Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

doi:10.1016/j.cell.2018.03.034

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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.; Chakravarty D.; Chatila W.K.; de Bruijn I.; Gao J.; Gross B.E.; Heins Z.J.; Kundra R.; La K.; Ladanyi M.; Luna A.; Nissan M.G.; Ochoa A.; Phillips S.M.; Reznik E.; Sanchez-Vega F.; Sander C.; Schultz N.; Sheridan R.; Sumer S.O.; Sun Y.; Taylor B.S.; Wang J.; Zhang H.; Anur P.; Peto M.; Spellman P.; Benz C.; Stuart J.M.; Wong C.K.; Yau C.; Hayes D.N.; Parker J.S.; Wilkerson M.D.; Ally A.; Balasundaram M.; Bowlby R.; Brooks D.; Carlsen R.; Chuah E.; Dhalla N.; Holt R.; Jones S.J.M.; Kasaian K.; Lee D.; Ma Y.; Marra M.A.; Mayo M.; Moore R.A.; Mungall A.J.; Mungall K.; Robertson A.G.; Sadeghi S.; Schein J.E.; Sipahimalani P.; Tam A.; Thiessen N.; Tse K.; Wong T.; Berger A.C.; Beroukhim R.; Cherniack A.D.; Cibulskis C.; Gabriel S.B.; Gao G.F.; Ha G.; Meyerson M.; Schumacher S.E.; Shih J.; Kucherlapati M.H.; Kucherlapati R.S.; Baylin S.; Cope L.; Danilova L.; Bootwalla M.S.; Lai P.H.; Maglinte D.T.; Van Den Berg D.J.; Weisenberger D.J.; Auman J.T.; Balu S.; Bodenheimer T.; Fan C.; Hoadley K.A.; Hoyle A.P.; Jefferys S.R.; Jones C.D.; Meng S.; Mieczkowski P.A.; Mose L.E.; Perou A.H.; Perou C.M.; Roach J.; Shi Y.; Simons J.V.; Skelly T.; Soloway M.G.; Tan D.; Veluvolu U.; Fan H.; Hinoue T.; Laird P.W.; Shen H.; Zhou W.; Bellair M.; Chang K.; Covington K.; Creighton C.J.; Dinh H.; Doddapaneni H.; Donehower L.A.; Drummond J.; Gibbs R.A.; Glenn R.; Hale W.; Han Y.; Hu J.; Korchina V.; Lee S.; Lewis L.; Li W.; Liu X.; Morgan M.; Morton D.; Muzny D.; Santibanez J.; Sheth M.; Shinbrot E.; Wang L.; Wang M.; Wheeler D.A.; Xi L.; Zhao F.; Hess J.; Appelbaum E.L.; Bailey M.; Cordes M.G.; Ding L.; Fronick C.C.; Fulton L.A.; Fulton R.S.; Kandoth C.; Mardis E.R.; McLellan M.D.; Miller C.A.; Schmidt H.K.; Wilson R.K.; Crain D.; Curley E.; Gardner J.; Lau K.; Mallery D.; Morris S.; Paulauskis J.; Penny R.; Shelton C.; Shelton T.; Sherman M.; Thompson E.; Yena P.; Bowen J.; Gastier-Foster J.M.; Gerken M.; Leraas K.M.; Lichtenberg T.M.; Ramirez N.C.; Wise L.; Zmuda E.; Corcoran N.; Costello T.; Hovens C.; Carvalho A.L.; de Carvalho A.C.; Fregnani J.H.; Longatto-Filho A.; Reis R.M.; Scapulatempo-Neto C.; Silveira H.C.S.; Vidal D.O.; Burnette A.; Eschbacher J.; Hermes B.; Noss A.; Singh R.; Anderson M.L.; Castro P.D.; Ittmann M.; Huntsman D.; Kohl B.; Le X.; Thorp R.; Andry C.; Duffy E.R.; Lyadov V.; Paklina O.; Setdikova G.; Shabunin A.; Tavobilov M.; McPherson C.; Warnick R.; Berkowitz R.; Cramer D.; Feltmate C.; Horowitz N.; Kibel A.; Muto M.; Raut C.P.; Malykh A.; Barnholtz-Sloan J.S.; Barrett W.; Devine K.; Fulop J.; Ostrom Q.T.; Shimmel K.; Wolinsky Y.; Sloan A.E.; De Rose A.; Giuliante F.; Goodman M.; Karlan B.Y.; Hagedorn C.H.; Eckman J.; Harr J.; Myers J.; Tucker K.; Zach L.A.; Deyarmin B.; Hu H.; Kvecher L.; Larson C.; Mural R.J.; Somiari S.; Vicha A.; Zelinka T.; Bennett J.; Iacocca M.; Rabeno B.; Swanson P.; Latour M.; Lacombe L.; Tetu B.; Bergeron A.; McGraw M.; Staugaitis S.M.; Chabot J.; Hibshoosh H.; Sepulveda A.; Su T.; Wang T.; Potapova O.; Voronina O.; Desjardins L.; Mariani O.; Roman-Roman S.; Sastre X.; Stern M.-H.; Cheng F.; Signoretti S.; Berchuck A.; Bigner D.; Lipp E.; Marks J.; McCall S.; McLendon R.; Secord A.; Sharp A.; Behera M.; Brat D.J.; Chen A.; Delman K.; Force S.; Khuri F.; Magliocca K.; Maithel S.; Olson J.J.; Owonikoko T.; Pickens A.; Ramalingam S.; Shin D.M.; Sica G.; Van Meir E.G.; Zhang H.; Eijckenboom W.; Gillis A.; Korpershoek E.; Looijenga L.; Oosterhuis W.; Stoop H.; van Kessel K.E.; Zwarthoff E.C.; Calatozzolo C.; Cuppini L.; Cuzzubbo S.; DiMeco F.; Finocchiaro G.; Mattei L.; Perin A.; Pollo B.; Chen C.; Houck J.; Lohavanichbutr P.; Hartmann A.; Stoehr C.; Stoehr R.; Taubert H.; Wach S.; Wullich B.; Kycler W.; Murawa D.; Wiznerowicz M.; Chung K.; Edenfield W.J.; Martin J.; Baudin E.; Bubley G.; Bueno R.; De Rienzo A.; Richards W.G.; Kalkanis S.; Mikkelsen T.; Noushmehr H.; Scarpace L.; Girard N.; Aymerich M.; Campo E.; Gine E.; Guillermo A.L.; Van Bang N.; Hanh P.T.; Phu B.D.; Tang Y.; Colman H.; Evason K.; Dottino P.R.; Martignetti J.A.; Gabra H.; Juhl H.; Akeredolu T.; Stepa S.; Hoon D.; Ahn K.; Kang K.J.; Beuschlein F.; Breggia A.; Birrer M.; Bell D.; Borad M.; Bryce A.H.; Castle E.; Chandan V.; Cheville J.; Copland J.A.; Farnell M.; Flotte T.; Giama N.; Ho T.; Kendrick M.; Kocher J.-P.; Kopp K.; Moser C.; Nagorney D.; O'Brien D.; O'Neill B.P.; Patel T.; Petersen G.; Que F.; Rivera M.; Roberts L.; Smallridge R.; Smyrk T.; Stanton M.; Thompson R.H.; Torbenson M.; Yang J.D.; Zhang L.; Brimo F.; Ajani J.A.; Gonzalez A.M.A.; Behrens C.; Bondaruk J.; Broaddus R.; Czerniak B.; Esmaeli B.; Fujimoto J.; Gershenwald J.; Guo C.; Lazar A.J.; Logothetis C.; Meric-Bernstam F.; Moran C.; Ramondetta L.; Rice D.; Sood A.; Tamboli P.; Thompson T.; Troncoso P.; Tsao A.; Wistuba I.; Carter C.; Haydu L.; Hersey P.; Jakrot V.; Kakavand H.; Kefford R.; Lee K.; Long G.; Mann G.; Quinn M.; Saw R.; Scolyer R.; Shannon K.; Spillane A.; Stretch J.; Synott M.; Thompson J.; Wilmott J.; Al-Ahmadie H.; Chan T.A.; Ghossein R.; Gopalan A.; Levine D.A.; Reuter V.; Singer S.; Singh B.; Tien N.V.; Broudy T.; Mirsaidi C.; Nair P.; Drwiega P.; Miller J.; Smith J.; Zaren H.; Park J.-W.; Hung N.P.; Kebebew E.; Linehan W.M.; Metwalli A.R.; Pacak K.; Pinto P.A.; Schiffman M.; Schmidt L.S.; Vocke C.D.; Wentzensen N.; Worrell R.; Yang H.; Moncrieff M.; Goparaju C.; Melamed J.; Pass H.; Botnariuc N.; Caraman I.; Cernat M.; Chemencedji I.; Clipca A.; Doruc S.; Gorincioi G.; Mura S.; Pirtac M.; Stancul I.; Tcaciuc D.; Albert M.; Alexopoulou I.; Arnaout A.; Bartlett J.; Engel J.; Gilbert S.; Parfitt J.; Sekhon H.; Thomas G.; Rassl D.M.; Rintoul R.C.; Bifulco C.; Tamakawa R.; Urba W.; Hayward N.; Timmers H.; Antenucci A.; Facciolo F.; Grazi G.; Marino M.; Merola R.; de Krijger R.; Gimenez-Roqueplo A.-P.; Piche A.; Chevalier S.; McKercher G.; Birsoy K.; Barnett G.; Brewer C.; Farver C.; Naska T.; Pennell N.A.; Raymond D.; Schilero C.; Smolenski K.; Williams F.; Morrison C.; Borgia J.A.; Liptay M.J.; Pool M.; Seder C.W.; Junker K.; Omberg L.4; Dinkin M.; Manikhas G.; Alvaro D.; Bragazzi M.C.; Cardinale V.; Carpino G.; Gaudio E.; Chesla D.; Cottingham S.; Dubina M.; Moiseenko F.; Dhanasekaran R.; Becker K.-F.; Janssen K.-P.; Slotta-Huspenina J.; Abdel-Rahman M.H.; Aziz D.; Bell S.; Cebulla C.M.; Davis A.; Duell R.; Elder J.B.; Hilty J.; Kumar B.; Lang J.; Lehman N.L.; Mandt R.; Nguyen P.; Pilarski R.; Rai K.; Schoenfield L.; Senecal K.; Wakely P.; Hansen P.; Lechan R.; Powers J.; Tischler A.; Grizzle W.E.; Sexton K.C.; Kastl A.; Henderson J.; Porten S.; Waldmann J.; Fassnacht M.; Asa S.L.; Schadendorf D.; Couce M.; Graefen M.; Huland H.; Sauter G.; Schlomm T.; Simon R.; Tennstedt P.; Olabode O.; Nelson M.; Bathe O.; Carroll P.R.; Chan J.M.; Disaia P.; Glenn P.; Kelley R.K.; Landen C.N.; Phillips J.; Prados M.; Simko J.; Smith-McCune K.; VandenBerg S.; Roggin K.; Fehrenbach A.; Kendler A.; Sifri S.; Steele R.; Jimeno A.; Carey F.; Forgie I.; Mannelli M.; Carney M.; Hernandez B.; Campos B.; Herold-Mende C.; Jungk C.; Unterberg A.; von Deimling A.; Bossler A.; Galbraith J.; Jacobus L.; Knudson M.; Knutson T.; Ma D.; Milhem M.; Sigmund R.; Godwin A.K.; Madan R.; Rosenthal H.G.; Adebamowo C.; Adebamowo S.N.; Boussioutas A.; Beer D.; Giordano T.; Mes-Masson A.-M.; Saad F.; Bocklage T.; Landrum L.; Mannel R.; Moore K.; Moxley K.; Postier R.; Walker J.; Zuna R.; Feldman M.; Valdivieso F.; Dhir R.; Luketich J.; Pinero E.M.M.; Quintero-Aguilo M.; Carlotti C.G.; Dos Santos J.S.; Kemp R.; Sankarankuty A.; Tirapelli D.; Catto J.; Agnew K.; Swisher E.; Creaney J.; Robinson B.; Shelley C.S.; Godwin E.M.; Kendall S.; Shipman C.; Bradford C.; Carey T.; Haddad A.; Moyer J.; Peterson L.; Prince M.; Rozek L.; Wolf G.; Bowman R.; Fong K.M.; Yang I.; Korst R.; Rathmell W.K.; Fantacone-Campbell J.L.; Hooke J.A.; Kovatich A.J.; Shriver C.D.; DiPersio J.; Drake B.; Govindan R.; Heath S.; Ley T.; Van Tine B.; Westervelt P.; Rubin M.A.; Lee J.I.; Aredes N.D.; Mariamidze A.; Stuart J.M.20; Hoadley K.A.10; Laird P.W.5; Noushmehr H.21; Wiznerowicz M.11
	2018-04-05
Source Publication	Cell
ISSN	10974172 00928674
Volume	173 Issue:2 Pages:338-354.e15
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.
Keyword	Cancer Stem Cells Dedifferentiation Epigenomic Genomic Machine Learning Pan-cancer Stemness The Cancer Genome Atlas
DOI	10.1016/j.cell.2018.03.034
URL	View the original
Language	英語English
WOS ID	WOS:000429320200010
Scopus ID	2-s2.0-85044967234
Fulltext Access	View Full-Text via DOI View Full-Text via Web of Science View Full-Text via Scopus
Citation statistics
Document Type	Journal article
Collection	University of Macau
Affiliation	1.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.., Weinstein J.N.., Kaminska B.., Huelsken J.., Omberg L.., Gevaert O.., Colaprico A.., Czerwinska P.., Mazurek S.., Mishra L.., Heyn H.., Krasnitz A.., Godwin A.K.., Lazar A.J.., Caesar-Johnson S.J.., ...& 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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