Measuring single cell divisions in human tissues from multi-region sequencing data

Benjamin Werner,Jack Case,Daniel Temko,George D Cresswell,Andrea Sottoriva,Ketevan Chkhaidze,Ian P M Tomlinson,Chris P Barnes,Weini Huang,Inmaculada Spiteri,Marc J Williams,William Cross,Javier Fernández-Mateos,Daniel Nichol,Trevor A Graham

doi:10.1038/s41467-020-14844-6

Abstract

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.

Highlights

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity
We introduce a Bayesian sampling framework based on the mutational distance distribution, allowing us to disentangle mutation rates per cell division and cell survival/death rates
All cells in a sample of a tissue must have descended from a most recent common ancestor cell (MRCA) that was present in that tissue at an earlier time (Fig. 1a)

Summary

Introduction

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. We show that multiple bulk or single-cell sequencing from the same patient contain recoverable information on these important quantities that can be recovered with evolutionary theory This allows inferring in vivo cell mutation and cell survival rates in tissues of individual humans from single time point sequencing data. We introduce a Bayesian sampling framework based on the mutational distance distribution, allowing us to disentangle mutation rates per cell division and cell survival/death rates We apply this framework to whole-genome single-cell sequencing data of haematopoiesis and brain tissue and measure both evolutionary parameters during early development. We utilise multi-sample sequencing data on 16 tumours to infer patient specific evolutionary parameters in human cancers

Methods

Results

Conclusion