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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
Malta T.M.21; Sokolov A.17; Gentles A.J.19; Burzykowski T.14; Poisson L.21; Weinstein J.N.15; Kaminska B.6; Huelsken J.22; Omberg L.4; Gevaert O.19; Colaprico A.18; Czerwinska P.11; Mazurek S.11; Mishra L.13; Heyn H.7; Krasnitz A.8; Godwin A.K.16; Lazar A.J.15; Caesar-Johnson S.J.; Demchok J.A.; Felau I.; Kasapi M.; Ferguson M.L.; Hutter C.M.; Sofia H.J.; Tarnuzzer R.; Wang Z.; Yang L.; Zenklusen J.C.; Zhang J.J.; Chudamani S.; Liu J.; Lolla L.; Naresh R.; Pihl T.; Sun Q.; Wan Y.; Wu Y.; Cho J.; DeFreitas T.; Frazer S.; Gehlenborg N.; Getz G.; Heiman D.I.; Kim J.; Lawrence M.S.; Lin P.; Meier S.; Noble M.S.; Saksena G.; Voet D.; Zhang H.; Bernard B.; Chambwe N.; Dhankani V.; Knijnenburg T.; Kramer R.; Leinonen K.; Liu Y.; Miller M.; Reynolds S.; Shmulevich I.; Thorsson V.; Zhang W.; Akbani R.; Broom B.M.; Hegde A.M.; Ju Z.; Kanchi R.S.; Korkut A.; Li J.; Liang H.; Ling S.; Liu W.; Lu Y.; Mills G.B.; Ng K.-S.; Rao A.; Ryan M.; Wang J.; Weinstein J.N.; Zhang J.; Abeshouse A.; Armenia J.; 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2018-04-05
Source PublicationCell
ISSN10974172 00928674
Volume173Issue:2Pages:338-354.e15
AbstractCancer 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.
Keywordcancer stem cells dedifferentiation epigenomic genomic machine learning pan-cancer stemness The Cancer Genome Atlas
DOI10.1016/j.cell.2018.03.034
URLView the original
Language英語
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Cited Times [WOS]:68   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Interuniversity Institute of Bioinformatics in Brussels
2.International Institute for Molecular Oncology
3.Greater Poland Cancer Center
4.Sage Bionetworks
5.Van Andel Research Institute
6.Nencki Institute of Experimental Biology of the Polish Academy of Sciences
7.Centro de Regulacion Genomica
8.Cold Spring Harbor Laboratory
9.Universidade de Sao Paulo - USP
10.The University of North Carolina at Chapel Hill
11.Poznan University of Medical Sciences
12.Medical University of Warsaw
13.George Washington University
14.Universiteit Hasselt
15.University of Texas MD Anderson Cancer Center
16.University of Kansas Medical Center
17.Harvard Medical School
18.Université libre de Bruxelles (ULB)
19.Stanford University
20.University of California, Santa Cruz
21.Henry Ford Health System
22.Swiss Federal Institute of Technology, Lausanne
Recommended Citation
GB/T 7714
Malta T.M.,Sokolov A.,Gentles A.J.,et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation[J]. Cell,2018,173(2):338-354.e15.
APA Malta T.M..,Sokolov A..,Gentles A.J..,Burzykowski T..,Poisson L..,...&Wiznerowicz M..(2018).Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.Cell,173(2),338-354.e15.
MLA Malta T.M.,et al."Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation".Cell 173.2(2018):338-354.e15.
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