ArticlePLoS computational biology2019
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- 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
- Agent-Based Simulations of Lung Tumor Evolution Suggest That Ongoing Cell Competition Drives Realistic Clonal Expansions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A hybrid multiscale model for predicting CAR-T therapy outcomes in solid tumors.Scientific reports · 2026Article
- tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients.NPJ systems biology and applications · 2026Article
- Genetic barcoding uncovers the clonal makeup of solid and liquid biopsies and their ability to capture intra-tumoral heterogeneity.Molecular systems biology · 2026Article
- A hybrid Ornstein-Uhlenbeck-Branching framework unifies microbial and pediatric tumor evolution.Frontiers in oncology · 2026Article
- Extrachromosomal DNA-Driven Oncogene Spatial Heterogeneity and Evolution in Glioblastoma.Cancer discovery · 2025Article
- Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.PLoS computational biology · 2025Article
- Extrachromosomal DNA: shaping the evolutionary dynamics of cancer.Trends in cancer · 2025Review
- 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
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- A theory of evolutionary dynamics on any complex population structure reveals stem cell niche architecture as a spatial suppressor of selection.Nature communications · 2024Article
- A seven-step guide to spatial, agent-based modelling of tumour evolution.Evolutionary applications · 2024Review
- Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma.Nature communications · 2024Article
- Modeling the Effect of Spatial Structure on Solid Tumor Evolution and Circulating Tumor DNA Composition.Cancers · 2024Article
- Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition.bioRxiv : the preprint server for biology · 2023Article
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8 authors.
Funding
Abstract
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.
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