5月27日，记者从中国科学技术大学获悉，该校李骜研究组在复杂肿瘤基因检测方面取得突破性进展，他们提出的一种新颖生物信息学算法，有效解决了利用新一代测序技术检测复杂肿瘤全基因组异常的国际性难题。成果日前发表在国际权威期刊《生物信息学》上。 基因组异常是多种恶性肿瘤的标志性特征，在肿瘤发病机理、临床诊断和治疗等研究中具有极为重要的作用。但传统肿瘤基因组异常检测技术，存在测序样本数量少、分辨率差等问题。随着平行测序实验技术的兴起，新一代测序技术凭借其在测序数量和分辨率方面的独特优势，成为癌症基因组学研究中最流行的实验手段。但由于肿瘤本身的复杂性，从新一代测序数据中准确检测基因组异常仍面临着正常细胞掺杂和污染等棘手问题。目前，利用新一代测序技术检测肿瘤基因组异常的方法呈井喷式发展态势，但这些方法无法提供可靠且全面的基因组变异信息。 李骜研究组基于丰富的经验积累，在近一年半的时间里，开发出一整套处理和分析下一代测序数据的软件和方法，有效解决了肿瘤样本分析中涉及到的关键性问题。他们在论文中提出的新颖生物信息学算法，有效解决了复杂肿瘤全基因组异常检测的国际性难题。审稿人认为，这一新算法具有令人关注的特性，并为解决上述难题提供了极佳思路。 原文摘要：CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore efficient computational methods are required to address these issues. Results: We introduce CLImAT, a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. |
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