A community effort to assess and improve drug sensitivity prediction algorithms

NCI-DREAM Community; James C. Costello; Laura M. Heiser; Elisabeth Georgii; Mehmet Gönen; Michael P. Menden; Nicholas J. Wang; Mukesh Bansal; Muhammad Ammad-Ud-Din; Petteri Hintsanen; Suleiman A. Khan; John Patrick Mpindi; Olli Kallioniemi; Antti Honkela; Tero Aittokallio; Krister Wennerberg; James J. Collins; Dan Gallahan; Dinah Singer; Julio Saez-Rodriguez; Samuel Kaski; Joe W. Gray; Gustavo Stolovitzky; Jean Paul Abbuehl; Jeffrey Allen; Russ B. Altman; Shawn Balcome; Alexis Battle; Andreas Bender; Bonnie Berger; Jonathan Bernard; Madhuchhanda Bhattacharjee; Krithika Bhuvaneshwar; Andrew A. Bieberich; Fred Boehm; Andrea Califano; Christina Chan; Beibei Chen; Ting Huei Chen; Jaejoon Choi; Luis Pedro Coelho; Thomas Cokelaer; James C. Collins; Chad J. Creighton; Jike Cui; Will Dampier; V. Jo Davisson; Bernard De Baets; Raamesh Deshpande; Barbara DiCamillo; Murat Dundar; Zhana Duren; Adam Ertel; Haoyang Fan; Hongbin Fang; Robinder Gauba; Assaf Gottlieb; Michael Grau; Yuriy Gusev; Min Jin Ha; Leng Han; Michael Harris; Nicholas Henderson; Hussein A. Hejase; Krisztian Homicsko; Jack P. Hou; Woochang Hwang; Adriaan P. IJzerman; Bilge Karacali; Sunduz Keles; Christina Kendziorski; Junho Kim; Min Kim; Youngchul Kim; David A. Knowles; Daphne Koller; Junehawk Lee; Jae K. Lee; Eelke B. Lenselink; Biao Li; Bin Li; Jun Li; Han Liang; Jian Ma; Subha Madhavan; Sean Mooney; Chad L. Myers; Michael A. Newton; John P. Overington; Ranadip Pal; Jian Peng; Richard Pestell; Robert J. Prill; Peng Qiu; Bartek Rajwa; Anguraj Sadanandam; Francesco Sambo; Hyunjin Shin; Jiuzhou Song; Lei Song; Arvind Sridhar; Michiel Stock; Wei Sun; Tram Ta; Mahlet Tadesse; Ming Tan; Hao Tang; Dan Theodorescu; Gianna Maria Toffolo; Aydin Tozeren; William Trepicchio; Nelle Varoquaux; Jean Philippe Vert; Willem Waegeman; Thomas Walter; Qian Wan; Difei Wang; Wen Wang; Yong Wang; Zhishi Wang; Joerg K. Wegner; Tongtong Wu; Tian Xia; Guanghua Xiao; Yang Xie; Yanxun Xu; Jichen Yang; Yuan Yuan; Shihua Zhang; Xiang Sun Zhang; Junfei Zhao; Chandler Zuo; Herman W.T. Van Vlijmen; Gerard J.P. Van Westen

doi:10.1038/nbt.2877

A community effort to assess and improve drug sensitivity prediction algorithms

NCI-DREAM Community, James C. Costello, Laura M. Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P. Menden, Nicholas J. Wang, Mukesh Bansal, Muhammad Ammad-Ud-Din, Petteri Hintsanen, Suleiman A. Khan, John Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, James J. Collins, Dan Gallahan, Dinah Singer, Julio Saez-RodriguezSamuel Kaski, Joe W. Gray, Gustavo Stolovitzky, Jean Paul Abbuehl, Jeffrey Allen, Russ B. Altman, Shawn Balcome, Alexis Battle, Andreas Bender, Bonnie Berger, Jonathan Bernard, Madhuchhanda Bhattacharjee, Krithika Bhuvaneshwar, Andrew A. Bieberich, Fred Boehm, Andrea Califano, Christina Chan, Beibei Chen, Ting Huei Chen, Jaejoon Choi, Luis Pedro Coelho, Thomas Cokelaer, James C. Collins, Chad J. Creighton, Jike Cui, Will Dampier, V. Jo Davisson, Bernard De Baets, Raamesh Deshpande, Barbara DiCamillo, Murat Dundar, Zhana Duren, Adam Ertel, Haoyang Fan, Hongbin Fang, Robinder Gauba, Assaf Gottlieb, Michael Grau, Yuriy Gusev, Min Jin Ha, Leng Han, Michael Harris, Nicholas Henderson, Hussein A. Hejase, Krisztian Homicsko, Jack P. Hou, Woochang Hwang, Adriaan P. IJzerman, Bilge Karacali, Sunduz Keles, Christina Kendziorski, Junho Kim, Min Kim, Youngchul Kim, David A. Knowles, Daphne Koller, Junehawk Lee, Jae K. Lee, Eelke B. Lenselink, Biao Li, Bin Li, Jun Li, Han Liang, Jian Ma, Subha Madhavan, Sean Mooney, Chad L. Myers, Michael A. Newton, John P. Overington, Ranadip Pal, Jian Peng, Richard Pestell, Robert J. Prill, Peng Qiu, Bartek Rajwa, Anguraj Sadanandam, Francesco Sambo, Hyunjin Shin, Jiuzhou Song, Lei Song, Arvind Sridhar, Michiel Stock, Wei Sun, Tram Ta, Mahlet Tadesse, Ming Tan, Hao Tang, Dan Theodorescu, Gianna Maria Toffolo, Aydin Tozeren, William Trepicchio, Nelle Varoquaux, Jean Philippe Vert, Willem Waegeman, Thomas Walter, Qian Wan, Difei Wang, Wen Wang, Yong Wang, Zhishi Wang, Joerg K. Wegner, Tongtong Wu, Tian Xia, Guanghua Xiao, Yang Xie, Yanxun Xu, Jichen Yang, Yuan Yuan, Shihua Zhang, Xiang Sun Zhang, Junfei Zhao, Chandler Zuo, Herman W.T. Van Vlijmen, Gerard J.P. Van Westen

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

565 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1202-1212
Number of pages	11
Journal	Nature Biotechnology
Volume	32
Issue number	12
DOIs	https://doi.org/10.1038/nbt.2877
Publication status	Published - 1 Dec 2014
Externally published	Yes

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

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10.1038/nbt.2877

Cite this

NCI-DREAM Community, Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-Ud-Din, M., Hintsanen, P., Khan, S. A., Mpindi, J. P., Kallioniemi, O., Honkela, A., Aittokallio, T., Wennerberg, K., Collins, J. J., Gallahan, D., Singer, D., ... Van Westen, G. J. P. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202-1212. https://doi.org/10.1038/nbt.2877

@article{8ab65add7887497ea48d72746c1dd745,

title = "A community effort to assess and improve drug sensitivity prediction algorithms",

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.",

author = "{NCI-DREAM Community} and Costello, {James C.} and Heiser, {Laura M.} and Elisabeth Georgii and Mehmet G{\"o}nen and Menden, {Michael P.} and Wang, {Nicholas J.} and Mukesh Bansal and Muhammad Ammad-Ud-Din and Petteri Hintsanen and Khan, {Suleiman A.} and Mpindi, {John Patrick} and Olli Kallioniemi and Antti Honkela and Tero Aittokallio and Krister Wennerberg and Collins, {James J.} and Dan Gallahan and Dinah Singer and Julio Saez-Rodriguez and Samuel Kaski and Gray, {Joe W.} and Gustavo Stolovitzky and Abbuehl, {Jean Paul} and Jeffrey Allen and Altman, {Russ B.} and Shawn Balcome and Alexis Battle and Andreas Bender and Bonnie Berger and Jonathan Bernard and Madhuchhanda Bhattacharjee and Krithika Bhuvaneshwar and Bieberich, {Andrew A.} and Fred Boehm and Andrea Califano and Christina Chan and Beibei Chen and Chen, {Ting Huei} and Jaejoon Choi and Coelho, {Luis Pedro} and Thomas Cokelaer and Collins, {James C.} and Creighton, {Chad J.} and Jike Cui and Will Dampier and Davisson, {V. Jo} and {De Baets}, Bernard and Raamesh Deshpande and Barbara DiCamillo and Murat Dundar and Zhana Duren and Adam Ertel and Haoyang Fan and Hongbin Fang and Robinder Gauba and Assaf Gottlieb and Michael Grau and Yuriy Gusev and Ha, {Min Jin} and Leng Han and Michael Harris and Nicholas Henderson and Hejase, {Hussein A.} and Krisztian Homicsko and Hou, {Jack P.} and Woochang Hwang and IJzerman, {Adriaan P.} and Bilge Karacali and Sunduz Keles and Christina Kendziorski and Junho Kim and Min Kim and Youngchul Kim and Knowles, {David A.} and Daphne Koller and Junehawk Lee and Lee, {Jae K.} and Lenselink, {Eelke B.} and Biao Li and Bin Li and Jun Li and Han Liang and Jian Ma and Subha Madhavan and Sean Mooney and Myers, {Chad L.} and Newton, {Michael A.} and Overington, {John P.} and Ranadip Pal and Jian Peng and Richard Pestell and Prill, {Robert J.} and Peng Qiu and Bartek Rajwa and Anguraj Sadanandam and Francesco Sambo and Hyunjin Shin and Jiuzhou Song and Lei Song and Arvind Sridhar and Michiel Stock and Wei Sun and Tram Ta and Mahlet Tadesse and Ming Tan and Hao Tang and Dan Theodorescu and Toffolo, {Gianna Maria} and Aydin Tozeren and William Trepicchio and Nelle Varoquaux and Vert, {Jean Philippe} and Willem Waegeman and Thomas Walter and Qian Wan and Difei Wang and Wen Wang and Yong Wang and Zhishi Wang and Wegner, {Joerg K.} and Tongtong Wu and Tian Xia and Guanghua Xiao and Yang Xie and Yanxun Xu and Jichen Yang and Yuan Yuan and Shihua Zhang and Zhang, {Xiang Sun} and Junfei Zhao and Chandler Zuo and {Van Vlijmen}, {Herman W.T.} and {Van Westen}, {Gerard J.P.}",

year = "2014",

month = dec,

day = "1",

doi = "10.1038/nbt.2877",

language = "English",

volume = "32",

pages = "1202--1212",

journal = "Nature Biotechnology",

issn = "1087-0156",

publisher = "Nature Publishing Group",

number = "12",

}

NCI-DREAM Community, Costello, JC, Heiser, LM, Georgii, E, Gönen, M, Menden, MP, Wang, NJ, Bansal, M, Ammad-Ud-Din, M, Hintsanen, P, Khan, SA, Mpindi, JP, Kallioniemi, O, Honkela, A, Aittokallio, T, Wennerberg, K, Collins, JJ, Gallahan, D, Singer, D, Saez-Rodriguez, J, Kaski, S, Gray, JW, Stolovitzky, G, Abbuehl, JP, Allen, J, Altman, RB, Balcome, S, Battle, A, Bender, A, Berger, B, Bernard, J, Bhattacharjee, M, Bhuvaneshwar, K, Bieberich, AA, Boehm, F, Califano, A, Chan, C, Chen, B, Chen, TH, Choi, J, Coelho, LP, Cokelaer, T, Collins, JC, Creighton, CJ, Cui, J, Dampier, W, Davisson, VJ, De Baets, B, Deshpande, R, DiCamillo, B, Dundar, M, Duren, Z, Ertel, A, Fan, H, Fang, H, Gauba, R, Gottlieb, A, Grau, M, Gusev, Y, Ha, MJ, Han, L, Harris, M, Henderson, N, Hejase, HA, Homicsko, K, Hou, JP, Hwang, W, IJzerman, AP, Karacali, B, Keles, S, Kendziorski, C, Kim, J, Kim, M, Kim, Y, Knowles, DA, Koller, D, Lee, J, Lee, JK, Lenselink, EB, Li, B, Li, B, Li, J, Liang, H, Ma, J, Madhavan, S, Mooney, S, Myers, CL, Newton, MA, Overington, JP, Pal, R, Peng, J, Pestell, R, Prill, RJ, Qiu, P, Rajwa, B, Sadanandam, A, Sambo, F, Shin, H, Song, J, Song, L, Sridhar, A, Stock, M, Sun, W, Ta, T, Tadesse, M, Tan, M, Tang, H, Theodorescu, D, Toffolo, GM, Tozeren, A, Trepicchio, W, Varoquaux, N, Vert, JP, Waegeman, W, Walter, T, Wan, Q, Wang, D, Wang, W, Wang, Y, Wang, Z, Wegner, JK, Wu, T, Xia, T, Xiao, G, Xie, Y, Xu, Y, Yang, J, Yuan, Y, Zhang, S, Zhang, XS, Zhao, J, Zuo, C, Van Vlijmen, HWT & Van Westen, GJP 2014, 'A community effort to assess and improve drug sensitivity prediction algorithms', Nature Biotechnology, vol. 32, no. 12, pp. 1202-1212. https://doi.org/10.1038/nbt.2877

TY - JOUR

T1 - A community effort to assess and improve drug sensitivity prediction algorithms

AU - NCI-DREAM Community

AU - Costello, James C.

AU - Heiser, Laura M.

AU - Georgii, Elisabeth

AU - Gönen, Mehmet

AU - Menden, Michael P.

AU - Wang, Nicholas J.

AU - Bansal, Mukesh

AU - Ammad-Ud-Din, Muhammad

AU - Hintsanen, Petteri

AU - Khan, Suleiman A.

AU - Mpindi, John Patrick

AU - Kallioniemi, Olli

AU - Honkela, Antti

AU - Aittokallio, Tero

AU - Wennerberg, Krister

AU - Collins, James J.

AU - Gallahan, Dan

AU - Singer, Dinah

AU - Saez-Rodriguez, Julio

AU - Kaski, Samuel

AU - Gray, Joe W.

AU - Stolovitzky, Gustavo

AU - Abbuehl, Jean Paul

AU - Allen, Jeffrey

AU - Altman, Russ B.

AU - Balcome, Shawn

AU - Battle, Alexis

AU - Bender, Andreas

AU - Berger, Bonnie

AU - Bernard, Jonathan

AU - Bhattacharjee, Madhuchhanda

AU - Bhuvaneshwar, Krithika

AU - Bieberich, Andrew A.

AU - Boehm, Fred

AU - Califano, Andrea

AU - Chan, Christina

AU - Chen, Beibei

AU - Chen, Ting Huei

AU - Choi, Jaejoon

AU - Coelho, Luis Pedro

AU - Cokelaer, Thomas

AU - Collins, James C.

AU - Creighton, Chad J.

AU - Cui, Jike

AU - Dampier, Will

AU - Davisson, V. Jo

AU - De Baets, Bernard

AU - Deshpande, Raamesh

AU - DiCamillo, Barbara

AU - Dundar, Murat

AU - Duren, Zhana

AU - Ertel, Adam

AU - Fan, Haoyang

AU - Fang, Hongbin

AU - Gauba, Robinder

AU - Gottlieb, Assaf

AU - Grau, Michael

AU - Gusev, Yuriy

AU - Ha, Min Jin

AU - Han, Leng

AU - Harris, Michael

AU - Henderson, Nicholas

AU - Hejase, Hussein A.

AU - Homicsko, Krisztian

AU - Hou, Jack P.

AU - Hwang, Woochang

AU - IJzerman, Adriaan P.

AU - Karacali, Bilge

AU - Keles, Sunduz

AU - Kendziorski, Christina

AU - Kim, Junho

AU - Kim, Min

AU - Kim, Youngchul

AU - Knowles, David A.

AU - Koller, Daphne

AU - Lee, Junehawk

AU - Lee, Jae K.

AU - Lenselink, Eelke B.

AU - Li, Biao

AU - Li, Bin

AU - Li, Jun

AU - Liang, Han

AU - Ma, Jian

AU - Madhavan, Subha

AU - Mooney, Sean

AU - Myers, Chad L.

AU - Newton, Michael A.

AU - Overington, John P.

AU - Pal, Ranadip

AU - Peng, Jian

AU - Pestell, Richard

AU - Prill, Robert J.

AU - Qiu, Peng

AU - Rajwa, Bartek

AU - Sadanandam, Anguraj

AU - Sambo, Francesco

AU - Shin, Hyunjin

AU - Song, Jiuzhou

AU - Song, Lei

AU - Sridhar, Arvind

AU - Stock, Michiel

AU - Sun, Wei

AU - Ta, Tram

AU - Tadesse, Mahlet

AU - Tan, Ming

AU - Tang, Hao

AU - Theodorescu, Dan

AU - Toffolo, Gianna Maria

AU - Tozeren, Aydin

AU - Trepicchio, William

AU - Varoquaux, Nelle

AU - Vert, Jean Philippe

AU - Waegeman, Willem

AU - Walter, Thomas

AU - Wan, Qian

AU - Wang, Difei

AU - Wang, Wen

AU - Wang, Yong

AU - Wang, Zhishi

AU - Wegner, Joerg K.

AU - Wu, Tongtong

AU - Xia, Tian

AU - Xiao, Guanghua

AU - Xie, Yang

AU - Xu, Yanxun

AU - Yang, Jichen

AU - Yuan, Yuan

AU - Zhang, Shihua

AU - Zhang, Xiang Sun

AU - Zhao, Junfei

AU - Zuo, Chandler

AU - Van Vlijmen, Herman W.T.

AU - Van Westen, Gerard J.P.

PY - 2014/12/1

Y1 - 2014/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84906549588&partnerID=8YFLogxK

U2 - 10.1038/nbt.2877

DO - 10.1038/nbt.2877

M3 - Article

C2 - 24880487

AN - SCOPUS:84906549588

SN - 1087-0156

VL - 32

SP - 1202

EP - 1212

JO - Nature Biotechnology

JF - Nature Biotechnology

IS - 12

ER -

A community effort to assess and improve drug sensitivity prediction algorithms

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