Generation and multi-dimensional profiling of a childhood cancer cell line atlas defines new therapeutic opportunities

Pediatric solid and central nervous system tumors are the leading cause of cancer-related death among children. Identifying new targeted therapies necessitates the use of pediatric cancer models that faithfully recapitulate the patient's disease. However, the generation and characterization of pediatric cancer models has significantly lagged behind adult cancers, underscoring the urgent need to develop pediatric-focused cell line resources. Herein, we establish a single-site collection of 261 cell lines, including 224 pediatric cell lines representing 18 distinct extracranial and brain childhood tumor types. We subjected 182 cell lines to multi-omics analyses (DNA sequencing, RNA sequencing, DNA methylation), and in parallel performed pharmacological and genetic CRISPR-Cas9 loss-of-function screens to identify pediatric-specific treatment opportunities and biomarkers. Our work provides insight into specific pathway vulnerabilities in molecularly defined pediatric tumor classes and uncovers biomarker-linked therapeutic opportunities of clinical relevance. Cell line data and resources are provided in an open access portal.

MODL-17. The Childhood Brain Cancer Cell Line Atlas: A Resource for Biomarker Identification and Therapeutic Development

Cell lines represent the most versatile and widely used models of cancer and, as such, are critical for identifying and advancing new therapies. Strikingly, there is a significant gap in both the number of childhood brain cancer cell lines and their characterisation compared to their adult counterparts. To address this inequity, we established a childhood brain cancer cell line atlas (publicly available at vicpcc.org.au/dashboard) encompassing over 180 childhood CNS-derived cell lines, representing 20 tumour types and 11 molecular subtypes. Cell lines are characterized by whole genome, RNA-sequencing, phospho- and total proteomics, DNA methylation and ATAC-seq analyses. Multi-omic factor analysis revealed distinct lineage-specified classification of our cell line cohort. In parallel, high throughput drug and CRISPR/Cas9 screens were conducted to map the functional dependencies in over 70 childhood CNS cell lines, including 47 paediatric high grade glioma models. These screens identified both lineage and molecular-subtype specific genetic and drug dependencies, underscoring the utility of this wide-scale approach. Machine based learning approaches to predict genotype-phenotype correlations uncovered distinct paediatric-specific biomarkers of growth dependency, highlighting the unique genetic wiring underlying paediatric CNS tumours. Finally, by integrating functional, molecular and drug profiles of paediatric CNS cell lines, we construct a system to prioritize investigation of novel therapeutic target-biomarkers pairs in specific CNS tumour types.