Genotype patterns in growing solid tumors

Nanda, Mridu; Durrett, Richard

doi:10.1101/390385

“…The spatial effects of drift and sampling bias one can observe are remarkable and represent a major challenge for the correct subclonal reconstruction of tumours growing in three-dimensional space. Due to the inherent complexity, analytical solutions to this problem that take space into the account remain challenging, although some attempts to tackle this difficult question are being undertaken [49]. Understanding the complex impact of spatially growing cell populations on the actual genomic data requires an approach based on computational simulations.…”

Section: Resultsmentioning

confidence: 99%

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

Chkhaidze

¹

,

Heide

²

,

Werner

³

et al. 2019

View full text Add to dashboard Cite

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.

show abstract

“…The spatial effects of drift and sampling bias one can observe are remarkable and represent a major challenge for the correct subclonal reconstruction of tumours growing in threedimensional space. Due to the inherent complexity, analytical solutions to this problem that take space into the account remain challenging, although some attempts to tackle this difficult question are being undertaken [39]. However, understanding the complex impact of spatially growing cell populations on the actual genomic data requires an approach based on computational simulations.…”

Section: Resolving Spatial Effects With Inferencementioning

confidence: 99%

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

K

¹

,

Heide

²

,

Werner

³

et al. 2019

Preprint

View full text Add to dashboard Cite

Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data. SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

show abstract

“…Tumors can be considered as an ecosystem of interacting sub-clones [6,7], but as spatially structured populations [2,[8][9][10][11][12], they may not follow theories for well-mixed populations [13]. Unfortunately, analytical understanding of structured populations is limited to specific structures [14] which necessitates development of different models to understand tumor evolution [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Short-range migration can alter evolutionary dynamics in solid tumors

Azimzade¹,

Saberi²

2019

View full text Add to dashboard Cite

Here, we investigate how competition in the Eden model is affected by short range dispersal and the requirement that site updates occur only after several updates of the same site have been attempted previously. The latter models the effect of tissue or media resistance to invasion. We found that the existence of tissue intensifies Natural Selection and de-accelerating Genetic Drift, both to a limited extent. More interestingly, our results show that short-range migration can eliminate Genetic demixing and conceal Natural Selection. *

show abstract

Genotype patterns in growing solid tumors

Cited by 3 publications

References 43 publications

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

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

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

Short-range migration can alter evolutionary dynamics in solid tumors

Contact Info

Product

Resources

About