A community computational challenge to predict the activity of pairs of compounds

被引：231

作者：

Bansal, Mukesh ^{[1
,2
]}

Yang, Jichen ^{[3
]}

Karan, Charles ^{[4
]}

Menden, Michael P. ^{[5
]}

Costello, James C. ^{[6
,7
]}

Tang, Hao ^{[3
]}

Xiao, Guanghua ^{[3
]}

Li, Yajuan ^{[8
]}

Allen, Jeffrey ^{[3
]}

Zhong, Rui ^{[3
]}

Chen, Beibei ^{[3
]}

Kim, Minsoo ^{[3
,9
]}

Wang, Tao ^{[3
]}

Heiser, Laura M. ^{[10
]}

Realubit, Ronald ^{[4
]}

Mattioli, Michela ^{[11
]}

Alvarez, Mariano J. ^{[1
,2
]}

Shen, Yao ^{[1
,2
]}

Gallahan, Daniel ^{[12
]}

Singer, Dinah ^{[12
]}

Saez-Rodriguez, Julio ^{[5
]}

Xie, Yang ^{[3
,9
]}

Stolovitzky, Gustavo ^{[13
]}

Califano, Andrea ^{[1
,2
,14
,15
,16
,17
]}

Abbuehl, Jean-Paul ^{[18
]}

Altman, Russ B. ^{[19
]}

Balcome, Shawn ^{[20
]}

Bell, Ana ^{[21
,22
]}

Bender, Andreas ^{[23
]}

Berger, Bonnie ^{[24
]}

Bernard, Jonathan ^{[18
]}

Bieberich, Andrew A. ^{[25
]}

Borboudakis, Giorgos ^{[26
,27
]}

Chan, Christina ^{[28
,29
,30
]}

Chen, Ting-Huei ^{[31
]}

Choi, Jaejoon ^{[32
]}

Coelho, Luis Pedro ^{[33
]}

Creighton, Chad J. ^{[34
]}

Dampier, Will ^{[35
]}

Davisson, V. Jo ^{[25
]}

Deshpande, Raamesh ^{[20
]}

Diao, Lixia ^{[36
]}

Di Camillo, Barbara ^{[37
]}

Dundar, Murat ^{[38
]}

Ertel, Adam ^{[39
]}

Goswami, Chirayu P. ^{[41
]}

Gottlieb, Assaf ^{[19
]}

Gould, Michael N. ^{[42
]}

Goya, Jonathan ^{[22
]}

Grau, Michael ^{[43
]}

机构：

[1] Columbia Univ, Dept Syst Biol, New York, NY USA

[2] Columbia Univ, Ctr Computat Biol & Bioinformat, New York, NY USA

[3] Univ Texas SW Med Ctr Dallas, Quantitat Biomed Res Ctr, Dept Clin Sci, Dallas, TX 75390 USA

[4] Columbia Univ, Columbia Genome Ctr, High Throughput Screening Facil, New York, NY USA

[5] European Bioinformat Inst, European Mol Biol Lab, Cambridge, England

[6] Boston Univ, Dept Biomed Engn, Howard Hughes Med Inst, Boston, MA 02215 USA

[7] Boston Univ, Howard Hughes Med Inst, Ctr Synthet Biol, Boston, MA 02215 USA

[8] Univ Texas SW Med Ctr Dallas, Dept Immunol, Dallas, TX 75390 USA

[9] Univ Texas SW Med Ctr Dallas, Simmons Comprehens Canc Ctr, Dallas, TX USA

[10] Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97201 USA

[11] Fdn Ist Italiano Tecnol IIT, Ctr Genom Sci IIT SEMM, Milan, Italy

[12] NCI, Div Canc Biol, Bethesda, MD 20892 USA

[13] IBM Corp, Thomas J Watson Res Ctr, IBM Computat Biol Ctr, Yorktown Hts, NY 10598 USA

[14] Columbia Univ, Dept Biomed Informat, New York, NY USA

[15] Columbia Univ, Dept Biochem & Mol Biophys, New York, NY USA

[16] Columbia Univ, Inst Canc Genet, New York, NY USA

[17] Columbia Univ, Herbert Irving Comprehens Canc Ctr, New York, NY USA

[18] Swiss Fed Inst Technol Lausanne EPFL, Swiss Inst Expt Canc Res ISREC, Lausanne, Switzerland

[19] Stanford Univ, Dept Genet, Stanford, CA 94305 USA

[20] Univ Minnesota, Minneapolis, MN USA

[21] Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA

[22] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 USA

[23] Univ Cambridge, Unilever Ctr, Cambridge, England

[24] MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[25] Purdue Univ, Dept Med Chem & Mol Pharmacol, W Lafayette, IN 47907 USA

[26] Univ Crete, Dept Comp Sci, Iraklion, NE, Greece

[27] FORTH, Inst Comp Sci, Iraklion, NE, Greece

[28] Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA

[29] Michigan State Univ, Dept Chem Engn & Mat Sci, E Lansing, MI 48824 USA

[30] Michigan State Univ, Dept Biochem & Mol Biol, E Lansing, MI 48824 USA

[31] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27515 USA

[32] Korea Adv Inst Sci & Technol, Daejeon, South Korea

[33] Univ Lisbon, Inst Med Mol, Fac Med, Lisbon, Portugal

[34] Baylor Coll Med, Dept Med, Dan L Duncan Ctr Div Biostat, Houston, TX 77030 USA

[35] Drexel Univ, Ctr Integrated Bioinformat, Philadelphia, PA 19104 USA

[36] Univ Texas MD Anderson Canc Ctr, Dept Bioinformat & Computat Biol, Houston, TX 77030 USA

[37] Univ Padua, Dept Informat Engn, Padua, Italy

[38] IUPUI, Dept Comp & Informat Sci, Indianapolis, IN USA

[39] Jefferson Kimmel Canc Ctr, Philadelphia, PA USA

[40] Cellworks Grp Inc, San Jose, CA USA

[41] IU Sch Med, Ctr Computat Biol & Bioinformat, Indianapolis, IN USA

[42] Univ Wisconsin, Dept Oncol & Carbone Canc Ctr, Madison, WI USA

[43] Univ Marburg, Dept Phys, Marburg, Germany

[44] Leiden Univ, Leiden Acad Ctr Drug Res, Leiden, Netherlands

[45] Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki, Finland

[46] Izmir Inst Technol, Izmir, Turkey

[47] Aalto Univ, Helsinki Inst Informat Technol, Dept Informat & Comp Sci, Espoo, Finland

[48] Univ Helsinki, Inst Informat Technol HIIT, Dept Comp Sci, Helsinki, Finland

[49] Korea Inst Sci & Technol Informat, Daejeon, South Korea

[50] Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, Key Lab Computat Biol, Shanghai, Peoples R China

来源：

NATURE BIOTECHNOLOGY | 2014年 / 32卷 / 12期

基金：

美国国家卫生研究院; 美国国家科学基金会;

关键词：

DRUG-COMBINATIONS; SYSTEMS BIOLOGY; CANCER; THERAPY; NETWORK; CHEMOTHERAPY; MODELS; INHIBITION; EXPRESSION; RESISTANCE;

D O I：

10.1038/nbt.3052

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

引用

页码：1213 / +

页数：12

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