{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T11:41:55Z","timestamp":1767181315188,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010732","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000}}],"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["MSCA-ITN-2017-766030"],"award-info":[{"award-number":["MSCA-ITN-2017-766030"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Swedish Foundation for Strategic Research","award":["BD15-0043"],"award-info":[{"award-number":["BD15-0043"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method\u2019s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010732","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T13:33:40Z","timestamp":1670247220000},"page":"e1010732","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["ToMExO: A probabilistic tree-structured model for cancer progression"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0782-8308","authenticated-orcid":true,"given":"Mohammadreza","family":"Mohaghegh Neyshabouri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4552-0240","authenticated-orcid":true,"given":"Jens","family":"Lagergren","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"issue":"1","key":"pcbi.1010732.ref001","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1093\/sysbio\/syu081","article-title":"Cancer evolution: mathematical models and computational inference","volume":"64","author":"N Beerenwinkel","year":"2015","journal-title":"Systematic biology"},{"issue":"6","key":"pcbi.1010732.ref002","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1089\/cmb.2016.0171","article-title":"pathTiMEx: joint inference of mutually exclusive cancer pathways and their progression dynamics","volume":"24","author":"S Cristea","year":"2017","journal-title":"Journal of Computational Biology"},{"issue":"1","key":"pcbi.1010732.ref003","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1089\/cmb.1999.6.37","article-title":"Inferring tree models for oncogenesis from comparative genome hybridization data","volume":"6","author":"R Desper","year":"1999","journal-title":"Journal of computational biology"},{"issue":"6","key":"pcbi.1010732.ref004","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1089\/10665270050514936","article-title":"Distance-based reconstruction of tree models for oncogenesis","volume":"7","author":"R Desper","year":"2000","journal-title":"Journal of Computational Biology"},{"issue":"4","key":"pcbi.1010732.ref005","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1093\/biostatistics\/kxh007","article-title":"Maximum likelihood estimation of oncogenetic tree models","volume":"5","author":"Av Heydebreck","year":"2004","journal-title":"Biostatistics"},{"issue":"2","key":"pcbi.1010732.ref006","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0025-5564(02)00086-X","article-title":"Estimating an oncogenetic tree when false negatives and positives are present","volume":"176","author":"A Szabo","year":"2002","journal-title":"Mathematical biosciences"},{"issue":"9","key":"pcbi.1010732.ref007","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1093\/bioinformatics\/bti274","article-title":"Mtreemix: a software package for learning and using mixture models of mutagenetic trees","volume":"21","author":"N Beerenwinkel","year":"2005","journal-title":"Bioinformatics"},{"issue":"10","key":"pcbi.1010732.ref008","doi-asserted-by":"crossref","first-page":"e108358","DOI":"10.1371\/journal.pone.0108358","article-title":"Inferring tree causal models of cancer progression with probability raising","volume":"9","author":"LO Loohuis","year":"2014","journal-title":"PloS one"},{"key":"pcbi.1010732.ref009","unstructured":"Tofigh A, Sjolund E, Hoglund M, Lagergren J. A global structural EM algorithm for a model of cancer progression. In: Proceedings of the 24th International Conference on Neural Information Processing Systems; 2011. p. 163\u2013171."},{"issue":"21","key":"pcbi.1010732.ref010","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1093\/bioinformatics\/btp505","article-title":"Quantifying cancer progression with conjunctive Bayesian networks","volume":"25","author":"M Gerstung","year":"2009","journal-title":"Bioinformatics"},{"issue":"4","key":"pcbi.1010732.ref011","doi-asserted-by":"crossref","first-page":"893","DOI":"10.3150\/07-BEJ6133","article-title":"Conjunctive bayesian networks","volume":"13","author":"N Beerenwinkel","year":"2007","journal-title":"Bernoulli"},{"issue":"3","key":"pcbi.1010732.ref012","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1093\/biomet\/asp023","article-title":"Markov models for accumulating mutations","volume":"96","author":"N Beerenwinkel","year":"2009","journal-title":"Biometrika"},{"issue":"18","key":"pcbi.1010732.ref013","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1093\/bioinformatics\/bts433","article-title":"Efficient sampling for Bayesian inference of conjunctive Bayesian networks","volume":"28","author":"T Sakoparnig","year":"2012","journal-title":"Bioinformatics"},{"issue":"7","key":"pcbi.1010732.ref014","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1093\/bioinformatics\/btv400","article-title":"TiMEx: a waiting time model for mutually exclusive cancer alterations","volume":"32","author":"S Constantinescu","year":"2016","journal-title":"Bioinformatics"},{"issue":"5","key":"pcbi.1010732.ref015","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.cell.2014.07.027","article-title":"Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality","volume":"158","author":"L Jerby-Arnon","year":"2014","journal-title":"Cell"},{"issue":"5","key":"pcbi.1010732.ref016","doi-asserted-by":"crossref","first-page":"e1003054","DOI":"10.1371\/journal.pcbi.1003054","article-title":"Simultaneous identification of multiple driver pathways in cancer","volume":"9","author":"MD Leiserson","year":"2013","journal-title":"PLoS computational biology"},{"issue":"1","key":"pcbi.1010732.ref017","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-015-0700-7","article-title":"CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer","volume":"16","author":"MD Leiserson","year":"2015","journal-title":"Genome biology"},{"issue":"3","key":"pcbi.1010732.ref018","doi-asserted-by":"crossref","first-page":"e1003503","DOI":"10.1371\/journal.pcbi.1003503","article-title":"Modeling mutual exclusivity of cancer mutations","volume":"10","author":"E Szczurek","year":"2014","journal-title":"PLoS computational biology"},{"issue":"6","key":"pcbi.1010732.ref019","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1089\/cmb.2014.0161","article-title":"Simultaneous inference of cancer pathways and tumor progression from cross-sectional mutation data","volume":"22","author":"BJ Raphael","year":"2015","journal-title":"Journal of Computational Biology"},{"issue":"10","key":"pcbi.1010732.ref020","doi-asserted-by":"crossref","first-page":"e1008183","DOI":"10.1371\/journal.pcbi.1008183","article-title":"Inferring tumor progression in large datasets","volume":"16","author":"M Mohaghegh Neyshabouri","year":"2020","journal-title":"PLoS computational biology"},{"issue":"1","key":"pcbi.1010732.ref021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"AP Dempster","year":"1977","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"4","key":"pcbi.1010732.ref022","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1089\/cmb.2006.13.853","article-title":"New probabilistic network models and algorithms for oncogenesis","volume":"13","author":"M Hjelm","year":"2006","journal-title":"Journal of Computational Biology"},{"issue":"1","key":"pcbi.1010732.ref023","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1093\/bioinformatics\/btz513","article-title":"Modelling cancer progression using mutual hazard networks","volume":"36","author":"R Schill","year":"2020","journal-title":"Bioinformatics"},{"issue":"7","key":"pcbi.1010732.ref024","doi-asserted-by":"crossref","first-page":"2090","DOI":"10.1093\/bioinformatics\/btz869","article-title":"Distance measures for tumor evolutionary trees","volume":"36","author":"Z DiNardo","year":"2020","journal-title":"Bioinformatics"},{"issue":"5","key":"pcbi.1010732.ref025","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data","volume":"2","author":"E Cerami","year":"2012","journal-title":"Cancer discovery"},{"issue":"18","key":"pcbi.1010732.ref026","doi-asserted-by":"crossref","first-page":"3016","DOI":"10.1093\/bioinformatics\/btv296","article-title":"CAPRI: efficient inference of cancer progression models from cross-sectional data","volume":"31","author":"D Ramazzotti","year":"2015","journal-title":"Bioinformatics"},{"issue":"10","key":"pcbi.1010732.ref027","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41568-020-0290-x","article-title":"A compendium of mutational cancer driver genes","volume":"20","author":"F Mart\u00ednez-Jim\u00e9nez","year":"2020","journal-title":"Nature Reviews Cancer"},{"issue":"4","key":"pcbi.1010732.ref028","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/biom9040153","article-title":"The PTEN\u2013PI3K axis in cancer","volume":"9","author":"A Papa","year":"2019","journal-title":"Biomolecules"},{"issue":"1","key":"pcbi.1010732.ref029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-09659-z","article-title":"A requirement for STAG2 in replication fork progression creates a targetable synthetic lethality in cohesin-mutant cancers","volume":"10","author":"G Mondal","year":"2019","journal-title":"Nature communications"},{"issue":"4","key":"pcbi.1010732.ref030","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1093\/brain\/awz044","article-title":"The landscape of the mesenchymal signature in brain tumours","volume":"142","author":"J Behnan","year":"2019","journal-title":"Brain"},{"issue":"5","key":"pcbi.1010732.ref031","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.ccr.2010.03.017","article-title":"Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma","volume":"17","author":"H Noushmehr","year":"2010","journal-title":"Cancer cell"},{"issue":"1","key":"pcbi.1010732.ref032","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.ccr.2009.12.020","article-title":"Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1","volume":"17","author":"RG Verhaak","year":"2010","journal-title":"Cancer cell"},{"issue":"4","key":"pcbi.1010732.ref033","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ccr.2012.08.024","article-title":"Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma","volume":"22","author":"D Sturm","year":"2012","journal-title":"Cancer cell"},{"issue":"7793","key":"pcbi.1010732.ref034","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41586-019-1907-7","article-title":"The evolutionary history of 2,658 cancers","volume":"578","author":"M Gerstung","year":"2020","journal-title":"Nature"},{"issue":"3","key":"pcbi.1010732.ref035","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ccr.2011.01.039","article-title":"Cooperativity within and among Pten, p53, and Rb pathways induces high-grade astrocytoma in adult brain","volume":"19","author":"LM Chow","year":"2011","journal-title":"Cancer cell"},{"issue":"4","key":"pcbi.1010732.ref036","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1101\/gad.293449.116","article-title":"Oncogenic Kras drives invasion and maintains metastases in colorectal cancer","volume":"31","author":"AT Boutin","year":"2017","journal-title":"Genes & development"},{"issue":"4","key":"pcbi.1010732.ref037","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1093\/jmcb\/mjy075","article-title":"Mutant p53 in colon cancer","volume":"11","author":"M Nakayama","year":"2019","journal-title":"Journal of molecular cell biology"},{"issue":"5","key":"pcbi.1010732.ref038","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/0092-8674(90)90186-I","article-title":"A genetic model for colorectal tumorigenesis","volume":"61","author":"ER Fearon","year":"1990","journal-title":"cell"},{"issue":"5","key":"pcbi.1010732.ref039","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3390\/cancers9050042","article-title":"KRAS, TP53, CDKN2A, SMAD4, BRCA1, and BRCA2 mutations in pancreatic cancer","volume":"9","author":"J Cicenas","year":"2017","journal-title":"Cancers"},{"issue":"1","key":"pcbi.1010732.ref040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tranon.2015.11.007","article-title":"Prognostic value of SMAD4 in pancreatic cancer: a meta-analysis","volume":"9","author":"X Shugang","year":"2016","journal-title":"Translational oncology"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010732","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010732","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T19:16:09Z","timestamp":1728501369000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010732"}},"subtitle":[],"editor":[{"given":"Teresa M.","family":"Przytycka","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010732","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]}}}