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.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.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.doi http://dx.doi.org/10.1016/j.cell.2018.03.034
• dc.identifier.issn 0092-8674
• dc.identifier.uri http://hdl.handle.net/10230/42586
• 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.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.title Machine learning identifies stemness features associated with oncogenic dedifferentiation
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion