In:
Neuro-Oncology Advances, Oxford University Press (OUP), Vol. 5, No. Supplement_3 ( 2023-08-04), p. iii30-iii30
Abstract:
Paediatric solid tumours are the leading cause of cancer related death amongst children. Identification of paediatric-specific targeted therapies necessitates the use of paediatric cancer models that faithfully recapitulate the patient’s disease. In adult cancers, comprehensive cell line repositories and data atlases have enabled both hypothesis-driven research and scalable screens for new therapies. The generation and characterisation of paediatric cancer cell lines has significantly lagged behind that of their adult counterparts, underscoring the urgent need to develop a paediatric-focussed cell line resource. Herein, we establish a single-site collection of 261 cell lines, including 224 paediatric cancer cell lines representing 18 distinct extracranial and brain childhood tumour types. We subjected 182 paediatric cancer cell lines to multi-omic analyses across three dimensions (DNA-sequencing, RNA-sequencing, DNA methylation) to classify them based on clinically relevant molecular subtypes. In parallel, pharmacological and genetic CRISPR-Cas9 loss of function screens were performed to identify paediatric-specific drug sensitivities and genetic dependencies. Machine-learning approaches were employed to delineate predictive features of therapeutic vulnerabilities in different subtypes of paediatric cancers. By integrating molecular features with functional genomic and pharmacological profiles, we demonstrate how therapeutic target-biomarkers pairs may be rapidly prioritised and advanced. Lastly, we provide cell line data and resources in an open access portal (vicpcc.org.au/dashboard) to support drug development efforts, clinical trial design, and personalised medicine approaches for paediatric cancers of greatest unmet medical need.
Type of Medium:
Online Resource
ISSN:
2632-2498
DOI:
10.1093/noajnl/vdad070.119
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2023
detail.hit.zdb_id:
3009682-0
