{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T22:41:48Z","timestamp":1768171308926,"version":"3.49.0"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"ERC Synergy","award":["609883"],"award-info":[{"award-number":["609883"]}]},{"name":"SystemsX.ch RTD","award":["2013\/150"],"award-info":[{"award-number":["2013\/150"]}]},{"DOI":"10.13039\/501100000268","name":"BBSRC","doi-asserted-by":"publisher","award":["BB\/R006563\/1"],"award-info":[{"award-number":["BB\/R006563\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["C14303\/A17197"],"award-info":[{"award-number":["C14303\/A17197"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from &amp;gt;200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/cbg-ethz\/predictability\\_of\\_cancer\\_evolution<\/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\/btz332","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T03:18:35Z","timestamp":1557976715000},"page":"i389-i397","source":"Crossref","is-referenced-by-count":35,"title":["Estimating the predictability of cancer evolution"],"prefix":"10.1093","volume":"35","author":[{"given":"Sayed-Rzgar","family":"Hosseini","sequence":"first","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Z\u00fcrich, Basel, Switzerland"},{"name":"Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK"}]},{"given":"Ramon","family":"Diaz-Uriarte","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Universidad Aut\u00f3noma de Madrid, Instituto de Investigaciones Biom\u00e9dicas \u201cAlberto Sols (UAM-CSIC)\u201d, Madrid, Spain"}]},{"given":"Florian","family":"Markowetz","sequence":"additional","affiliation":[{"name":"Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK"}]},{"given":"Niko","family":"Beerenwinkel","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Z\u00fcrich, Basel, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2019,7,5]]},"reference":[{"key":"2023062712324796300_btz332-B1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/978-94-007-7347-9_11","article-title":"The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing","volume":"781","author":"Achaz","year":"2014","journal-title":"Adv. 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