Fischer Andrej, Vázquez-García Ignacio, Illingworth Christopher J R, Mustonen Ville
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK.
Cell Rep. 2014 Jun 12;7(5):1740-1752. doi: 10.1016/j.celrep.2014.04.055. Epub 2014 May 29.
The extensive genetic heterogeneity of cancers can greatly affect therapy success due to the existence of subclonal mutations conferring resistance. However, the characterization of subclones in mixed-cell populations is computationally challenging due to the short length of sequence reads that are generated by current sequencing technologies. Here, we report cloneHD, a probabilistic algorithm for the performance of subclone reconstruction from data generated by high-throughput DNA sequencing: read depth, B-allele counts at germline heterozygous loci, and somatic mutation counts. The algorithm can exploit the added information present in correlated longitudinal or multiregion samples and takes into account correlations along genomes caused by events such as copy-number changes. We apply cloneHD to two case studies: a breast cancer sample and time-resolved samples of chronic lymphocytic leukemia, where we demonstrate that monitoring the response of a patient to therapy regimens is feasible. Our work provides new opportunities for tracking cancer development.
癌症广泛的基因异质性会因赋予抗性的亚克隆突变的存在而极大地影响治疗效果。然而，由于当前测序技术产生的序列读段长度较短，在混合细胞群体中对亚克隆进行表征在计算上具有挑战性。在此，我们报告了cloneHD，这是一种概率算法，用于从高通量DNA测序产生的数据（读深度、种系杂合位点的B等位基因计数和体细胞突变计数）中进行亚克隆重建。该算法可以利用相关纵向或多区域样本中存在的额外信息，并考虑由拷贝数变化等事件引起的沿基因组的相关性。我们将cloneHD应用于两个案例研究：一个乳腺癌样本和慢性淋巴细胞白血病的时间分辨样本，在这些研究中我们证明监测患者对治疗方案的反应是可行的。我们的工作为追踪癌症发展提供了新的机会。
