ArticleScientific reports2020
Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 31 citations in OpenAlex.
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- GenoPath: a pipeline to infer tumor clone composition, mutational history, and metastatic cell migration events from tumor DNA sequencing data.Frontiers in bioinformatics · 2025Article
- Whole exome sequencing and single-cell DNA sequencing for assessment of clonal heterogeneity and evolution in acute myeloid leukemia.Computational and structural biotechnology journal · 2025Article
- Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data.Frontiers in bioinformatics · 2023Article
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- A phylogenetic approach to study the evolution of somatic mutational processes in cancer.Communications biology · 2022Article
- Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer.Cancers · 2022Article
- CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.BMC bioinformatics · 2022Article
- Implantation metastasis from sigmoid colon cancer to rectal anastomosis proved by whole exome sequencing and lineage inference for cancer heterogeneity and evolution analysis: Case report and literature review.Frontiers in oncology · 2022Article
- Genetic Analysis and Combined Therapy of Surgery and Chemotherapy for the Progression-Free Survival of a Patient with Ovarian Carcinosarcoma: A Case Report and Literature Review.OncoTargets and therapy · 2022Article
- Tumors Are Evolutionary Island-Like Ecosystems.Genome biology and evolution · 2021Article
- The Generalized Robinson-Foulds Distance for Phylogenetic Trees.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Migrations of cancer cells through the lens of phylogenetic biogeography.Scientific reports · 2021Article
- PathFinder: Bayesian inference of clone migration histories in cancer.Bioinformatics (Oxford, England) · 2020Article
- SCARLET: Single-cell tumor phylogeny inference with copy-number constrained mutation losses.Cell systems · 2020Article
- Article
- Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology.Cancers · 2019Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.
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