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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 | Source: | 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 | Document URI: | http://hdl.handle.net/1942/26598 | ISSN: | 0092-8674 | e-ISSN: | 1097-4172 | DOI: | 10.1016/j.cell.2018.03.034 | ISI #: | 000429320200010 | Rights: | ª 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/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
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malta 1.pdf | Published version | 9.02 MB | Adobe PDF | View/Open |
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