Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America.
PLoS Comput Biol. 2020 Oct 1;16(10):e1008240. doi: 10.1371/journal.pcbi.1008240. eCollection 2020 Oct.
The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.
对同一肿瘤的大量和单细胞 DNA 测序数据的组合，能够推断出高精度的系统发育树，这些系统发育树是癌症基因组学中许多重要下游分析的输入。虽然许多研究同时进行大量和单细胞测序，但有些研究已经分析了初始的大量数据，以确定在后续的单细胞测序实验中要针对哪些突变，从而降低成本。大量数据提供了一个额外的未开发的有价值信息来源，包括候选系统发育树和相关的克隆流行率。在这里，我们引入了 PhyDOSE，这是一种利用这些信息来优化后续单细胞实验设计的方法。我们的方法的基础是观察到只有少数克隆可以唯一地区分一个候选树与所有其他树。我们将区分特征纳入概率模型中，该模型推断出要测序的细胞数量，以便有信心重建肿瘤的系统发育树。我们使用模拟和对白血病患者的回顾性分析来验证 PhyDOSE，得出的结论是，即使存在典型的单细胞测序错误，PhyDOSE 计算的细胞数量也可以解决树的模糊性。我们还对急性髓系白血病队列进行了回顾性分析，表明通过显著减少测序细胞数量，可以实现类似的结果。在前瞻性分析中，我们证明了在多个活检中选择要测序的细胞的优势，并且只有少量细胞就足以消除最近的肺癌队列中树的解决方案空间的模糊性。总之，PhyDOSE 提出了具有成本效益的单细胞测序实验，可产生高精度的系统发育树，这将改善旨在加深我们对癌症生物学理解的下游分析。
