Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
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 constraint...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007243 |
| _version_ | 1798030181649088512 |
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| author | Ketevan Chkhaidze Timon Heide Benjamin Werner Marc J Williams Weini Huang Giulio Caravagna Trevor A Graham Andrea Sottoriva |
| author_facet | Ketevan Chkhaidze Timon Heide Benjamin Werner Marc J Williams Weini Huang Giulio Caravagna Trevor A Graham Andrea Sottoriva |
| author_sort | Ketevan Chkhaidze |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:37:07Z |
| format | Article |
| id | doaj.art-3e188ccc5e124ed7ae1596ad08c87868 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T19:37:07Z |
| publishDate | 2019-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-3e188ccc5e124ed7ae1596ad08c878682022-12-22T04:06:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582019-07-01157e100724310.1371/journal.pcbi.1007243Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.Ketevan ChkhaidzeTimon HeideBenjamin WernerMarc J WilliamsWeini HuangGiulio CaravagnaTrevor A GrahamAndrea SottorivaQuantification 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.https://doi.org/10.1371/journal.pcbi.1007243 |
| spellingShingle | Ketevan Chkhaidze Timon Heide Benjamin Werner Marc J Williams Weini Huang Giulio Caravagna Trevor A Graham Andrea Sottoriva Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLoS Computational Biology |
| title | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. |
| title_full | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. |
| title_fullStr | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. |
| title_full_unstemmed | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. |
| title_short | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. |
| title_sort | spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data |
| url | https://doi.org/10.1371/journal.pcbi.1007243 |
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