ArticleCell genomics2023
Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues.
Article in Cell genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Phylogenetic and phylodynamic approaches to understanding the evolution of non-small cell lung cancer using single-cell sequencing.Oncogene · 2026Article
- A guide to understanding tumour evolution through the lens of population genetics.Nature reviews. Cancer · 2026Review
- A single-cell lens into the co-evolution of genotypes and phenotypes in cancer.Nature reviews. Cancer · 2026Review
- LeafRank: A phylodynamic framework for inferring relative fitness from single-cell phylogenies in chromosomally unstable tumors.bioRxiv : the preprint server for biology · 2026Article
- PyEvoMotion: a Python tool for population-based time-course analysis of genome evolution.Bioinformatics (Oxford, England) · 2026Article
- Detecting branching rate heterogeneity with tree balance statistics in lineage tracing trees.bioRxiv : the preprint server for biology · 2025Article
- A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- GenoPath: a pipeline to infer tumor clone composition, mutational history, and metastatic cell migration events from tumor DNA sequencing data.Frontiers in bioinformatics · 2025Article
- Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.