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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.
Quantifying somatic evolutionary processes in cancer and healthy tissue is a challenge. Here, the authors use single time point multi-region sampling of cancer and normal tissue, combined with evolutionary theory, to quantify in vivo mutation and cell survival rates per cell division.
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1 The Institute of Cancer Research, Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, London, UK (GRID:grid.18886.3f) (ISNI:0000 0001 1271 4623); Queen Mary University of London, Evolutionary Dynamics Group, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133)
2 The Institute of Cancer Research, Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, London, UK (GRID:grid.18886.3f) (ISNI:0000 0001 1271 4623); University of Cambridge, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934)
3 Queen Mary University London, Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); University College London, Department of Cell and Developmental Biology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University College London, Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)
4 The Institute of Cancer Research, Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, London, UK (GRID:grid.18886.3f) (ISNI:0000 0001 1271 4623)
5 Queen Mary University London, Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133)
6 Sun Yat-sen University, Group of Theoretical Biology, The State Key Laboratory of Biocontrol, School of Life Science, Guangzhou, China (GRID:grid.12981.33) (ISNI:0000 0001 2360 039X); Queen Mary University London, School of Mathematical Sciences, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133)
7 University of Birmingham, Institute of Cancer and Genomic Sciences, Birmingham, UK (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486)
8 University College London, Department of Cell and Developmental Biology, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201); University College London, UCL Genetics Institute, London, UK (GRID:grid.83440.3b) (ISNI:0000000121901201)