TY - JOUR
T1 - Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation
AU - The Cancer Genome Atlas Research Network
AU - Malta, Tathiane M.
AU - Sokolov, Artem
AU - Gentles, Andrew J.
AU - Burzykowski, Tomasz
AU - Poisson, Laila
AU - Weinstein, John N.
AU - Kamińska, Bożena
AU - Huelsken, Joerg
AU - Mishra, Lopa
AU - Lazar, Alexander J.
AU - Liu, Yuexin
AU - Zhang, Wei
AU - Akbani, Rehan
AU - Broom, Bradley M.
AU - Ju, Zhenlin
AU - Kanchi, Rupa Sridevi
AU - Korkut, Anil
AU - Li, Jun
AU - Liang, Han
AU - Ling, Shiyun
AU - Liu, Wenbin
AU - Lu, Yiling
AU - Mills, Gordon B
AU - Uppore Kukkillaya, Arvind Rao
AU - Zhang, Jiexin
AU - Liu, Xiuping
AU - Wang, Linghua
AU - Fregnani, José Humberto T. G.
AU - Reis, Rui M. V.
AU - Ajani, Jaffer A.
AU - Behrens, Carmen
AU - Bondaruk, Jolanta
AU - Broaddus, Russell
AU - Czerniak, Bogdan
AU - Esmaeli, Bita
AU - Fujimoto, Junya
AU - Gershenwald, Jeffrey
AU - Guo, Charles
AU - Logothetis, Christopher
AU - Meric-Bernstam, Funda
AU - Moran, Cesar
AU - Ramondetta, Lois
AU - Rice, David
AU - Sood, Anil
AU - Tamboli, Pheroze
AU - Thompson, Timothy
AU - Troncoso, Patricia
AU - Tsao, Anne
AU - Wistuba, Ignacio
AU - von Deimling, Andreas
N1 - Publisher Copyright:
© 2018 The Authors
PY - 2018/4/5
Y1 - 2018/4/5
N2 - 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.
AB - 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.
KW - The Cancer Genome Atlas
KW - cancer stem cells
KW - dedifferentiation
KW - epigenomic
KW - genomic
KW - machine learning
KW - pan-cancer
KW - stemness
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UR - http://www.scopus.com/inward/citedby.url?scp=85044967234&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2018.03.034
DO - 10.1016/j.cell.2018.03.034
M3 - Article
C2 - 29625051
AN - SCOPUS:85044967234
SN - 0092-8674
VL - 173
SP - 338-354.e15
JO - Cell
JF - Cell
IS - 2
ER -