Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

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.

Multi-omics integration for neuroblastoma clinical endpoint prediction

“…The introduction of genome wide assays able to probe in great detail multiple genomics aspects often at affordable prices brought the promise of novel biomarker identification for clinical outcome prediction, notably in combination with effective data analysis [ 5 , 6 ]. Machine learning approaches have been adopted for the predictive classification of patient outcome in neuroblastoma, also through integration of data from multiple assays [ 5 , 7 ]. For example, in a previous effort, the MicroArray/Sequencing Quality Control (MAQC/SEQC) initiative extensively explored expression-based predictive models for neuroblastoma risk assessment [ 8 ].…”

“…For example, in a previous effort, the MicroArray/Sequencing Quality Control (MAQC/SEQC) initiative extensively explored expression-based predictive models for neuroblastoma risk assessment [ 8 ]. However, comprehensive integrative approaches effective across multiple clinical outcomes are still limited [ 5 ].…”

A five-year review of children with neuroblastoma at Dr. Sardjito General Hospital, Yogyakarta, Indonesia

“…Metastases are mostly found in long bones and skull, bone marrow, liver, lymph nodes, and skin. 11,25 Pulmonary and intracranial metastases are rarely seen, although there is often hematogenous spreading. 8 We found pulmonary metastases in 2 (5%) cases and both of them died.…”

Emerging Role of the Macrophage Migration Inhibitory Factor Family of Cytokines in Neuroblastoma. Pathogenic Effectors and Novel Therapeutic Targets?

“…According to the International NB Staging Series (INSS), which relies on surgical observations, NB is classified by risk level, tumor location and dissemination, and MYCN (proto-oncogene protein) amplification [5]. The International NB Risk Group (INRG) Staging System was more recently designed in order to find homogeneous pretreatment risk groups, considering clinical criteria and tumor imaging [6].…”