Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.
| Autor: | Salazar BM; Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA. Salazar.Brittany@mayo.edu., Balczewski EA; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. Balczewski.Emily@mayo.edu., Ung CY; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. Ung.ChoongYong@mayo.edu., Zhu S; Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA. Zhu.Shizhen@mayo.edu.; Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. Zhu.Shizhen@mayo.edu. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | International journal of molecular sciences [Int J Mol Sci] 2016 Dec 27; Vol. 18 (1). Date of Electronic Publication: 2016 Dec 27. |
| DOI: | 10.3390/ijms18010037 |
| Abstrakt: | 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. Competing Interests: The authors declare no conflict of interest. |
| Databáze: | MEDLINE |
| Externí odkaz: |
|