Orchard: building large cancer phylogenies using stochastic
  combinatorial search
E. Kulman R. Kuang Q. Morris
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.