一种分析异质肿瘤样本染色体畸变的新型HMM

Hong Xia, Yuanning Liu, Minghui Wang, Huanqing Feng, Ao Li
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引用次数: 1

摘要

需要对染色体畸变的拷贝数和LOH进行全面的检测和鉴定,为人类癌症的准确治疗提供依据。作为一种节省成本和高通量的工具,SNP阵列有助于分析整个基因组的染色体畸变。以前的方法的性能仅限于几个关键问题,如正常细胞污染,非整倍体和肿瘤异质性。基于这些原因,我们提出了一种基于隐马尔可夫模型(HMM)的方法,称为TH-HMM(肿瘤异质性HMM),用于使用Illumina SNP阵列的数据同时检测异质性肿瘤样本的拷贝数和LOH。通过采用高效的EM算法,我们的方法可以正确地检测肿瘤亚克隆中的染色体畸变事件。对模拟数据序列的评估表明,TH-HMM可以准确地估计正常细胞和亚克隆的比例,并最终恢复每个克隆的畸变曲线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel HMM for analyzing chromosomal aberrations in heterogeneous tumor samples
Comprehensive detection and identification of copy number and LOH of chromosomal aberration is required to provide an accurate therapy of human cancer. As a cost-saving and high-throughput tool, SNP arrays facilitate analysis of chromosomal aberration throughout the whole genome. The performance of previous approaches has been limited to several critical issues such as normal cell contamination, aneuploidy and tumor heterogeneity. For these reasons we present a Hidden Markov Model (HMM) based approach called TH-HMM (Tumor Heterogeneity HMM), for simultaneous detection of copy number and LOH in heterogeneous tumor samples using data from Illumina SNP arrays. Through adopting an efficient EM algorithm, our method can correctly detect chromosomal aberration events in tumor subclones. Evaluation on simulated data series indicated that TH-HMM could accurately estimate both normal cell and subclone proportions, and finally recovery the aberration profiles for each clones.
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