Orchard: Building large cancer phylogenies using stochastic combinatorial search.
Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point muta...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-12-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012653 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533253454921728 |
|---|---|
| author | Ethan Kulman Rui Kuang Quaid Morris |
| author_facet | Ethan Kulman Rui Kuang Quaid Morris |
| author_sort | Ethan Kulman |
| collection | DOAJ |
| description | Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations. |
| format | Article |
| id | doaj-art-a8e7ba6c884f4f5e9e13fc2100f85046 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-a8e7ba6c884f4f5e9e13fc2100f850462025-01-17T05:30:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101265310.1371/journal.pcbi.1012653Orchard: Building large cancer phylogenies using stochastic combinatorial search.Ethan KulmanRui KuangQuaid MorrisPhylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations.https://doi.org/10.1371/journal.pcbi.1012653 |
| spellingShingle | Ethan Kulman Rui Kuang Quaid Morris Orchard: Building large cancer phylogenies using stochastic combinatorial search. PLoS Computational Biology |
| title | Orchard: Building large cancer phylogenies using stochastic combinatorial search. |
| title_full | Orchard: Building large cancer phylogenies using stochastic combinatorial search. |
| title_fullStr | Orchard: Building large cancer phylogenies using stochastic combinatorial search. |
| title_full_unstemmed | Orchard: Building large cancer phylogenies using stochastic combinatorial search. |
| title_short | Orchard: Building large cancer phylogenies using stochastic combinatorial search. |
| title_sort | orchard building large cancer phylogenies using stochastic combinatorial search |
| url | https://doi.org/10.1371/journal.pcbi.1012653 |
| work_keys_str_mv | AT ethankulman orchardbuildinglargecancerphylogeniesusingstochasticcombinatorialsearch AT ruikuang orchardbuildinglargecancerphylogeniesusingstochasticcombinatorialsearch AT quaidmorris orchardbuildinglargecancerphylogeniesusingstochasticcombinatorialsearch |