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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
- Source :
- Cell, vol 173, iss 2
- Publication Year :
- 2018
- Publisher :
- eScholarship, University of California, 2018.
-
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. Stemness features extracted from transcriptomic and epigenetic data from TCGA tumors reveal novel biological and clinical insight, as well as potential drug targets for anti-cancer therapies.
- Subjects :
- cancer stem cells
Carcinogenesis
pan-cancer
The Cancer Genome Atlas
Cancer Genome Atlas Research Network
epigenomic
Medical and Health Sciences
Machine Learning
genomic
Databases
stemness
Genetic
Stem Cell Research - Nonembryonic - Human
Neoplasms
Tumor Microenvironment
Humans
Neoplasm Metastasis
Cancer
Stem Cells
dedifferentiation
DNA Methylation
Cell Dedifferentiation
Biological Sciences
Stem Cell Research
MicroRNAs
Good Health and Well Being
Stem Cell Research - Nonembryonic - Non-Human
Transcriptome
Epigenesis
Developmental Biology
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Cell, vol 173, iss 2
- Accession number :
- edsair.doi.dedup.....ef6132804531425903cb8ac4d8bd9f47