Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer i.e., cancer phylogenies, provide useful insights about cancer development and inform treatment. Cancer phylogenies can be reconstructed using data obtained from bulk DNA sequencing of multiple tissue samples from the same cancer. We introduce Orchard, a fast algorithm that reconstructs cancer phylogenies using point mutations detected in bulk DNA sequencing data. Orchard constructs cancer phylogenies progressively, one point mutation at a time, ultimately sampling complete phylogenies from a posterior distribution implied by the bulk DNA data. Orchard reconstructs more plausible phylogenies than state-of-the-art cancer phylogeny reconstruction methods on 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). These results demonstrate that Orchard accurately reconstructs cancer phylogenies with up to 300 mutations. We then introduce a simple graph based clustering algorithm that uses a reconstructed phylogeny to infer unique groups of mutations i.e., mutation clusters, that characterize the genetic differences between cancer cell populations, and show that this approach is competitive with state-of-the-art mutation clustering methods.

Orchard算法重构了最多300个突变的癌症进化树，并采用基于图的聚类算法推断出具有遗传差异的肿瘤细胞群的突变群集。

Orchard：使用随机组合搜索构建大型癌症谱系图