Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Department of Computer Science, Brown University, Providence, RI 02912, USA.
Cell Syst. 2019 Jun 26;8(6):514-522.e5. doi: 10.1016/j.cels.2019.05.010. Epub 2019 Jun 19.
Longitudinal DNA sequencing of cancer patients yields insight into how tumors evolve over time or in response to treatment. However, sequencing data from bulk tumor samples often have considerable ambiguity in clonal composition, complicating the inference of ancestral relationships between clones. We introduce Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction (CALDER), an algorithm to infer phylogenetic trees from longitudinal bulk DNA sequencing data. CALDER explicitly models a longitudinally observed phylogeny incorporating constraints that longitudinal sampling imposes on phylogeny reconstruction. We show on simulated bulk tumor data that longitudinal constraints substantially reduce ambiguity in phylogeny reconstruction and that CALDER outperforms existing methods that do not leverage this longitudinal information. On real data from two chronic lymphocytic leukemia patients, we find that CALDER reconstructs more plausible and parsimonious phylogenies than existing methods, with CALDER phylogenies containing fewer tumor clones per sample. CALDER's use of longitudinal information will be advantageous in further studies of tumor heterogeneity and evolution.
对癌症患者进行纵向 DNA 测序可以深入了解肿瘤随时间推移或对治疗的反应如何演变。然而，来自肿瘤样本的测序数据在克隆组成方面通常存在很大的不确定性，这使得推断克隆之间的祖先关系变得复杂。我们引入了通过进化重建进行纵向数据分析（CALDER），这是一种从纵向批量 DNA 测序数据中推断系统发育树的算法。CALDER 从纵向观察到的系统发育树出发，明确地进行建模，同时考虑到纵向采样对系统发育重建施加的约束。我们在模拟的肿瘤批量数据上表明，纵向约束大大降低了系统发育重建的不确定性，并且 CALDER 优于那些没有利用这种纵向信息的现有方法。在来自两名慢性淋巴细胞白血病患者的真实数据上，我们发现，CALDER 构建的系统发育树比现有方法更合理且更简洁，CALDER 系统发育树中每个样本的肿瘤克隆数量更少。CALDER 对纵向信息的使用将有助于进一步研究肿瘤异质性和进化。
