification and validation of prognostic and diagnostic biomarkers is a key element of The SIOP Ependymoma II trial, realised through the Biomarkers of Ependymoma in Children and Adolescents study (BIOMECA). BIOMECA aims to identify and validate biomarkers for prediction of outcome whilst enhancing stratification for the next generation of ependymoma trials. We outline our findings from the first 147 consecutive BIOMECA cases (posterior fossa, PF=111; supratentorial, ST=32; spinal, SP=4). We compared various methods for biomarker assessment, across six European laboratories to determine key analysis methods. Methods included: methylation-based classification (EPIC 850K DNA methylation array) (n=141); immunohistochemistry (IHC) for nuclear p65-RELA (n=32), H3K27me3 (n=115), and Tenascin-C (TNC) (n=147); copy number (CN) analysis by FISH, MLPA (1q, CDKN2A) (n=147), and MIP (molecular inversion probe) and DNA methylation array (1q, CDKN2A, 6q, 11q, 13q, 22q) (n=141); analysis of ZFTA- and YAP1-fusions by RT-PCR, sequencing, Nanostring assays and break-apart FISH (n=32). Using DNA methylation-based classification, 91% (n=101/111) of PF cases classified as PF ependymoma group A (PFA) and 69% (n=22/32) of ST cases as ST ependymoma, ZFTA fusion-positive (ZFTA). Most PFAs demonstrated inter-centre agreement for loss of H3K27me3, and were TNC positive, representing surrogate markers for PFA identification. Combinations of p65-RELA IHC, FISH analysis, and RNA-based methods were suitable to identify ZFTA- and YAP1- fused ST ependymomas. Predictive CN alterations were identified by high-resolution, quantitative MIP technology.The integration of histopathology assessment and molecular typing is now critical as the updated 2021 WHO CNS5 classification of ependymomas lists seven molecularly distinct entities. This study highlights the importance of evaluating different methods in a prospective trial cohort. Here, advanced molecular techniques represent powerful tools for the classification of ependymoma entities (DNA methylation array) and for the detection of CN alterations (MIP) and specific fusions, enabling the correct classification and identification of prognostic markers.

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