97
IRUS Total
Downloads
  Altmetric

Machine learning identifies stemness features associated with oncogenic dedifferentiation

File Description SizeFormat 
1-s2.0-S0092867418303581-main.pdfPublished version8.84 MBAdobe PDFView/Open
Title: Machine learning identifies stemness features associated with oncogenic dedifferentiation
Authors: Malta, TM
Sokolov, A
Gentles, AJ
Burzykowski, T
Poisson, L
Weinstein, JN
Kaminska, B
Huelsken, J
Omberg, L
Gevaert, O
Colaprico, A
Czerwinska, P
Mazurek, S
Mishra, L
Heyn, H
Krasnitz, A
Godwin, AK
Lazar, AJ
Stuart, JM
Hoadley, KA
Laird, PW
Noushmehr, H
Wiznerowicz, M
Item Type: Journal Article
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.
Issue Date: 5-Apr-2018
Date of Acceptance: 14-Mar-2018
URI: http://hdl.handle.net/10044/1/71222
DOI: https://doi.org/10.1016/j.cell.2018.03.034
ISSN: 0092-8674
Publisher: Elsevier
Start Page: 338
End Page: 354.e15
Journal / Book Title: Cell
Volume: 173
Issue: 2
Copyright Statement: © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor/Funder: SAIC-F-Frederick, Inc
Leidos Biomedical Research, Inc.
Funder's Grant Number: TCGA Pilot Program
15Y011ST
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
GENE-EXPRESSION SIGNATURE
EMBRYONIC STEM
CONNECTIVITY MAP
SELF-RENEWAL
ANNEXIN 1
CANCER
CELLS
TUMOR
OVEREXPRESSION
PROLIFERATION
The Cancer Genome Atlas
cancer stem cells
dedifferentiation
epigenomic
genomic
machine learning
pan-cancer
stemness
Carcinogenesis
Cell Dedifferentiation
DNA Methylation
Databases, Genetic
Epigenesis, Genetic
Humans
Machine Learning
MicroRNAs
Neoplasm Metastasis
Neoplasms
Stem Cells
Transcriptome
Tumor Microenvironment
Cancer Genome Atlas Research Network
Stem Cells
Humans
Neoplasms
Neoplasm Metastasis
MicroRNAs
DNA Methylation
Epigenesis, Genetic
Databases, Genetic
Cell Dedifferentiation
Tumor Microenvironment
Transcriptome
Carcinogenesis
Machine Learning
Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
GENE-EXPRESSION SIGNATURE
EMBRYONIC STEM
CONNECTIVITY MAP
SELF-RENEWAL
ANNEXIN 1
CANCER
CELLS
TUMOR
OVEREXPRESSION
PROLIFERATION
06 Biological Sciences
11 Medical and Health Sciences
Developmental Biology
Publication Status: Published
Open Access location: http://www.cell.com/cell/retrieve/pii/S0092867418303581?_returnURL=https:%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867418303581%3Fshowall%3Dtrue
Online Publication Date: 2018-04-05
Appears in Collections:Department of Surgery and Cancer