MODL-17. The Childhood Brain Cancer Cell Line Atlas: A Resource for Biomarker Identification and Therapeutic Development. (3rd June 2022)

• Record Type: Journal Article
• Authors: Daniel, Paul; Sun, Claire; Koptyra, Mateusz; Drinkwater, Caroline; Chew, Nicole; Bradshaw, Gabrielle; Loi, Melissa; Shi, Claire; Tourchi, Motahhareh; Parackal, Sarah; Chong, Wai Chin; Fernando, Dasun; Adjumain, Shazia; Nguyen, Hoang; Habarakada, Dilru; Sooraj, Dhanya; Crombie, Duncan; Zhukova, Nataliya; Jones, Chris; Rubens, Jeffrey; Raabe, Eric; Vinci, Maria; Dun, Matt; Ludlow, Louise; Nazarian, Javad; Fletcher, Jamie; Ekert, Paul; Ziegler, David; Loh, Amos Hong Pheng; Low, Sharon Yin Yee; Monje, Michelle; Neeman, Naama; Williams, Bryan; Resnick, Adam; Gough, Daniel; Cain, Jason; Firestein, Ron;
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 1
• Page Start: i172
• Page End: i172
• Publication Date: 2022-06-03
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac079.640
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: 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.