Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

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
Cell. 2018 173 (2): 338-354.e15

PMID: 29625051 · PMCID: PMC5902191 · DOI:10.1016/j.cell.2018.03.034

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.

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

MeSH Terms (13)

Carcinogenesis Cell Dedifferentiation Databases, Genetic DNA Methylation Epigenesis, Genetic Humans Machine Learning MicroRNAs Neoplasm Metastasis Neoplasms Stem Cells Transcriptome Tumor Microenvironment

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