, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, 3000, Australia.
The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, 3010, Australia.
Genome Biol. 2022 Feb 2;23(1):43. doi: 10.1186/s13059-022-02600-6.
Clonal analysis of tumour sequencing data enables the evaluation of the relationship of histologically distinct synchronous lesions, such as co-existing benign areas, and temporally distinct tumours, such as primary-recurrence comparisons. In this review, we summarise statistical approaches that are commonly employed to define tumour clonal relatedness using data from bulk DNA technologies. We discuss approaches using total copy number, allele-specific copy number and mutation data, and the relative genomic resolution required for analysis and summarise some of the current tools for inferring clonal relationships. We argue that the impact of the biological context is critical in selecting any particular approach, such as the relative genomic complexity of the lesions being compared, and we recommend considering this context before employing any method to a new dataset.
肿瘤测序数据的克隆分析可评估组织学上不同的同步病变（如共存的良性区域）和时间上不同的肿瘤（如原发性-复发性比较）之间的关系。在这篇综述中，我们总结了常用的统计方法，这些方法使用来自批量 DNA 技术的数据来定义肿瘤克隆相关性。我们讨论了使用总拷贝数、等位基因特异性拷贝数和突变数据的方法，以及分析所需的相对基因组分辨率，并总结了一些推断克隆关系的当前工具。我们认为，生物背景的影响在选择任何特定方法时都至关重要，例如正在比较的病变的相对基因组复杂性，我们建议在将任何方法应用于新数据集之前考虑这种背景。
