Title

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Document Type

Article

Publication Date

4-5-2018

Publication Title

Cell

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.

Medical Subject Headings

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

PubMed ID

29625051

Volume

173

Issue

2

First Page

338

Last Page

354

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