A community effort to assess and improve drug sensitivity prediction algorithms

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 & 113 others 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 journalArticle

    • 115 Citations

    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 languageEnglish (US)
    Pages (from-to)1202-1212
    Number of pages11
    JournalNature Biotechnology
    Volume32
    Issue number12
    DOIs
    StatePublished - Dec 1 2014

    Profile

    Datasets
    Airway Obstruction
    National Cancer Institute (U.S.)
    Epigenomics
    Proteomics
    Medicine
    Breast Neoplasms
    Gene Expression
    Cell Line
    Reverse engineering
    Microarrays
    Gene expression
    Innovation
    Cells
    Dihydroxyacetone Phosphate
    Lod Score
    Castration
    Aortic Valve Stenosis
    Canidae

    ASJC Scopus subject areas

    • Applied Microbiology and Biotechnology
    • Biotechnology
    • Molecular Medicine
    • Bioengineering
    • Biomedical Engineering

    Cite this

    Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., ... Van Westen, G. J. P. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202-1212. DOI: 10.1038/nbt.2877

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

    In: Nature Biotechnology, Vol. 32, No. 12, 01.12.2014, p. 1202-1212.

    Research output: Contribution to journalArticle

    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. DOI: 10.1038/nbt.2877
    Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology. 2014 Dec 1;32(12):1202-1212. Available from, DOI: 10.1038/nbt.2877

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

    In: Nature Biotechnology, Vol. 32, No. 12, 01.12.2014, p. 1202-1212.

    Research output: Contribution to journalArticle

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    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 = "Costello, {James C.} and Heiser, {Laura M.} and Elisabeth Georgii and Mehmet Gönen 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}",
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    AU - Menden,Michael P.

    AU - Wang,Nicholas J.

    AU - Bansal,Mukesh

    AU - Ammad-Ud-Din,Muhammad

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    AU - Mpindi,John Patrick

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    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.

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    AU - De Baets,Bernard

    AU - Deshpande,Raamesh

    AU - DiCamillo,Barbara

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    AU - Duren,Zhana

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    AU - Fang,Hongbin

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    AU - Gusev,Yuriy

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    AU - Han,Leng

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    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

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    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.

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