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Open AccessReview

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

1
Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN 55902, USA
2
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: William Chi-shing Cho
Int. J. Mol. Sci. 2017, 18(1), 37; https://doi.org/10.3390/ijms18010037
Received: 18 October 2016 / Revised: 14 December 2016 / Accepted: 17 December 2016 / Published: 27 December 2016
(This article belongs to the Special Issue Big Data for Oncology)
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. View Full-Text
Keywords: neuroblastoma; big data; computational modeling; drug repositioning; networks; spontaneous regression; metastasis neuroblastoma; big data; computational modeling; drug repositioning; networks; spontaneous regression; metastasis
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MDPI and ACS Style

Salazar, B.M.; Balczewski, E.A.; Ung, C.Y.; Zhu, S. Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology. Int. J. Mol. Sci. 2017, 18, 37. https://doi.org/10.3390/ijms18010037

AMA Style

Salazar BM, Balczewski EA, Ung CY, Zhu S. Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology. International Journal of Molecular Sciences. 2017; 18(1):37. https://doi.org/10.3390/ijms18010037

Chicago/Turabian Style

Salazar, Brittany M.; Balczewski, Emily A.; Ung, Choong Y.; Zhu, Shizhen. 2017. "Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology" Int. J. Mol. Sci. 18, no. 1: 37. https://doi.org/10.3390/ijms18010037

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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