期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 18, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/ijms18010037
关键词
neuroblastoma; big data; computational modeling; drug repositioning; networks; spontaneous regression; metastasis
资金
- National Cancer Institute [K99/R00CA178189]
- Alex's Lemonade Stand Foundation
- CureSearch for Children's Cancer Foundation
- V Foundation for Cancer Research
- Fraternal Order of Eagles
- Mayo Center for Biomedical Discovery
- Mayo Clinic Cancer Centerand Center for Individualized Medicine
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring big data applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which big data and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.
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