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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Publication ,  Journal Article
Malta, TM; Sokolov, A; Gentles, AJ; Burzykowski, T; Poisson, L; Weinstein, JN; Kamińska, B; Huelsken, J; Omberg, L; Gevaert, O; Colaprico, A ...
Published in: Cell
April 5, 2018

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.

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Published In

Cell

DOI

EISSN

1097-4172

Publication Date

April 5, 2018

Volume

173

Issue

2

Start / End Page

338 / 354.e15

Location

United States

Related Subject Headings

  • Tumor Microenvironment
  • Transcriptome
  • Stem Cells
  • Neoplasms
  • Neoplasm Metastasis
  • MicroRNAs
  • Machine Learning
  • Humans
  • Epigenesis, Genetic
  • Developmental Biology
 

Citation

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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
Malta, Tathiane M., Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John N. Weinstein, Bożena Kamińska, et al. “Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.Cell 173, no. 2 (April 5, 2018): 338-354.e15. https://doi.org/10.1016/j.cell.2018.03.034.
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 Apr 5;173(2):338-354.e15.
Malta, Tathiane M., et al. “Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.Cell, vol. 173, no. 2, Apr. 2018, pp. 338-354.e15. Pubmed, doi:10.1016/j.cell.2018.03.034.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kamińska B, Huelsken J, Omberg L, Gevaert O, Colaprico A, Czerwińska P, Mazurek S, Mishra L, Heyn H, Krasnitz A, Godwin AK, Lazar AJ, Cancer Genome Atlas Research Network, Stuart JM, Hoadley KA, Laird PW, Noushmehr H, Wiznerowicz M. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018 Apr 5;173(2):338-354.e15.

Published In

Cell

DOI

EISSN

1097-4172

Publication Date

April 5, 2018

Volume

173

Issue

2

Start / End Page

338 / 354.e15

Location

United States

Related Subject Headings

  • Tumor Microenvironment
  • Transcriptome
  • Stem Cells
  • Neoplasms
  • Neoplasm Metastasis
  • MicroRNAs
  • Machine Learning
  • Humans
  • Epigenesis, Genetic
  • Developmental Biology