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Nat Biotechnol. 2014 Dec;32(12):1202-12. doi: 10.1038/nbt.2877. Epub 2014 Jun 1.

A community effort to assess and improve drug sensitivity prediction algorithms.

Collaborators (132)

Abbuehl JP14, Aittokallio T8, Allen J15, Altman RB16, Ammad-ud-din M4, Balcome S17, Bansal M7, Battle A18, Bender A19, Berger B20, Bernard J14, Bhattacharjee M21, Bhuvaneshwar K22, Bieberich AA23, Boehm F24, Califano A7, Chan C25, Chen B15, Chen TH26, Choi J27, Coelho LP28, Cokelaer T5, Collins JC10, Costello JC29, Creighton CJ30, Cui J31, Dampier W32, Davisson VJ23, De Baets B33, Deshpande R17, DiCamillo B34, Dundar M35, Duren Z36, Ertel A37, Fan H24, Fang H38, Gallahan D11, Gauba R22, Georgii E4, Gönen M4, Gottlieb A16, Grau M39, Gray JW6, Gusev Y22, Ha MJ26, Han L40, Harris M22, Heiser LM6, Henderson N24, Hejase HA41, Hintsanen P8, Homicsko K14, Honkela A9, Hou JP42, Hwang W27, IJzerman AP43, Kallioniemi O8, Karacali B44, Kaski S12, Keles S24, Kendziorski C24, Khan SA4, Kim J27, Kim M15, Kim Y45, Knowles DA18, Koller D18, Lee J46, Lee JK45, Lenselink EB43, Li B47, Li B31, Li J48, Liang H49, Ma J42, Madhavan S50, Menden MP5, Mooney S47, Mpindi JP8, Myers CL17, Newton MA24, Overington JP51, Pal R52, Peng J20, Pestell R32, Prill RJ53, Qiu P54, Rajwa B55, Sadanandam A14, Saez-Rodriguez J5, Sambo F34, Shin H31, Singer D11, Song J56, Song L22, Sridhar A57, Stock M33, Stolovitzky G13, Sun W26, Ta T24, Tadesse M58, Tan M38, Tang H15, Theodorescu D59, Toffolo GM34, Tozeren A32, Trepicchio W31, Varoquaux N60, Vert JP60, Waegeman W33, Walter T60, Wan Q52, Wang D50, Wang NJ6, Wang W17, Wang Y36, Wang Z24, Wegner JK61, Wennerberg K8, Wu T62, Xia T17, Xiao G15, Xie Y15, Xu Y63, Yang J15, Yuan Y49, Zhang S36, Zhang XS36, Zhao J36, Zuo C24, van Vlijmen HW61, van Westen GJ51.

Author information

1
1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. [2] Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. [3] [4].
2
1] Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA. [2].
3
1] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland. [2].
4
Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland.
5
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
6
Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA.
7
Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA.
8
Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
9
Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
10
1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. [2] Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. [3] Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA.
11
National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
12
1] Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, Espoo, Finland. [2] Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
13
IBM T.J. Watson Research Center, IBM, Yorktown Heights, New York, USA.
14
Swiss Institute for Experimental Cancer Research (ISREC), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
15
Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
16
Departments of Genetics and Bioengineering, Stanford University, Stanford, California, USA.
17
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
18
Department of Computer Science, Stanford University, Palo Alto, California, USA.
19
Unilever Centre, Cambridge University, Cambridge, UK.
20
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA.
21
1] Department of Statistics, University of Pune, Pune, India. [2] School of Mathematics and Statistics, University of Hyderabad, Hyderabad, India.
22
Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA.
23
Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, W. Lafayette, Indiana, USA.
24
1] Department of Statistics, University of Wisconsin, Madison, Wisconsin, USA. [2] Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA.
25
1] Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA. [2] Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, USA. [3] Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA.
26
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
27
Korea Advanced Institute of Science and Technology, Daejeon, Korea.
28
Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.
29
1] Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA. [2] Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. [3].
30
Department of Medicine, Dan L. Duncan Center Division of Biostatistics, Baylor College of Medicine, Houston, Texas, USA.
31
Translational Medicine, Millennium Pharmaceuticals, Cambridge, Massachusetts, USA.
32
Center for Integrated Bioinformatics, Drexel University, Philadelphia, Pennsylvania, USA.
33
Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium.
34
Department of Information Engineering, University of Padova, Padova, Italy.
35
Computer and Information Science Department, IUPUI, Indianapolis, Indiana, USA.
36
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
37
Jefferson Kimmel Cancer Center, Drexel University, Philadelphia, Pennsylvania, USA.
38
Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.
39
Department of Physics, University of Marburg, Marburg, Germany.
40
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
41
Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA.
42
Department of Bioengineering and Institute for Genomic Biology, University of Illinois, Champaign-Urbana, Illinois, USA.
43
Leiden Academic Center for Drug Research, University of Leiden, Leiden, Netherlands.
44
Izmir Institute of Technology, Izmir, Turkey.
45
Division of Biostatistics, University of Virginia School of Medicine, Charlottesville, Virginia, USA.
46
1] Korea Advanced Institute of Science and Technology, Daejeon, Korea. [2] Korea Institute of Science and Technology Information, Daejeon, Korea.
47
Buck Institute, Novato, California, USA.
48
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] CAS-MPG Partner Institute for Computational Biology, Key Laboratory of Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China.
49
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA.
50
1] Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA. [2] Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.
51
ChEMBL Group, The EMBL-European Bioinformatics Institute, Cambridge, UK.
52
Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, USA.
53
IBM Almaden Research Center, IBM Almaden Research Center, San Jose, California, USA.
54
Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
55
Bindley Bioscience Center, Purdue University, W. Lafayette, Indiana, USA.
56
Department of Animal and Avian Science, University of Maryland, College Park, Maryland, USA.
57
Embedded Systems Laboratory (ESL), Institute of Electrical Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
58
Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA.
59
The University of Colorado Cancer Center, University of Colorado School of Medicine, Aurora, Colorado, USA.
60
1] Centre for Computational Biology, Mines ParisTech, Fontainebleau, France. [2] Institut Curie, Paris, France. [3] INSERM U900, Paris, France.
61
Janssen Pharmaceutica, Beerse, Belgium.
62
Department of Biostatistics and Computational Biology, Rochester University Medical Center, Rochester, New York, USA.
63
1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. [2] Department of Statistics, Rice University, Houston, Texas, USA.

Abstract

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

PMID:
24880487
PMCID:
PMC4547623
DOI:
10.1038/nbt.2877
[Indexed for MEDLINE]
Free PMC Article

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