Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Journal: Cell
PMID: 29625051

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.

Authors

  • Tathiane M Malta
    Henry Ford Health System, Detroit, MI 48202, USA; University of São Paulo, Ribeirão Preto-SP 14049, Brazil.
  • Artem Sokolov
    Harvard Medical School, Boston, MA 02115, USA.
  • Andrew J Gentles
    Stanford University, Palo Alto, CA 94305, USA.
  • Tomasz Burzykowski
    Hasselt University, 3590 Diepenbeek, Belgium.
  • Laila Poisson
    Henry Ford Health System, Detroit, MI 48202, USA.
  • John N Weinstein
    Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Bożena Kamińska
    Nencki Institute of Experimental Biology of PAS, 02093 Warsaw, Poland.
  • Joerg Huelsken
    Swiss Federal Institute of Technology Lausanne (EPFL), CH-1015 Lausanne; Switzerland.
  • Larsson Omberg
    Sage Bionetworks, Seattle, WA 98109 USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.
  • Antonio Colaprico
    Université Libre de Bruxelles, 1050 Bruxelles, Belgium; Interuniversity Institute of Bioinformatics in Brussels (IB)(2), 1050 Bruxelles; Belgium.
  • Patrycja Czerwińska
    Poznań University of Medical Sciences, 61701 Poznań, Poland.
  • Sylwia Mazurek
    Poznań University of Medical Sciences, 61701 Poznań, Poland; Postgraduate School of Molecular Medicine, Medical University of Warsaw, 02109 Warsaw, Poland.
  • Lopa Mishra
    Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
  • Holger Heyn
    Centre for Genomic Regulation (CNAG-CRG), 08003 Barcelona, Spain.
  • Alex Krasnitz
    Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
  • Andrew K Godwin
    University of Kansas Medical Center, Kansas City, KS 66160, USA.
  • Alexander J Lazar
    The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Joshua M Stuart
    University of California, Santa Cruz, Santa Cruz, CA 95064, USA.
  • Katherine A Hoadley
    University of North Carolina, Chapel Hill, NC 27599, USA.
  • Peter W Laird
    Van Andel Research Institute, Grand Rapids, MI 49503, USA.
  • Houtan Noushmehr
    Henry Ford Health System, Detroit, MI 48202, USA; University of São Paulo, Ribeirão Preto-SP 14049, Brazil. Electronic address: hnoushm1@hfhs.org.
  • Maciej Wiznerowicz
    Poznań University of Medical Sciences, 61701 Poznań, Poland; Greater Poland Cancer Center, 61866 Poznań, Poland; International Institute for Molecular Oncology, 60203 Poznań, Poland. Electronic address: maciej.wiznerowicz@iimo.pl.

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