Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
Loading...
Embargo End Date
Authors
Chkhaidze, K
Heide, T
Werner, B
Williams, MJ
Huang, W
Caravagna, G
Graham, TA
Sottoriva, A
Heide, T
Werner, B
Williams, MJ
Huang, W
Caravagna, G
Graham, TA
Sottoriva, A
Document Type
Journal Article
Date
2019-07-29
Date Accepted
2019-07-05
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.
Citation
PLoS computational biology, 2019, 15 (7), pp. e1007243 - ?
Source Title
Publisher
PUBLIC LIBRARY SCIENCE
ISSN
1553-734X
eISSN
1553-7358
Research Team
Evolutionary Genomics & Modelling
Paediatric Solid Tumour Biology and Therapeutics
Paediatric Solid Tumour Biology and Therapeutics