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

BioRxiv : the Preprint Server for Biology
Katevan ChkhaidzeAndrea Sottoriva

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 constrains, 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 bulk sequencing data 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 present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.

Related Concepts

Biological Evolution
Genome
Neoplasms
Spatial Distribution
Solid Carcinoma
Sampling - Surgical Action
Patterns
Somatic Mutation
Tumor Growth
Simulation

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