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

Ketevan Chkhaidze, Timon Heide, Benjamin Werner, Marc J Williams, Weini Huang, Giulio Caravagna, Trevor A Graham and Andrea Sottoriva

PLOS Computational Biology, 2019, vol. 15, issue 7, 1-26

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.Author summary: Sequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mechanistic mathematical and computational models that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial computational model of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to start overcoming these biases and more robustly measure aspects of the biology of tumours from the data.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007243

DOI: 10.1371/journal.pcbi.1007243

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