Wilkerson Matthew D, Cabanski Christopher R, Sun Wei, Hoadley Katherine A, Walter Vonn, Mose Lisle E, Troester Melissa A, Hammerman Peter S, Parker Joel S, Perou Charles M, Hayes D Neil
Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA The Genome Institute at Washington University, St. Louis, MO 63108, USA.
Nucleic Acids Res. 2014 Jul;42(13):e107. doi: 10.1093/nar/gku489. Epub 2014 Jun 26.
Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.
识别体细胞突变对于癌症基因组特征分析和确定患者治疗优先级至关重要。DNA全外显子测序（DNA-WES）是目前最常用的技术；然而，在低纯度肿瘤中其灵敏度较低。RNA测序（RNA-seq）能够覆盖表达的外显子，深度与表达水平成正比。我们推测，与单独使用DNA-WES相比，整合DNA-WES和RNA-seq能够实现更优的突变检测。我们开发了一种首创的方法，称为UNCeqR，通过整合患者匹配的RNA-seq和DNA-WES来检测体细胞突变。在模拟中，整合的DNA和RNA模型优于仅使用DNA-WES的模型。通过患者匹配的全基因组测序进行验证，结果表明整合模型比仅使用DNA-WES的模型具有更优的性能，包括一种已发表的方法和已发表的突变谱。对乳腺癌和肺癌队列（n = 871）进行全基因组突变分析，揭示了显著的肿瘤基因组学特性。通过整合RNA-seq和DNA-WES，低纯度肿瘤在突变检测方面的提升最大。在表达的突变中，RNA比DNA提供了更强的突变信号。与该队列早期的研究相比，UNCeqR提高了驱动基因和治疗靶点基因（如PIK3CA、ERBB2和FGFR2）的突变率。总之，将RNA-seq与DNA-WES整合可提高突变检测性能，尤其是对于低纯度肿瘤。
