Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

doi:10.1016/j.cell.2018.03.034

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Research output: Contribution to journal › Article › peer-review

1776 Scopus citations

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.

Original language	English
Pages (from-to)	338-354.e15
Journal	Cell
Volume	173
Issue number	2
DOIs	https://doi.org/10.1016/j.cell.2018.03.034
State	Published - Apr 5 2018

Bibliographical note

Publisher Copyright:
© 2018 The Authors

Funding

Michael Seiler, Peter G. Smith, Ping Zhu, Silvia Buonamici, and Lihua Yu are employees of H3 Biomedicine, Inc. Parts of this work are the subject of a patent application: WO2017040526 titled “Splice variants associated with neomorphic sf3b1 mutants.” Shouyoung Peng, Anant A. Agrawal, James Palacino, and Teng Teng are employees of H3 Biomedicine, Inc. Andrew D. Cherniack, Ashton C. Berger, and Galen F. Gao receive research support from Bayer Pharmaceuticals. Gordon B. Mills serves on the External Scientific Review Board of Astrazeneca. Anil Sood is on the Scientific Advisory Board for Kiyatec and is a shareholder in BioPath. Jonathan S. Serody receives funding from Merck, Inc. Kyle R. Covington is an employee of Castle Biosciences, Inc. Preethi H. Gunaratne is founder, CSO, and shareholder of NextmiRNA Therapeutics. Christina Yau is a part-time employee/consultant at NantOmics. Franz X. Schaub is an employee and shareholder of SEngine Precision Medicine, Inc. Carla Grandori is an employee, founder, and shareholder of SEngine Precision Medicine, Inc. Robert N. Eisenman is a member of the Scientific Advisory Boards and shareholder of Shenogen Pharma and Kronos Bio. Daniel J. Weisenberger is a consultant for Zymo Research Corporation. Joshua M. Stuart is the founder of Five3 Genomics and shareholder of NantOmics. Marc T. Goodman receives research support from Merck, Inc. Andrew J. Gentles is a consultant for Cibermed. Charles M. Perou is an equity stock holder, consultant, and Board of Directors member of BioClassifier and GeneCentric Diagnostics and is also listed as an inventor on patent applications on the Breast PAM50 and Lung Cancer Subtyping assays. Matthew Meyerson receives research support from Bayer Pharmaceuticals; is an equity holder in, consultant for, and Scientific Advisory Board chair for OrigiMed; and is an inventor of a patent for EGFR mutation diagnosis in lung cancer, licensed to LabCorp. Eduard Porta-Pardo is an inventor of a patent for domainXplorer. Han Liang is a shareholder and scientific advisor of Precision Scientific and Eagle Nebula. Da Yang is an inventor on a pending patent application describing the use of antisense oligonucleotides against specific lncRNA sequence as diagnostic and therapeutic tools. Yonghong Xiao was an employee and shareholder of TESARO, Inc. Bin Feng is an employee and shareholder of TESARO, Inc. Carter Van Waes received research funding for the study of IAP inhibitor ASTX660 through a Cooperative Agreement between NIDCD, NIH, and Astex Pharmaceuticals. Raunaq Malhotra is an employee and shareholder of Seven Bridges, Inc. Peter W. Laird serves on the Scientific Advisory Board for AnchorDx. Joel Tepper is a consultant at EMD Serono. Kenneth Wang serves on the Advisory Board for Boston Scientific, Microtech, and Olympus. Andrea Califano is a founder, shareholder, and advisory board member of DarwinHealth, Inc. and a shareholder and advisory board member of Tempus, Inc. Toni K. Choueiri serves as needed on advisory boards for Bristol-Myers Squibb, Merck, and Roche. Lawrence Kwong receives research support from Array BioPharma. Sharon E. Plon is a member of the Scientific Advisory Board for Baylor Genetics Laboratory. Beth Y. Karlan serves on the Advisory Board of Invitae. We thank Marcin Cieślik from Michigan Center for Translational Pathology at University of Michigan for providing the MTE500 dataset. This work was supported by the following grants: NIH grants U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845, U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, and P30 CA016672; NCI grants 5R01CA180778, 3U24CA143858, 1U24CA210990, 5U54HG006097, 1U24CA210949, and 1U24CA210950; NIGMS grant 5R01GM109031; the Henry Ford Cancer Institute's Early Career Investigator Award grant A20054 to T.M.M; Sao Paulo Research Foundation (FAPESP) grants 2014/02245-3 and 2016/01975-3 to T.M.M. and H.N.; FAPESP grants 2014/08321-3, 2015/07925-5, 2016/01389-7, 2016/10436-9, 2016/06488-3, 2016/12329-5, and 2016/15485-8, and Henry Ford Hospital grant A30935 to H.N.; Spanish Institute of Health Carlos III grant CP14/00229; Mary K. Chapman Foundation gift “Chapman Foundation Fund for Bioinformatics,” CPRIT grant RP13039, and the Michael & Susan Dell Foundation grant “The Lorraine Dell Program in Bioinformatics” to J.N.W.; and the Polish Science Foundation Welcome grant 2010/3-3 to M.W. We thank Marcin Cieślik from Michigan Center for Translational Pathology at University of Michigan for providing the MTE500 dataset. This work was supported by the following grants: NIH grants U54 HG003273 , U54 HG003067 , U54 HG003079 , U24 CA143799 , U24 CA143835 , U24 CA143840 , U24 CA143843 , U24 CA143845 , U24 CA143848 , U24 CA143858 , U24 CA143866 , U24 CA143867 , U24 CA143882 , U24 CA143883 , U24 CA144025 , and P30 CA016672 ; NCI grants 5R01CA180778 , 3U24CA143858 , 1U24CA210990 , 5U54HG006097 , 1U24CA210949 , and 1U24CA210950 ; NIGMS grant 5R01GM109031 ; the Henry Ford Cancer Institute’s Early Career Investigator Award grant A20054 to T.M.M; Sao Paulo Research Foundation (FAPESP) grants 2014/02245-3 and 2016/01975-3 to T.M.M. and H.N.; FAPESP grants 2014/08321-3 , 2015/07925-5 , 2016/01389-7 , 2016/10436-9 , 2016/06488-3 , 2016/12329-5 , and 2016/15485-8 , and Henry Ford Hospital grant A30935 to H.N.; Spanish Institute of Health Carlos III grant CP14/00229 ; Mary K. Chapman Foundation gift “Chapman Foundation Fund for Bioinformatics,” CPRIT grant RP13039 , and the Michael & Susan Dell Foundation grant “The Lorraine Dell Program in Bioinformatics” to J.N.W.; and the Polish Science Foundation Welcome grant 2010/3-3 to M.W.

Funders	Funder number
Merck
National Institutes of Health (NIH)
Michael and Susan Dell Foundation
Mary K. Chapman Foundation
Lorraine Dell Program in Bioinformatics
Henry Ford Hospital	A30935
National Institute of General Medical Sciences DP2GM119177 Sophie Dumont National Institute of General Medical Sciences	R01GM109031
National Institute of Biomedical Imaging and Bioengineering	R01EB020527
National Human Genome Research Institute	U54HG003273, U54HG006097, U54HG003079, U54HG003067
Cancer Prevention and Research Institute of Texas	RP13039
National Computational Infrastructure	5U54HG006097, 5R01CA180778, 1U24CA210950, 3U24CA143858, 1U24CA210949, 1U24CA210990
National Childhood Cancer Registry – National Cancer Institute	P30CA016086, U24CA143858, P30CA124435, R01CA163722, R01CA180778, U24CA210988, U24CA143882, R50CA221675, U24CA143843, U24CA143866, U24CA143867, U24CA143845, U24CA143883, U24CA143840, P30CA016672, U24CA143848, U24CA210949, U24CA210957, U24CA144025, R01CA236591, U24CA210950, U24CA210974, U24CA143799, U24CA210990, U01CA230690, U24CA143835
Fundação de Amparo à Pesquisa do Estado de São Paulo	2014/08321-3, 2015/07925-5, 2016/12329-5, 2016/06488-3, 2016/01389-7, 2016/01975-3, 2016/15485-8, 2016/10436-9, 2014/02245-3
Henry Ford Cancer Institute	A20054
National Institutes of Health/National Institute of Environmental Health Sciences	P30ES010126
Polish Science Foundation Welcome	2010/3-3
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung	169995
U.S. Department of Veterans Affairs	I01BX003732
National Institute on Alcohol Abuse and Alcoholism	R01AA023146
National Institute of Dental and Craniofacial Research	U01DE025188
Spanish Institute of Health Carlos III	CP14/00229

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

The Cancer Genome Atlas
cancer stem cells
dedifferentiation
epigenomic
genomic
machine learning
pan-cancer
stemness

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

Access to Document

10.1016/j.cell.2018.03.034

Cite this

@article{3cef24b446034e098beb55c236af7f39,

title = "Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation",

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.",

keywords = "The Cancer Genome Atlas, cancer stem cells, dedifferentiation, epigenomic, genomic, machine learning, pan-cancer, stemness",

author = "Malta, \{Tathiane M.\} and Artem Sokolov and Gentles, \{Andrew J.\} and Tomasz Burzykowski and Laila Poisson and Weinstein, \{John N.\} and Bo{\.z}ena Kami{\'n}ska and Joerg Huelsken and Larsson Omberg and Olivier Gevaert and Antonio Colaprico and Patrycja Czerwi{\'n}ska and Sylwia Mazurek and Lopa Mishra and Holger Heyn and Alex Krasnitz and Godwin, \{Andrew K.\} and Lazar, \{Alexander J.\} and Caesar-Johnson, \{Samantha J.\} and Demchok, \{John A.\} and Ina Felau and Melpomeni Kasapi and Ferguson, \{Martin L.\} and Hutter, \{Carolyn M.\} and Sofia, \{Heidi J.\} and Roy Tarnuzzer and Zhining Wang and Liming Yang and Zenklusen, \{Jean C.\} and Zhang, \{Jiashan (Julia)\} and Sudha Chudamani and Jia Liu and Laxmi Lolla and Rashi Naresh and Todd Pihl and Qiang Sun and Yunhu Wan and Ye Wu and Juok Cho and Timothy DeFreitas and Scott Frazer and Nils Gehlenborg and Gad Getz and Heiman, \{David I.\} and Jaegil Kim and Lawrence, \{Michael S.\} and Pei Lin and Sam Meier and Noble, \{Michael S.\} and Therese Bocklage",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors",

year = "2018",

month = apr,

day = "5",

doi = "10.1016/j.cell.2018.03.034",

language = "English",

volume = "173",

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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 - Omberg, Larsson

AU - Gevaert, Olivier

AU - Colaprico, Antonio

AU - Czerwińska, Patrycja

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AU - Caesar-Johnson, Samantha J.

AU - Demchok, John A.

AU - Felau, Ina

AU - Kasapi, Melpomeni

AU - Ferguson, Martin L.

AU - Hutter, Carolyn M.

AU - Sofia, Heidi J.

AU - Tarnuzzer, Roy

AU - Wang, Zhining

AU - Yang, Liming

AU - Zenklusen, Jean C.

AU - Zhang, Jiashan (Julia)

AU - Chudamani, Sudha

AU - Liu, Jia

AU - Lolla, Laxmi

AU - Naresh, Rashi

AU - Pihl, Todd

AU - Sun, Qiang

AU - Wan, Yunhu

AU - Wu, Ye

AU - Cho, Juok

AU - DeFreitas, Timothy

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AU - Noble, Michael S.

AU - Bocklage, Therese

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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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 -

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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