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

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 !! 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.

Testing for phylogenetic signal in single-cell RNA-seq data

“…SNV detection and filtering. To obtain SNVs for individual cells of the scRNA-seq data, first a list of SNVs were obtained by running the Mutect2 (Benjamin et al 2019), treating the data set as a pseudo-bulk sample, and retaining only the SNVs that passed all filters.…”

“…Within-organism cancer evolution is increasingly being studied using population genetics approaches, including phylogenetics (Navin et al 2011; Yuan et al 2015; Alves et al 2017; Schwartz et al 2017; Caravagna et al 2018; Singer et al 2018; Alves et al 2019; Caravagna et al 2019; Detering et al 2019; Malikic et al 2019; Werner et al 2019; Kuipers et al 2020), to understand molecular dynamics of cancer cell populatons. These approaches have shown promise to be developed into therapeutic applications in the personalized medicine framework (Gerlinger et al 2012; Abbosh et al 2017; Rao et al 2020).…”

Starvation and Climate Change—How to Constrain Cancer Cell Epigenetic Diversity and Adaptability to Enhance Treatment Efficacy

“…Thus, a metastatic patient with multiple lesions and circulating tumor cells can have tens of billions of actively dividing malignant cells. With an average cell cycle time of ∼48 h and mutation rate of 1.14 mutations per genome per cell division (Werner et al, 2019), every gene in the cancer cell genome is affected by coding and non-coding genetic mutations multiple independent times in a patient with years of metastatic disease. All enemies are at the gate.…”

Stochastic dynamics of extra-chromosomal DNA

“…Consequently, patterns of inter-and intra-patient ecDNA heterogeneity differ compared to chromosomal point mutations [13,14,15]. Methods that quantify somatic evolutionary processes based on chromosomal point mutations, such as phylogenetic trees [16,17,18,19,20,21], ratios of synonymous and non-synonymous mutations (dN/dS) [22,23,24], or site-frequency spectra [25,26,27,10,28,29] are not directly applicable to quantify the evolutionary dynamics of ecDNA.…”