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; Kamińska, Bożena; Huelsken, Joerg; Omberg, Larsson; Gevaert, Olivier; Colaprico, Antonio; Czerwińska, Patrycja; Mazurek, Sylwia; Mishra, Lopa; Heyn, Holger; Krasnitz, Alex; Godwin, Andrew K; Lazar, Alexander J; Cancer Genome Atlas Research Network; Stuart, Joshua M; Hoadley, Katherine A; Laird, Peter W; Noushmehr, Houtan; Wiznerowicz, Maciej

Published In Cell, (2018 04 05)

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.

PubMed ID: 29625051

MeSH Terms: No MeSH terms associated with this publication