利用三种及以上肿瘤的杂合性缺失谱检测其克隆性。

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Irina Ostrovnaya
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引用次数: 2

摘要

癌症患者经常发展为多种恶性肿瘤,可能是先前癌症(克隆肿瘤)的转移性扩散,也可能是新的原发癌症(独立肿瘤)。如果在病理检查的基础上诊断不容易,可以比较肿瘤中体细胞突变的模式。以前,我们已经开发了统计方法来检测两种肿瘤的克隆性,使用它们在几个候选标记上的杂合性缺失(LOH)谱。当分析多个肿瘤时,这些方法可以应用于所有可能的肿瘤对,但这种策略可能导致结果不一致并失去统计能力。在这项工作中,我们将把克隆测试扩展到来自同一患者的三种或更多恶性肿瘤。非参数测试可以使用任何可能的肿瘤子集进行,随后进行多次测试调整。建立了3个或4个肿瘤的参数似然模型,可用于估计肿瘤的系统发育树。建议的测试比所有可能的成对测试的组合更强大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Testing clonality of three and more tumors using their loss of heterozygosity profiles.

Cancer patients often develop multiple malignancies that may be either metastatic spread of a previous cancer (clonal tumors) or new primary cancers (independent tumors). If diagnosis cannot be easily made on the basis of the pathology review, the patterns of somatic mutations in the tumors can be compared. Previously we have developed statistical methods for testing clonality of two tumors using their loss of heterozygosity (LOH) profiles at several candidate markers. These methods can be applied to all possible pairs of tumors when multiple tumors are analyzed, but this strategy can lead to inconsistent results and loss of statistical power. In this work we will extend clonality tests to three and more malignancies from the same patient. A non-parametric test can be performed using any possible subset of tumors, with the subsequent adjustment for multiple testing. A parametric likelihood model is developed for 3 or 4 tumors, and it can be used to estimate the phylogenetic tree of tumors. The proposed tests are more powerful than combination of all possible pairwise tests.

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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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