Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kaminska B, Huelsken J, Omberg L, Gevaert O, Colaprico A, Czerwinska P, Mazurek S, Mishra L, Heyn H, Krasnitz A, Godwin AK, Lazar AJ, Stuart JM, Hoadley KA, Laird PW, Noushmehr H, Wiznerowicz M (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Book Volume: 173

Pages Range: 338-354.e15

Journal Issue: 2

DOI: 10.1016/j.cell.2018.03.034

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.

Involved external institutions

Henry Ford Health System (HFHS) United States (USA) (US) Harvard University United States (USA) (US) Stanford University United States (USA) (US) Hasselt University / Universiteit Hasselt Belgium (BE) University of Texas MD Anderson Cancer Center United States (USA) (US) Nencki Institute of Experimental Biology / Instytut Biologii Doświadczalnej im. Marcelego Nenckiego Poland (PL) École Polytechnique Fédérale de Lausanne (EPFL) Switzerland (CH) Sage Bionetworks United States (USA) (US) Université libre de Bruxelles (ULB) / Free University of Brussels Belgium (BE) Poznan University of Medical Sciences / Uniwersytet Medyczny im. Karola Marcinkowskiego w Poznaniu Poland (PL) George Washington University (GWU) United States (USA) (US) Centre for Genomic Regulation (CRG) Spain (ES) Cold Spring Harbor Laboratory United States (USA) (US) University of Kansas (KU) United States (USA) (US) University of California Santa Cruz United States (USA) (US) University of North Carolina at Chapel Hill United States (USA) (US) Van Andel Institute United States (USA) (US)

How to cite

APA:

Malta, T.M., Sokolov, A., Gentles, A.J., Burzykowski, T., Poisson, L., Weinstein, J.N.,... Wiznerowicz, M. (2018). Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell, 173(2), 338-354.e15. https://doi.org/10.1016/j.cell.2018.03.034

MLA:

Malta, Tathiane M., et al. "Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation." Cell 173.2 (2018): 338-354.e15.

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