基于体细胞突变谱识别癌症亚型

Sungchul Kim, Lee Sael, Hwanjo Yu
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引用次数: 10

摘要

肿瘤分层是肿瘤基因组学的基本任务之一,有助于更好地了解肿瘤的异质性和更好地进行靶向治疗。有各种各样的生物学数据可用于肿瘤分层,包括基因表达和测序数据。在这项工作中,我们使用体细胞突变数据。生成两种类型的体细胞突变谱,并使用k-means聚类和适当的距离度量进行聚类,以获得每种癌症类型的癌症亚型:二元体细胞突变谱和加权体细胞突变谱。根据所鉴定亚型的临床特征和生存时间的预测能力,具有Jaccard距离的二元体细胞突变谱(B-Jac)表现最好,具有欧几里得距离的加权体细胞突变谱(W-Euc)表现较好。这两种方法的表现都明显优于典型的体细胞突变方法,即具有欧几里得距离的二进制体细胞突变谱(B-Euc)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Cancer Subtypes based on Somatic Mutation Profile
Tumor stratification is one of the basic tasks in cancer genomics for a better understanding of the tumor heterogeneity and better targeted treatments. There are various biological data that can be used to stratify tumors including gene expression and sequencing data. In this work, we use the somatic mutation data. Two types of somatic mutation profiles are generated and clustered using k-means clustering with appropriate distance measures to obtain cancer subtypes for each cancer type: binary somatic mutation profile and weighted somatic mutation profile. According to the predictive power of clinical features and survival time of the identified subtypes, the binary somatic mutation profile with Jaccard distance (B-Jac) performed the best and the weighted somatic mutation profile with Euclidean distance (W-Euc) performed comparably. Both approaches performed significantly better than the typical usage of somatic mutation, i.e. the binary somatic mutation profile with Euclidean distance (B-Euc).
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