Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

摘要: Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how initiate, progress, and metastasize early childhood. Also, due limited detected driver mutations, it is difficult benchmark key genes for drug development. In this review, we use neuroblastoma, pediatric solid tumor of neural crest origin, as paradigm exploring “big data” applications oncology. Computational strategies derived from big data science–network- machine learning-based modeling repositioning—hold the promise shedding new light on molecular mechanisms driving neuroblastoma pathogenesis identifying potential therapeutics combat devastating disease. These integrate robust input, genomic transcriptomic studies, clinical data, vivo vitro experimental models specific other types that closely mimic its biological characteristics. We discuss contexts which computational approaches, especially network-based modeling, may advance research, describe currently available resources, propose future strategic collection analyses related diseases.