Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

PLoS Comput Biol. 2019 Jul 29;15(7):e1007243. doi: 10.1371/journal.pcbi.1007243. eCollection 2019 Jul.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Proliferation
  • Clonal Evolution
  • Computational Biology
  • Computer Simulation
  • Genetic Drift
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Models, Biological*
  • Models, Genetic
  • Mutation
  • Neoplasms / genetics*
  • Neoplasms / pathology*
  • Single-Cell Analysis
  • Stochastic Processes