Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing the mutational processes...
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| Format: | Article |
| Language: | English |
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Peer Community In
2023-07-01
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.292/ |
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| author | Salehi, Sohrab Dorri, Fatemeh Chern, Kevin Kabeer, Farhia Rusk, Nicole Funnell, Tyler Williams, Marc J. Lai, Daniel Andronescu, Mirela Campbell, Kieran R. McPherson, Andrew Aparicio, Samuel Roth, Andrew Shah, Sohrab P. Bouchard-Côté, Alexandre |
| author_facet | Salehi, Sohrab Dorri, Fatemeh Chern, Kevin Kabeer, Farhia Rusk, Nicole Funnell, Tyler Williams, Marc J. Lai, Daniel Andronescu, Mirela Campbell, Kieran R. McPherson, Andrew Aparicio, Samuel Roth, Andrew Shah, Sohrab P. Bouchard-Côté, Alexandre |
| author_sort | Salehi, Sohrab |
| collection | DOAJ |
| description | A new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing the mutational processes that gave rise to them. Existing phylogenetic tree building models do not scale to the tens of thousands of high resolution genomes achievable with current scWGS methods. We constructed a phylogenetic model and associated Bayesian inference procedure, sitka, specifically for scWGS data. The method is based on a novel phylogenetic encoding of copy number (CN) data, the sitka transformation, that simplifies the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. We demonstrate our method on three single cell datasets, including a novel PDX series, and analyse the topological properties of the inferred trees. Sitka is freely available at https://github.com/UBC-Stat-ML/sitkatree.git
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| format | Article |
| id | doaj-art-7d336692a2954e17ba3b00dfa26ea966 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-7d336692a2954e17ba3b00dfa26ea9662025-02-07T10:16:49ZengPeer Community InPeer Community Journal2804-38712023-07-01310.24072/pcjournal.29210.24072/pcjournal.292Cancer phylogenetic tree inference at scale from 1000s of single cell genomes Salehi, Sohrab0Dorri, Fatemeh1Chern, Kevin2Kabeer, Farhia3https://orcid.org/0000-0003-3456-507XRusk, Nicole4https://orcid.org/0000-0003-2663-6288Funnell, Tyler5https://orcid.org/0000-0003-1612-5644Williams, Marc J.6https://orcid.org/0000-0001-5524-4174Lai, Daniel7https://orcid.org/0000-0001-9203-6323Andronescu, Mirela8Campbell, Kieran R.9https://orcid.org/0000-0003-1981-5763McPherson, Andrew10https://orcid.org/0000-0002-5654-5101Aparicio, Samuel11https://orcid.org/0000-0002-0487-9599Roth, Andrew12https://orcid.org/0000-0003-3422-8823Shah, Sohrab P.13https://orcid.org/0000-0001-6402-523XBouchard-Côté, Alexandre14Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USADepartment of Computer Science, University of British Columbia, CanadaDepartment of Statistics, University of British Columbia, CanadaDepartment of Pathology and Laboratory Medicine, University of British Columbia, CanadaComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USAComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USAComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USADepartment of Pathology and Laboratory Medicine, University of British Columbia, Canada; Department of Molecular Oncology, BC Cancer Research Centre, CanadaDepartment of Pathology and Laboratory Medicine, University of British Columbia, Canada; Department of Molecular Oncology, BC Cancer Research Centre, CanadaLunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Canada; Department of Molecular Genetics, University of Toronto, Canada; Department of Statistical Sciences, University of Toronto, CanadaComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USADepartment of Pathology and Laboratory Medicine, University of British Columbia, Canada; Department of Molecular Oncology, BC Cancer Research Centre, CanadaDepartment of Computer Science, University of British Columbia, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Canada; Department of Molecular Oncology, BC Cancer Research Centre, CanadaComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, USADepartment of Statistics, University of British Columbia, CanadaA new generation of scalable single cell whole genome sequencing (scWGS) methods allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cell populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing the mutational processes that gave rise to them. Existing phylogenetic tree building models do not scale to the tens of thousands of high resolution genomes achievable with current scWGS methods. We constructed a phylogenetic model and associated Bayesian inference procedure, sitka, specifically for scWGS data. The method is based on a novel phylogenetic encoding of copy number (CN) data, the sitka transformation, that simplifies the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. We demonstrate our method on three single cell datasets, including a novel PDX series, and analyse the topological properties of the inferred trees. Sitka is freely available at https://github.com/UBC-Stat-ML/sitkatree.git https://peercommunityjournal.org/articles/10.24072/pcjournal.292/Phylogenetics, Cancer evolution, Bayesian statistics, MCMC, Copy number evolution, PDX, Triple negative breast cancer |
| spellingShingle | Salehi, Sohrab Dorri, Fatemeh Chern, Kevin Kabeer, Farhia Rusk, Nicole Funnell, Tyler Williams, Marc J. Lai, Daniel Andronescu, Mirela Campbell, Kieran R. McPherson, Andrew Aparicio, Samuel Roth, Andrew Shah, Sohrab P. Bouchard-Côté, Alexandre Cancer phylogenetic tree inference at scale from 1000s of single cell genomes Peer Community Journal Phylogenetics, Cancer evolution, Bayesian statistics, MCMC, Copy number evolution, PDX, Triple negative breast cancer |
| title | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
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| title_full | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
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| title_fullStr | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
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| title_full_unstemmed | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
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| title_short | Cancer phylogenetic tree inference at scale from 1000s of single cell genomes
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| title_sort | cancer phylogenetic tree inference at scale from 1000s of single cell genomes |
| topic | Phylogenetics, Cancer evolution, Bayesian statistics, MCMC, Copy number evolution, PDX, Triple negative breast cancer |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.292/ |
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