Zhang Bing, Wang Jing, Wang Xiaojing, Zhu Jing, Liu Qi, Shi Zhiao, Chambers Matthew C, Zimmerman Lisa J, Shaddox Kent F, Kim Sangtae, Davies Sherri R, Wang Sean, Wang Pei, Kinsinger Christopher R, Rivers Robert C, Rodriguez Henry, Townsend R Reid, Ellis Matthew J C, Carr Steven A, Tabb David L, Coffey Robert J, Slebos Robbert J C, Liebler Daniel C
1] Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA [2] Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Nature. 2014 Sep 18;513(7518):382-7. doi: 10.1038/nature13438. Epub 2014 Jul 20.
Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
对人类癌症进行广泛的基因组特征分析带来了从基因组异常推断癌症表型的问题。为了解决这个问题,我们分析了先前由癌症基因组图谱(TCGA)表征的结肠和直肠肿瘤的蛋白质组,并进行了综合蛋白质基因组分析。与种系变异相比,体细胞变异显示出蛋白质丰度降低。信使RNA转录本丰度不能可靠地预测肿瘤之间的蛋白质丰度差异。蛋白质组学在TCGA队列中确定了五种蛋白质组亚型,其中两种与TCGA的“微卫星不稳定性/CpG岛甲基化表型”转录组亚型重叠,但具有与不同临床结果相关的独特突变、甲基化和蛋白质表达模式。虽然拷贝数改变对mRNA丰度显示出强烈的顺式和反式效应,但其中相对较少的效应会延伸到蛋白质水平。因此,蛋白质组学数据能够对候选驱动基因进行优先级排序。20号染色体q臂扩增子在mRNA和蛋白质水平上都与最大的全局变化相关;蛋白质组学数据突出了潜在的20q候选基因,包括HNF4A(肝细胞核因子4α)、TOMM34(外线粒体膜转位酶34)和SRC(SRC原癌基因,非受体酪氨酸激酶)。综合蛋白质基因组分析为解释基因组异常提供了功能背景,并为理解癌症生物学提供了新的范例。