ABSTRACT
Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.
Subject(s)
Chromosome Mapping/methods , Gene Expression Regulation, Neoplastic , Genome, Human , Mutation , Neoplasm Proteins/genetics , Neoplasms/genetics , Adult , CRISPR-Associated Protein 9/genetics , CRISPR-Associated Protein 9/metabolism , CRISPR-Cas Systems , Cell Line, Tumor , Child , Clustered Regularly Interspaced Short Palindromic Repeats , Gene Editing , Gene Expression Profiling , Genetic Predisposition to Disease , Humans , Neoplasm Proteins/classification , Neoplasm Proteins/metabolism , Neoplasms/metabolism , Neoplasms/pathology , RNA, Guide, Kinetoplastida/genetics , RNA, Guide, Kinetoplastida/metabolismABSTRACT
Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.
