ArticleNature ecology & evolution2023
State-dependent evolutionary models reveal modes of solid tumour growth.
Article in Nature ecology & evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 45 citations in OpenAlex.
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- Evidence of latency reshapes our understanding of Ebola virus reservoir dynamics.bioRxiv : the preprint server for biology · 2025Article
- Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.PLoS computational biology · 2025Article
- A graph homomorphism approach for unraveling histories of metastatic cancers and viral outbreaks under evolutionary constraints.Nature communications · 2025Article
- Quantitative and sensitive sequencing of somatic mutations induced by a maize transposon.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.bioRxiv : the preprint server for biology · 2025Article
- Detecting branching rate heterogeneity with tree balance statistics in lineage tracing trees.bioRxiv : the preprint server for biology · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Bayesian phylodynamic inference of population dynamics with dormancy.bioRxiv : the preprint server for biology · 2025Article
- PhyloJunction: A Computational Framework for Simulating, Developing, and Teaching Evolutionary Models.Systematic biology · 2024Article
- Article
- Spatial interactions modulate tumor growth and immune infiltration.NPJ systems biology and applications · 2024Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Spatial properties of tumour growth have profound implications for cancer progression, therapeutic resistance and metastasis. Yet, how spatial position governs tumour cell division remains difficult to evaluate in clinical tumours. Here, we demonstrate that faster division on the tumour periphery leaves characteristic genetic patterns, which become evident when a phylogenetic tree is reconstructed from spatially sampled cells. Namely, rapidly dividing peripheral lineages branch more extensively and acquire more mutations than slower-dividing centre lineages. We develop a Bayesian state-dependent evolutionary phylodynamic model (SDevo) that quantifies these patterns to infer the differential division rates between peripheral and central cells. We demonstrate that this approach accurately infers spatially varying birth rates of simulated tumours across a range of growth conditions and sampling strategies. We then show that SDevo outperforms state-of-the-art, non-cancer multi-state phylodynamic methods that ignore differential sequence evolution. Finally, we apply SDevo to single-time-point, multi-region sequencing data from clinical hepatocellular carcinomas and find evidence of a three- to six-times-higher division rate on the tumour edge. With the increasing availability of high-resolution, multi-region sequencing, we anticipate that SDevo will be useful in interrogating spatial growth restrictions and could be extended to model non-spatial factors that influence tumour progression.
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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.