ID #828 CCMA-EPIC an epigenetic driven framework for biomarker discovery in paediatric brain tumours

Abstract

Paediatric central nervous system (CNS) cancers represent a leading cause of cancer related mortality in children and are driven by distinct developmental and epigenetic mechanisms. However, systematic epigenetic characterisation across paediatric CNS cancer models remains limited. The Childhood Cancer Model Atlas (CCMA) was established as a comprehensive and globally accessible resource for paediatric cancer research, with a specific and strategic focus on CNS tumours. CCMA contains the largest and most diverse single-site collection of paediatric CNS tumour cell line models (n > 250), including high grade glioma, atypical teratoid rhabdoid tumour, ependymoma, medulloblastoma, and several rare CNS cancer types, alongside extensive molecular profiling, functional genomics, and drug response data.

To deepen biological insight into these models, we initiated the development of CCMA-EPIC, a new epigenetic framework to characterise chromatin landscapes across CCMA cell lines. We employed Cut&Run, using six key histone modification markers that capture active promoters, enhancers, transcriptionally active regions, heterochromatin, and quiescent chromatin, to define chromatin states in four paediatric CNS tumour types and subtypes including ATRT (n = 6), H3K27M (n = 10), H3G34-altered (n = 8) and H3 wild-type high grade gliomas (n = 10). Integration of these markers enables systematic annotation of model specific chromatin regulatory states, revealing pronounced epigenetic heterogeneity across CNS tumour entities and highlighting lineage specific regulatory programs that are not apparent from genomic features alone.

We further evaluated the functional relevance of CCMA-EPIC by integrating epigenetic features with machine learning models to predict CRISPR gene dependency and drug response profiles. Preliminary analyses show that inclusion of chromatin state features substantially improves prediction accuracy compared to models based solely on genomic and transcriptomic data. Collectively, our work reveals the critical role of the epigenome in defining genetic dependencies and drug sensitivities in paediatric CNS tumours and sets the stage for uncovering epigenetic biomarkers that may inform future precision medicine clinical trials.