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

Benjamin Werner(Institute of Cancer Research), Jack Case(Institute of Cancer Research), Marc Williams(Queen Mary University of London), Kate Chkhaidze(Institute of Cancer Research), Daniel Temko(Queen Mary University of London), Javier Fernández-Mateos(Institute of Cancer Research), George D. Cresswell(Institute of Cancer Research), Daniel Nichol(Institute of Cancer Research), William Cross(Queen Mary University of London), Inmaculada Spiteri(Institute of Cancer Research), Weini Huang(Sun Yat-sen University), Ian Tomlinson(University of Birmingham), C. Barnes(University College London), Trevor A. Graham(Queen Mary University of London), Andrea Sottoriva(Institute of Cancer Research)
bioRxiv (Cold Spring Harbor Laboratory)
February 25, 2019
Cited by 3Open Access
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Abstract

Abstract Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean: 1.69×10 −8 bp per cell division) and per-cell survival rate (mean: 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate: 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.


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