Donmez Nilgun, Malikic Salem, Wyatt Alexander W, Gleave Martin E, Collins Colin C, Sahinalp S Cenk
1 School of Computing Science, Simon Fraser University , Burnaby, Canada .
2 Vancouver Prostate Centre , Vancouver, Canada .
J Comput Biol. 2017 Jun;24(6):515-523. doi: 10.1089/cmb.2016.0148. Epub 2017 Jan 5.
Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. Although several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims at inferring the subclonal composition by using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy, even restricted to a coverage depth of ∼30 × .
肿瘤内异质性的推断可为癌症演变提供有价值的见解。通过测序检测到的体细胞突变有助于估计肿瘤样本的纯度并重建其亚克隆组成。尽管已经开发了几种方法来推断肿瘤内异质性,但这些工具中的大多数都依赖于通过对同一肿瘤的多个样本进行超深度测序估计的变异等位基因频率。在实践中,仅在少数癌症类型(如液体肿瘤)中,或在涉及为研究而选择的实体瘤的罕见情况下,才可行从每位患者获取大量样本的测序数据。我们引入了CTPsingle,其旨在通过使用来自单个肿瘤样本的低覆盖度测序数据来推断亚克隆组成。我们表明,CTPsingle能够高精度地推断单样本肿瘤的纯度和克隆性,即使限于约30×的覆盖深度。