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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26598

Title: Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Authors: Malta, Tathiane M.
Sokolov, Artem
Gentles, Andrew J.
Burzykowski, Tomasz
Poisson, Laila
Weinstein, John N.
Kaminska, Bozena
Huelsken, Joerg
Omberg, Larsson
Gevaert, Olivier
Colaprico, Antonio
Czerwinska, Patrycja
Mazurek, Sylwia
Mishra, Lopa
Heyn, Holger
Krasnitz, Alex
Godwin, Andrew K.
Lazar, Alexander J.
Stuart, Joshua M.
Hoadley, Katherine A.
Laird, Peter W.
Noushmehr, Houtan
Wiznerowicz, Maciej
Issue Date: 2018
Citation: CELL, 173(2), p. 338-354
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.
Notes: Noushmehr, H (reprint author), Henry Ford Hlth Syst, Detroit, MI 48202 USA. hnoushm1@hfhs.org; maciej.wiznerowicz@iimo.pl
URI: http://hdl.handle.net/1942/26598
DOI: 10.1016/j.cell.2018.03.034
ISI #: 000429320200010
ISSN: 0092-8674
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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