Next Article in Journal
Involvement of Host Non-Coding RNAs in the Pathogenesis of the Influenza Virus
Previous Article in Journal
Tumor LINE-1 Methylation Level in Association with Survival of Patients with Stage II Colon Cancer
Previous Article in Special Issue
Cofilin-1 and Other ADF/Cofilin Superfamily Members in Human Malignant Cells
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessReview
Int. J. Mol. Sci. 2017, 18(1), 37; doi:10.3390/ijms18010037

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
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: William Chi-shing Cho
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)
View Full-Text   |   Download PDF [1433 KB, uploaded 27 December 2016]   |  

Abstract

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
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top