Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Sci Adv. 2020 Jul 24;6(30):eaba3064. doi: 10.1126/sciadv.aba3064. eCollection 2020 Jul.
Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here, we developed a computational framework to identify putative causal noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as putative causal mutations in five major pediatric cancers. We experimentally validated the oncogenic role of overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation profiles can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.
解析癌症基因组中非编码突变的功能仍然是一个主要挑战。在这里，我们开发了一个计算框架，通过对突变和基因表达数据的联合分析，来识别所有类型的潜在因果非编码突变。我们在五种主要儿科癌症中鉴定出数千个 SNVs/小插入缺失和结构变异作为潜在的因果突变。我们通过增强子劫持实验验证了在 B-ALL 中过表达的致癌作用。我们观察到影响相同基因和途径的编码和非编码突变通常是排他的。我们表明，整合的突变谱可以帮助定义具有不同临床结果的新的患者亚群。我们的研究提出了一种系统识别和表征癌症中非编码突变全谱的一般策略。
