Cancer subtype identification using somatic mutation data.
Cancer subtype identification using somatic mutation data.
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DOI：
10.1038/s41416-018-0109-7
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发表时间：
2018-05
影响因子：
8.8
通讯作者：
Quackenbush J
中科院分区：
文献类型：
--
作者：
Kuijjer ML;Paulson JN;Salzman P;Ding W;Quackenbush J
随着下一代测序技术的出现，我们在识别癌症复发突变驱动因素方面取得了巨大进展。由于现在经常对癌症组织进行特定突变组的筛查，因此大量样本可用于分析。对具有相似突变谱的患者进行分类可能有助于识别可能受益于特定类型治疗的患者亚组。然而，由于数据的稀疏性和异质性，基于体细胞突变的分类具有挑战性。在这里，我们描述了一种利用生物途径去稀疏体细胞突变数据的新方法。我们将此方法应用于癌症基因组图谱中的 23 种癌症类型，包括来自 5805 个原发肿瘤的样本。我们表明，对于大多数癌症类型，去稀疏突变数据与表型数据相关。我们在三种癌症类型中发现了预后不良的亚型，这些亚型与信号转导途径的突变有关，而针对这些突变有针对性的治疗方案。我们确定了另外 14 种亚型的亚型-药物关联。最后，我们进行了泛癌亚型分析，并确定了九种泛癌亚型，它们与四组总体生物途径的突变相关。这项研究是了解癌症突变模式的重要一步。
With the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data. Here we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours. We show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype–drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways. This study is an important step toward understanding mutational patterns in cancer.
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影响因子：
20.3
作者：
Kuehnl, Andrea;Valk, Peter J. M.;Grimwade, David
通讯作者：
Grimwade, David
影响因子：
48
作者：
Gonzalez-Perez A;Perez-Llamas C;Deu-Pons J;Tamborero D;Schroeder MP;Jene-Sanz A;Santos A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
15.9
作者：
Huang, Yujie;Rajappa, Prajwal;Greenfield, Jeffrey
通讯作者：
Greenfield, Jeffrey
影响因子：
3.7
作者：
Samur MK
通讯作者：
Samur MK
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y