{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:59Z","timestamp":1772138039338,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Oxford Biomedical Research Centre Programme"},{"name":"Wellcome Trust Core Award","award":["203141\/Z\/16\/Z"],"award-info":[{"award-number":["203141\/Z\/16\/Z"]}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so?<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian Process priors and using as input a sequence of several sparsely collected samples, our method can reconstruct the temporal profile of the abundance of any mutation cluster supported by the data as a continuous function of time. We benchmarked our method on whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data and we found that incorporating information on the timing of tissue collection improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. Thus, our approach is particularly useful when collecting a relatively long sequence of tumour samples is feasible, as in liquid cancers (e.g. leukaemia) and liquid biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The statistical methodology presented in this paper is freely available at github.com\/dvav\/clonosGP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa672","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T15:24:53Z","timestamp":1595431493000},"page":"147-154","source":"Crossref","is-referenced-by-count":6,"title":["A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3984-1507","authenticated-orcid":false,"given":"Dimitrios V","family":"Vavoulis","sequence":"first","affiliation":[{"name":"Department of Oncology, University of Oxford , Oxford, OX3 7DQ, UK"},{"name":"Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford , Oxford, OX3 7BN, UK"},{"name":"NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust , Oxford, OX3 9DU, UK"},{"name":"Department of Oncology, Molecular Diagnostic Centre, University of Oxford , Oxford OX3 9DU, UK"}]},{"given":"Anthony","family":"Cutts","sequence":"additional","affiliation":[{"name":"Department of Oncology, University of Oxford , Oxford, OX3 7DQ, UK"},{"name":"Department of Oncology, Molecular Diagnostic Centre, University of Oxford , Oxford OX3 9DU, UK"}]},{"given":"Jenny C","family":"Taylor","sequence":"additional","affiliation":[{"name":"Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford , Oxford, OX3 7BN, UK"},{"name":"NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust , Oxford, OX3 9DU, UK"}]},{"given":"Anna","family":"Schuh","sequence":"additional","affiliation":[{"name":"Department of Oncology, University of Oxford , Oxford, OX3 7DQ, UK"},{"name":"NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust , Oxford, OX3 9DU, UK"},{"name":"Department of Oncology, Molecular Diagnostic Centre, University of Oxford , Oxford OX3 9DU, UK"},{"name":"Department of Haematology, Oxford University Hospitals NHS Trust , Oxford OX3 9DU, UK"}]}],"member":"286","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"2023051511003792900_btaa672-B1","doi-asserted-by":"publisher","author":"Ab\u00e9cassis","year":"2019","DOI":"10.1101\/825778"},{"key":"2023051511003792900_btaa672-B2","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1561\/2200000036","article-title":"Kernels for Vector-Valued functions: a review","volume":"4","author":"\u00c1lvarez","year":"2012","journal-title":"Found. Trends Mach. Learn"},{"key":"2023051511003792900_btaa672-B3","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1093\/sysbio\/syu081","article-title":"Cancer evolution: mathematical models and computational inference","volume":"64","author":"Beerenwinkel","year":"2015","journal-title":"Syst. Biol"},{"key":"2023051511003792900_btaa672-B4","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: a review for statisticians","volume":"112","author":"Blei","year":"2017","journal-title":"J. Am. Stat. Assoc"},{"key":"2023051511003792900_btaa672-B5","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.1038\/ncomms3997","article-title":"Heterogeneity of genomic evolution and mutational profiles in multiple myeloma","volume":"5","author":"Bolli","year":"2014","journal-title":"Nat. Commun"},{"key":"2023051511003792900_btaa672-B6","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa599","article-title":"BnpC: Bayesian non-parametric clustering of single-cell mutation profiles","author":"Borgsm\u00fcller","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051511003792900_btaa672-B7","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1038\/nbt.2203","article-title":"Absolute quantification of somatic DNA alterations in human cancer","volume":"30","author":"Carter","year":"2012","journal-title":"Nat. Biotechnol"},{"key":"2023051511003792900_btaa672-B8","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.1093\/bioinformatics\/btaa172","article-title":"RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051511003792900_btaa672-B9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41525-017-0030-7","article-title":"Characterisation of the changing genomic landscape of metastatic melanoma using cell free DNA","volume":"2","author":"Cutts","year":"2017","journal-title":"NPJ Genomic Med"},{"key":"2023051511003792900_btaa672-B10","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.1093\/annonc\/mdx517","article-title":"ClonEvol: clonal ordering and visualization in cancer sequencing","volume":"28","author":"Dang","year":"2017","journal-title":"Ann. Oncol"},{"key":"2023051511003792900_btaa672-B11","doi-asserted-by":"crossref","first-page":"a026625","DOI":"10.1101\/cshperspect.a026625","article-title":"Principles of reconstructing the subclonal architecture of cancers","volume":"7","author":"Dentro","year":"2017","journal-title":"Cold Spring Harb. Perspect. Med"},{"key":"2023051511003792900_btaa672-B12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13059-015-0602-8","article-title":"PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors","volume":"16","author":"Deshwar","year":"2015","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B13","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1089\/cmb.2016.0148","article-title":"Clonality inference from single tumor samples using low-coverage sequence data","volume":"24","author":"Donmez","year":"2017","journal-title":"J. Comput. Biol"},{"key":"2023051511003792900_btaa672-B14","doi-asserted-by":"crossref","first-page":"i671","DOI":"10.1093\/bioinformatics\/bty589","article-title":"SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error","volume":"34","author":"El-Kebir","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051511003792900_btaa672-B15","doi-asserted-by":"crossref","first-page":"i62","DOI":"10.1093\/bioinformatics\/btv261","article-title":"Reconstruction of clonal trees and tumor composition from multi-sample sequencing data","volume":"31","author":"El-Kebir","year":"2015","journal-title":"Bioinformatics"},{"key":"2023051511003792900_btaa672-B16","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1016\/j.celrep.2014.04.055","article-title":"High-definition reconstruction of clonal composition in cancer","volume":"7","author":"Fischer","year":"2014","journal-title":"Cell Rep"},{"key":"2023051511003792900_btaa672-B17","author":"Gelman","year":"2013"},{"key":"2023051511003792900_btaa672-B18","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1038\/s41598-018-37389-7","article-title":"Clonal dynamics monitoring during clinical evolution in chronic lymphocytic leukaemia","volume":"9","author":"Gonz\u00e1lez-Rinc\u00f3n","year":"2019","journal-title":"Sci. Rep"},{"key":"2023051511003792900_btaa672-B19","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1101\/gr.180281.114","article-title":"TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data","volume":"24","author":"Ha","year":"2014","journal-title":"Genome Res"},{"key":"2023051511003792900_btaa672-B20","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1198\/016214501750332758","article-title":"Gibbs sampling methods for stick-breaking priors","volume":"96","author":"Ishwaran","year":"2001","journal-title":"J. Am. Stat. Assoc"},{"key":"2023051511003792900_btaa672-B21","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s40484-019-0188-3","article-title":"Algorithmic approaches to clonal reconstruction in heterogeneous cell populations","volume":"7","author":"Ismail","year":"2019","journal-title":"Quant. Biol"},{"key":"2023051511003792900_btaa672-B22","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1186\/s13059-016-0936-x","article-title":"Tree inference for single-cell data","volume":"17","author":"Jahn","year":"2016","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B23","doi-asserted-by":"crossref","first-page":"E5528","DOI":"10.1073\/pnas.1522203113","article-title":"Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing","volume":"113","author":"Jiang","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051511003792900_btaa672-B24","first-page":"1","article-title":"Automatic differentiation variational inference","volume":"18","author":"Kucukelbir","year":"2017","journal-title":"J. Mach. Learn. Res"},{"key":"2023051511003792900_btaa672-B25","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1101\/gr.234435.118","article-title":"PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data","volume":"29","author":"Malikic","year":"2019","journal-title":"Genome Res"},{"key":"2023051511003792900_btaa672-B26","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1214\/16-AOAS986","article-title":"A phylogenetic latent feature model for clonal deconvolution","volume":"10","author":"Marass","year":"2016","journal-title":"Ann. Appl. Stat"},{"key":"2023051511003792900_btaa672-B27","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/nrc2013","article-title":"Cancer as an evolutionary and ecological process","volume":"6","author":"Merlo","year":"2006","journal-title":"Nat. Rev. Cancer"},{"key":"2023051511003792900_btaa672-B28","doi-asserted-by":"crossref","first-page":"e1003665","DOI":"10.1371\/journal.pcbi.1003665","article-title":"SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution","volume":"10","author":"Miller","year":"2014","journal-title":"PLoS Comput. Biol"},{"key":"2023051511003792900_btaa672-B29","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.cels.2019.05.010","article-title":"CALDER: inferring phylogenetic trees from longitudinal tumor samples","volume":"8","author":"Myers","year":"2019","journal-title":"Cell Syst"},{"key":"2023051511003792900_btaa672-B30","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1016\/j.cell.2012.04.023","article-title":"The life history of 21 breast cancers","volume":"149","author":"Nik-Zainal","year":"2012","journal-title":"Cell"},{"key":"2023051511003792900_btaa672-B31","doi-asserted-by":"crossref","first-page":"e1004416","DOI":"10.1371\/journal.pcbi.1004416","article-title":"SubClonal hierarchy inference from somatic mutations: automatic reconstruction of cancer evolutionary trees from multi-region next generation sequencing","volume":"11","author":"Niknafs","year":"2015","journal-title":"PLoS Comput. Biol"},{"key":"2023051511003792900_btaa672-B32","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1126\/science.959840","article-title":"The clonal evolution of tumor cell populations","volume":"194","author":"Nowell","year":"1976","journal-title":"Science"},{"key":"2023051511003792900_btaa672-B33","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res"},{"key":"2023051511003792900_btaa672-B34","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1186\/s13059-014-0443-x","article-title":"SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization","volume":"15","author":"Qiao","year":"2014","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B35","doi-asserted-by":"publisher","author":"Ramazzotti","year":"2020","DOI":"10.1101\/2020.01.14.906453"},{"key":"2023051511003792900_btaa672-B36","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen","year":"2006"},{"key":"2023051511003792900_btaa672-B37","doi-asserted-by":"crossref","first-page":"20110550","DOI":"10.1098\/rsta.2011.0550","article-title":"Gaussian processes for time-series modelling","volume":"371","author":"Roberts","year":"2013","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci"},{"key":"2023051511003792900_btaa672-B38","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13059-016-0929-9","article-title":"OncoNEM: inferring tumor evolution from single-cell sequencing data","volume":"17","author":"Ross","year":"2016","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B39","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1038\/nmeth.2883","article-title":"PyClone: statistical inference of clonal population structure in cancer","volume":"11","author":"Roth","year":"2014","journal-title":"Nat. Methods"},{"key":"2023051511003792900_btaa672-B40","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/nmeth.3867","article-title":"Clonal genotype and population structure inference from single-cell tumor sequencing","volume":"13","author":"Roth","year":"2016","journal-title":"Nat. Methods"},{"key":"2023051511003792900_btaa672-B41","author":"Rubanova","year":"2020"},{"key":"2023051511003792900_btaa672-B42","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s41587-019-0364-z","article-title":"A community effort to create standards for evaluating tumor subclonal reconstruction","volume":"38","author":"Salcedo","year":"2020","journal-title":"Nat. Biotechnol"},{"key":"2023051511003792900_btaa672-B43","doi-asserted-by":"crossref","first-page":"e55","DOI":"10.7717\/peerj-cs.55","article-title":"Probabilistic programming in python using PyMC3","volume":"2","author":"Salvatier","year":"2016","journal-title":"PeerJ Comput. Sci"},{"key":"2023051511003792900_btaa672-B44","doi-asserted-by":"crossref","first-page":"4191","DOI":"10.1182\/blood-2012-05-433540","article-title":"Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns","volume":"120","author":"Schuh","year":"2012","journal-title":"Blood"},{"key":"2023051511003792900_btaa672-B45","first-page":"467","author":"Sengupta","year":"2014"},{"key":"2023051511003792900_btaa672-B46","year":"2020"},{"key":"2023051511003792900_btaa672-B47","doi-asserted-by":"crossref","first-page":"16910","DOI":"10.1073\/pnas.1009843107","article-title":"Allele-specific copy number analysis of tumors","volume":"107","author":"Van Loo","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051511003792900_btaa672-B48","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-1-0716-0026-9_9","article-title":"Exploring Bayesian approaches to eQTL mapping through probabilistic programming","volume":"2082","author":"Vavoulis","year":"2020","journal-title":"Methods Mol. Biol"},{"key":"2023051511003792900_btaa672-B49","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13059-015-0604-6","article-title":"DGEclust: differential expression analysis of clustered count data","volume":"16","author":"Vavoulis","year":"2015","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B50","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.1093\/bioinformatics\/btx355","article-title":"Hierarchical probabilistic models for multiple gene\/variant associations based on next-generation sequencing data","volume":"33","author":"Vavoulis","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051511003792900_btaa672-B51","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s13059-015-0592-6","article-title":"BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies","volume":"16","author":"Yuan","year":"2015","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B52","doi-asserted-by":"publisher","author":"Yuan","year":"2018","DOI":"10.1101\/484402"},{"key":"2023051511003792900_btaa672-B53","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1186\/s13059-017-1311-2","article-title":"SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models","volume":"18","author":"Zafar","year":"2017","journal-title":"Genome Biol"},{"key":"2023051511003792900_btaa672-B54","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1101\/gr.243121.118","article-title":"SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data","volume":"29","author":"Zafar","year":"2019","journal-title":"Genome Res"},{"key":"2023051511003792900_btaa672-B55","doi-asserted-by":"crossref","first-page":"e1003703","DOI":"10.1371\/journal.pcbi.1003703","article-title":"Inferring clonal composition from multiple sections of a breast cancer","volume":"10","author":"Zare","year":"2014","journal-title":"PLoS Comput. Biol"},{"key":"2023051511003792900_btaa672-B56","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/TPAMI.2018.2889774","article-title":"Advances in variational inference","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023051511003792900_btaa672-B57","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1093\/bioinformatics\/btz057","article-title":"Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing","volume":"35","author":"Zucker","year":"2019","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa672\/35069166\/btaa672.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/2\/147\/50321745\/btaa672.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/2\/147\/50321745\/btaa672.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T07:01:44Z","timestamp":1684134104000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/2\/147\/5877426"}},"subtitle":[],"editor":[{"given":"Peter","family":"Robinson","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,7,28]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,4,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa672","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.01.20.913236","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,1,15]]},"published":{"date-parts":[[2020,7,28]]}}}