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Machine learning identifies stemness features associated with oncogenic dedifferentiation

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dc.contributor.author Malta, Tathiane M.
dc.contributor.author Heyn, Holger
dc.contributor.author Wiznerowicz, Maciej
dc.date.accessioned 2019-11-04T08:47:38Z
dc.date.available 2019-11-04T08:47:38Z
dc.date.issued 2018
dc.identifier.citation Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018; 173(2):338-354.e15. DOI 10.1016/j.cell.2018.03.034
dc.identifier.issn 0092-8674
dc.identifier.uri http://hdl.handle.net/10230/42586
dc.description.abstract Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Cell. 2018; 173(2):338-354.e15
dc.rights © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title Machine learning identifies stemness features associated with oncogenic dedifferentiation
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.cell.2018.03.034
dc.subject.keyword The Cancer Genome Atlas
dc.subject.keyword Cancer stem cells
dc.subject.keyword Dedifferentiation
dc.subject.keyword Epigenomic
dc.subject.keyword Genomic
dc.subject.keyword Machine learning
dc.subject.keyword Pan-cancer
dc.subject.keyword Stemness
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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