title: Comprehensive study of tumour single nucleotide polymorphism array data reveals significant driver aberrations and disrupted signalling pathways in human hepatocellular cancer.
abstract: The authors describe an integrated method for analysing cancer driver aberrations and disrupted pathways by using tumour single nucleotide polymorphism (SNP) arrays. The authors new method adopts a novel statistical model to explicitly quantify the SNP signals, and therefore infers the genomic aberrations, including copy number alteration and loss of heterozygosity. Examination on the dilution series dataset shows that this method can correctly identify the genomic aberrations even with the existence of severe normal cell contamination in tumour sample. Furthermore, with the results of the aberration identification obtained from multiple tumour samples, a permutation-based approach is proposed for identifying the statistically significant driver aberrations, which are further incorporated with the known signalling pathways for pathway enrichment analysis. By applying the approach to 286 hepatocellular tumour samples, they successfully uncover numerous driver aberration regions across the cancer genome, for example, chromosomes 4p and 5q, which harbour many known hepatocellular cancer related genes such as alpha-fetoprotein (AFP) and ectodermal-neural cortex (ENC1). In addition, they identify nine disrupted pathways that are highly enriched by the driver aberrations, including the systemic lupus erythematosus pathway, the vascular endothelial growth factor (VEGF) signalling pathway and so on. These results support the feasibility and the utility of the proposed method on the characterisation of the cancer genome and the downstream analysis of the driver aberrations and the disrupted signalling pathways.
authors.affiliation: School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, People's Republic of China.
authors.forename: Yuanning
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH230027, People's Republic of China.
authors.forename: Minghui
authors.initials: M
authors.lastname: Wang
authors.affiliation: School of Information Science and Technology, University of Science and Technology of China, Hefei AH230027, People's Republic of China.
authors.forename: Huanqing
authors.initials: H
authors.lastname: Feng
authors.affiliation: Centers for Biomedical Engineering, University of Science and Technology of China, Hefei AH230027, People's Republic of China. aoli@ustc.edu.cn.
authors.forename: Ao
authors.initials: A
authors.lastname: Li
mesh_terms: D000465:Algorithms; D000483:Alleles; D006528:Carcinoma, Hepatocellular / Q000235:genetics*; D002869:Chromosome Aberrations; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D015972:Gene Expression Regulation, Neoplastic; D005838:Genotype; D006801:Humans; D008113:Liver Neoplasms / Q000235:genetics*; D019656:Loss of Heterozygosity; D008390:Markov Chains; D009369:Neoplasms / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA; D015398:Signal Transduction