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

Chkhaidze, Ketevan; Heide, Timon; Werner, Benjamin; Williams, Marc; Huang, Weini; Caravagna, Giulio; Graham, Trevor A.; Sottoriva, Andrea

doi:10.1371/journal.pcbi.1007243

Cited by 73 publications

(133 citation statements)

References 59 publications

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“…Computational validation and MCMC inference framework. We implemented stochastic spatial simulations of mutation accumulation in growing tissues using previously published code 23 . Briefly, cell birth and death on a two-or threedimensional grid was simulated using a Gillespie algorithm 24 .…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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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 multisample 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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, a correction is unlikely to increase the resolution of our analysis. In addition, spatial constraints can introduce sampling bias into patterns of the clonal selection of the tumor, 17 therefore the analyses also included the average frequency of mutations from all available samples.…”

Section: Evolutionary Trajectorymentioning

confidence: 99%

Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine

Röcken

Amallraja

Halske

et al. 2020

Preprint

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Background: Cancer is a somatic evolutionary disease, which adapts to environmental cues based on genetic constraints. Using multiregional exome sequencing, we tested the effect of somatic evolution on intratumoral heterogeneity and its putative clinical implications in adenocarcinomas of the stomach and gastroesophageal junction (GC). Methods: The study comprised a prospective discovery cohort of 9 and a validation cohort of 487 GCs. Multiregional whole-exome sequencing was done using 48 tumor samples (range: 3-10 tumor samples/patient) of the discovery cohort.Results: In total, the discovery cohort harbored 16,537 non-synonymous mutations (mutations/sample: median n=159; mutations/patient: median n=369). Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. 53-91% of the non-synonymous mutations were not present in each patient’s sample; 399 genes harbored 2-4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Phylogenetic analyses provided evidence for branched evolution being the most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. Studies on the validation cohort showed that the subclonal loss of SMAD4 is an independent predictor for poor patient outcome.Conclusions: Neutral and non-neutral somatic evolution shape the mutational landscape in GC. It leads to complex spatial intratumoral heterogeneity and may have profound effects on patient management. It provides crucial information for an individualized understanding of clinical prognosis and therapeutic options in GC patients.

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“…In addition, the scaling of the ecDNA copy number distribution alone cannot distinguish neutrality and selection. This is different for point mutations, where the site frequency spectrum can in principal distinguish neutrality from sufficiently strong selection [29,37]. The scaling of the ecDNA copy number distribution depends solely on the within dynamics of cells with ecDNA.…”

Section: Inter-and Intra-tumour Heterogeneity Of Ecdnamentioning

confidence: 99%

Stochastic dynamics of extra-chromosomal DNA

Pichugin

Huang

Werner

2019

Preprint

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Nuclear extra-chromosomal DNA (ecDNA) is highly prevalent in human tumours. ecDNA amplifications can promote accessible chromatin, oncogen over-expression and imply a worse clinical prognosis.Yet, little is known about the evolutionary process of ecDNA in human cancers. Here, we develop the theoretical foundation of the ecDNA somatic evolutionary process combining mathematical, computational and experimental approaches. We show that random ecDNA segregation leads to unintuitive dynamics even if ecDNA is under very strong positive selection. Patterns of inter-and intra-tumour ecDNA and chromosomal heterogeneity differ markedly and standard approaches are not directly applicable to quantify ecDNA evolution. We show that evolutionary informed modelling leads to testable predictions on how to distinguish positively selected from neutral ecDNA dynamics. Our predictions describe the dynamics of circular amplicons in GBM39 cell line experiments, suggesting a 300% fitness increase due to circular extra-chromosomal EGFRvI I I amplifications. Our work lies the basis for further studies to quantitate ecDNA somatic evolutionary processes.

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

Cited by 73 publications

References 59 publications

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

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

Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine

Stochastic dynamics of extra-chromosomal DNA

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