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Cancer subtype identification using somatic mutation data

机译：利用体细胞突变数据鉴定癌症亚型

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Background 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. Methods 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. Results 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. Conclusions This study is an important step toward understanding mutational patterns in cancer.
机译：背景技术随着下一代测序技术的出现，我们在鉴定癌症复发突变驱动因素方面取得了长足的进步。由于现在经常筛查癌症组织中的特定突变集，因此已经有大量样品可用于分析。对具有相似突变特征的患者进行分类可能有助于确定可能受益于特定治疗类型的患者亚组。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。方法在这里，我们描述了一种使用生物学途径去分离体细胞突变数据的新方法。我们将该方法应用于《癌症基因组图谱》中的23种癌症类型，包括来自5805种原发肿瘤的样本。结果我们显示，对于大多数癌症类型，脱简简的突变数据与表型数据相关。我们在三种癌症类型中识别出不良的预后亚型，这些亚型与信号转导途径的突变相关，可针对性地选择治疗方法。我们确定了14种其他亚型的亚型-药物关联。最后，我们进行了全癌基因分型分析，确定了9种全癌基因亚型，这些亚型与四个生物学途径的总体突变相关。结论这项研究是了解癌症突变模式的重要一步。

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