用于描述人类肿瘤中拷贝数改变的拷贝数估计算法及荧光原位杂交技术。

Copy number estimation algorithms and fluorescence in situ hybridization to describe copy number alterations in human tumors.

作者信息

Suzuki Masaya, Nagura Kiyoko, Igarashi Hisaki, Tao Hong, Midorikawa Yutaka, Kitayama Yasuhiko, Sugimura Haruhiko

机构信息

Department of Pathology, Hamamatsu University School of Medicine, Hamamatsu, Japan.

出版信息

Pathol Int. 2009 Apr;59(4):218-28. doi: 10.1111/j.1440-1827.2009.02354.x.

Abstract

The platforms of high-resolution genetic analysis of human tumors have become popular, and several copy number estimation algorithms have been applied to the data generated by single-nucleotide polymorphism microarrays. Although comparisons have been made between several different platforms or methodologies, there has never been a robust comparison of different copy number estimation algorithms, and the validity of the estimations in comparison with multiple fluorescence in situ hybridization (FISH) data in tumors has rarely been addressed. In the present study the dataset that the Affymetrix 250K Nsp array generated in two cancer cases was used to compare the two widely used algorithms for estimating copy number alterations (CNA): the genotyping microarray-based copy number variation (CNV) analysis (GEMCA) algorithm and the copy number analyzer for Affymetrix Genechip mapping (CNAG) algorithm. Considerable differences were noticed between the estimations by these two algorithms, because of the difference in the formula used to calculate the threshold values. Both algorithms yielded highly consistent data with the FISH results, but CNAG was more stringent for detecting loss. There were areas in which both algorithms provided gains, but FISH showed no change. It will be interesting to pursue the reasons for these remaining discrepancies.

摘要

人类肿瘤的高分辨率基因分析平台已广受欢迎,几种拷贝数估计算法已应用于单核苷酸多态性微阵列产生的数据。尽管已对几种不同的平台或方法进行了比较,但从未对不同的拷贝数估计算法进行过有力的比较,并且与肿瘤中多个荧光原位杂交(FISH)数据相比,估计的有效性很少得到探讨。在本研究中,使用Affymetrix 250K Nsp阵列在两个癌症病例中产生的数据集,比较两种广泛使用的估计拷贝数改变(CNA)的算法:基于基因分型微阵列的拷贝数变异(CNV)分析(GEMCA)算法和Affymetrix基因芯片图谱拷贝数分析仪(CNAG)算法。由于用于计算阈值的公式不同,这两种算法的估计结果存在显著差异。两种算法产生的数据与FISH结果高度一致,但CNAG在检测缺失方面更为严格。在某些区域,两种算法都显示有增益,但FISH显示无变化。探究这些剩余差异的原因将很有趣。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索