Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology.
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DOI：
10.3390/ijms18010037
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发表时间：
2016-12-27
影响因子：
5.6
通讯作者：
Zhu S
中科院分区：
文献类型：
--
作者：
Salazar BM;Balczewski EA;Ung CY;Zhu S
与大多数成人癌症相比，儿童癌症很少表现出复发的突变事件。这对理解癌症是如何在儿童早期开始、发展和转移的提出了挑战。此外，由于检测到的驱动程序突变有限，很难对药物开发的关键基因进行基准测试。在这篇综述中，我们使用神经母细胞瘤，一种起源于神经脊的儿童实体肿瘤，作为探索大数据在儿童肿瘤学中的应用的范例。源自大数据科学的计算策略-基于网络和机器学习的建模和药物重新定位-有望为推动神经母细胞瘤发病的分子机制提供新的线索，并确定对抗这种毁灭性疾病的潜在疗法。这些策略整合了强大的数据输入，来自基因组和转录研究、临床数据以及针对神经母细胞瘤和其他类型癌症的体内和体外实验模型，这些模型密切模拟其生物学特征。我们讨论了大数据和计算方法，特别是基于网络的建模，可以促进神经母细胞瘤研究的背景，描述了当前可用的数据和资源，并提出了神经母细胞瘤和其他相关疾病的战略性数据收集和分析的未来模型。
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.