
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data
Author(s) -
Ketevan Chkhaidze,
Timon Heide,
Benjamin Werner,
Marc Williams,
Weini Huang,
Giulio Caravagna,
Trevor A. Graham,
Andrea Sottoriva
Publication year - 2019
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1007243
Subject(s) - confounding , inference , selection (genetic algorithm) , spatial analysis , sampling (signal processing) , sampling bias , biology , computer science , computational biology , neutral theory of molecular evolution , data mining , statistics , genetics , artificial intelligence , mathematics , sample size determination , gene , filter (signal processing) , computer vision
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.