Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Solveig Sieberts; Fan Zhu; Javier Garcia-Garcia; Eli Stahl; Abhishek Pratap; Gaurav Pandey; Dimitrios Pappas; Daniel Aguilar; Bernat Anton; Jaume Bonet; Ridvan Eksi; Oriol Fornés; Emre Guney; Hongdong Li; Manuel Marín; Bharat Panwar; Joan Planas-Iglesias; Daniel Poglayen; Jing Cui; Andre Falcao; Christine Suver; Bruce Hoff; Venkat Balagurusamy; Donna Dillenberger; Elias Chaibub Neto; Thea Norman; Tero Aittokallio; Muhammad Ammad-ud-din; Chloe-Agathe Azencott; Víctor Bellón; Valentina Boeva; Kerstin Bunte; Himanshu Chheda; Lu Cheng; Jukka Corander; Michel Dumontier; Anna Goldenberg; Peddinti Gopalacharyulu; Mohsen Hajiloo; Daniel Hidru; Alok  Jaiswal; Samuel Kaski; Beyrem Khalfaoui; Suleiman Khan; Eric Kramer; Pekka Marttinen; Aziz Mezlini; Bhuvan Molparia; Matti Pirinen; Janna Saarela; Matthias Samwald; Véronique Stoven; Hao Tang; Jing Tang; Ali Torkamani; Jean-Philippe Vert; Bo Wang; Tao Wang; Krister Wennerberg; Nathan Wineinger; Guanghua Xiao; Yang Xie; Rae Yeung; Xiaowei Zhan; Cheng Zhao; Jeff Greenberg; Joel Kremer; Kaleb Michaud; Anne Barton; Marieke Coenen; Xavier Mariette; Corinne Miceli; Nancy Shadick; Michael Weinblatt; Niek  de Vries; Paul  Tak; Danielle  Gerlag; Tom W. J.  Huizinga; Fina  Kurreeman; Cornelia  Allaart; Stanley  Bridges; Lindsey  Criswell; Larry  Moreland; Lars  Klareskog; Saedis  Saevarsdottir; Leonid  Padyukov; Peter  Gregersen; Stephen  Friend; Robert  Plenge; Gustavo  Stolovitzky; Baldomero  Oliva; Yuanfang  Guan; Lara  Mangravite

doi:10.1038/ncomms12460

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Solveig Sieberts
, Fan Zhu
, Javier Garcia-Garcia
, Eli Stahl
, Abhishek Pratap
, Gaurav Pandey
, Dimitrios Pappas
, Daniel Aguilar
, Bernat Anton
, Jaume Bonet
, Ridvan Eksi
, Oriol Fornés
, Emre Guney
, Hongdong Li
, Manuel Marín
, Bharat Panwar
, Joan Planas-Iglesias
, Daniel Poglayen
, Jing Cui
, Andre Falcao

Christine Suver, Bruce Hoff, Venkat Balagurusamy, Donna Dillenberger, Elias Chaibub Neto, Thea Norman, Tero Aittokallio, Muhammad Ammad-ud-din, Chloe-Agathe Azencott, Víctor Bellón, Valentina Boeva, Kerstin Bunte, Himanshu Chheda, Lu Cheng, Jukka Corander, Michel Dumontier, Anna Goldenberg, Peddinti Gopalacharyulu, Mohsen Hajiloo, Daniel Hidru, Alok Jaiswal, Samuel Kaski, Beyrem Khalfaoui, Suleiman Khan, Eric Kramer, Pekka Marttinen, Aziz Mezlini, Bhuvan Molparia, Matti Pirinen, Janna Saarela, Matthias Samwald, Véronique Stoven, Hao Tang, Jing Tang, Ali Torkamani, Jean-Philippe Vert, Bo Wang, Tao Wang, Krister Wennerberg, Nathan Wineinger, Guanghua Xiao, Yang Xie, Rae Yeung, Xiaowei Zhan, Cheng Zhao, Jeff Greenberg, Joel Kremer, Kaleb Michaud, Anne Barton, Marieke Coenen, Xavier Mariette, Corinne Miceli, Nancy Shadick, Michael Weinblatt, Niek de Vries, Paul Tak, Danielle Gerlag, Tom W. J. Huizinga, Fina Kurreeman, Cornelia Allaart, Stanley Bridges, Lindsey Criswell, Larry Moreland, Lars Klareskog, Saedis Saevarsdottir, Leonid Padyukov, Peter Gregersen, Stephen Friend, Robert Plenge, Gustavo Stolovitzky, Baldomero Oliva, Yuanfang Guan, Lara Mangravite

Intelligent Systems

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Abstract

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Original language	English
Article number	12460 (2016)
Number of pages	9
Journal	Nature Communications
Volume	7
DOIs	https://doi.org/10.1038/ncomms12460
Publication status	Published - 2016

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Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritisFinal publisher's version, 329 KBLicence: CC BY

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Sieberts, S., Zhu, F., Garcia-Garcia, J., Stahl, E., Pratap, A., Pandey, G., Pappas, D., Aguilar, D., Anton, B., Bonet, J., Eksi, R., Fornés, O., Guney, E., Li, H., Marín, M., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., ... Mangravite, L. (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7, Article 12460 (2016). https://doi.org/10.1038/ncomms12460

@article{22bef760e7ed4260b0db734044b56c44,

title = "Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis",

abstract = "Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA\_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.",

author = "Solveig Sieberts and Fan Zhu and Javier Garcia-Garcia and Eli Stahl and Abhishek Pratap and Gaurav Pandey and Dimitrios Pappas and Daniel Aguilar and Bernat Anton and Jaume Bonet and Ridvan Eksi and Oriol Forn{\'e}s and Emre Guney and Hongdong Li and Manuel Mar{\'i}n and Bharat Panwar and Joan Planas-Iglesias and Daniel Poglayen and Jing Cui and Andre Falcao and Christine Suver and Bruce Hoff and Venkat Balagurusamy and Donna Dillenberger and Neto, \{Elias Chaibub\} and Thea Norman and Tero Aittokallio and Muhammad Ammad-ud-din and Chloe-Agathe Azencott and V{\'i}ctor Bell{\'o}n and Valentina Boeva and Kerstin Bunte and Himanshu Chheda and Lu Cheng and Jukka Corander and Michel Dumontier and Anna Goldenberg and Peddinti Gopalacharyulu and Mohsen Hajiloo and Daniel Hidru and Alok Jaiswal and Samuel Kaski and Beyrem Khalfaoui and Suleiman Khan and Eric Kramer and Pekka Marttinen and Aziz Mezlini and Bhuvan Molparia and Matti Pirinen and Janna Saarela and Matthias Samwald and V{\'e}ronique Stoven and Hao Tang and Jing Tang and Ali Torkamani and Jean-Philippe Vert and Bo Wang and Tao Wang and Krister Wennerberg and Nathan Wineinger and Guanghua Xiao and Yang Xie and Rae Yeung and Xiaowei Zhan and Cheng Zhao and Jeff Greenberg and Joel Kremer and Kaleb Michaud and Anne Barton and Marieke Coenen and Xavier Mariette and Corinne Miceli and Nancy Shadick and Michael Weinblatt and \{de Vries\}, Niek and Paul Tak and Danielle Gerlag and Huizinga, \{Tom W. J.\} and Fina Kurreeman and Cornelia Allaart and Stanley Bridges and Lindsey Criswell and Larry Moreland and Lars Klareskog and Saedis Saevarsdottir and Leonid Padyukov and Peter Gregersen and Stephen Friend and Robert Plenge and Gustavo Stolovitzky and Baldomero Oliva and Yuanfang Guan and Lara Mangravite",

year = "2016",

doi = "10.1038/ncomms12460",

language = "English",

volume = "7",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

}

Sieberts, S, Zhu, F, Garcia-Garcia, J, Stahl, E, Pratap, A, Pandey, G, Pappas, D, Aguilar, D, Anton, B, Bonet, J, Eksi, R, Fornés, O, Guney, E, Li, H, Marín, M, Panwar, B, Planas-Iglesias, J, Poglayen, D, Cui, J, Falcao, A, Suver, C, Hoff, B, Balagurusamy, V, Dillenberger, D, Neto, EC, Norman, T, Aittokallio, T, Ammad-ud-din, M, Azencott, C-A, Bellón, V, Boeva, V, Bunte, K, Chheda, H, Cheng, L, Corander, J, Dumontier, M, Goldenberg, A, Gopalacharyulu, P, Hajiloo, M, Hidru, D, Jaiswal, A, Kaski, S, Khalfaoui, B, Khan, S, Kramer, E, Marttinen, P, Mezlini, A, Molparia, B, Pirinen, M, Saarela, J, Samwald, M, Stoven, V, Tang, H, Tang, J, Torkamani, A, Vert, J-P, Wang, B, Wang, T, Wennerberg, K, Wineinger, N, Xiao, G, Xie, Y, Yeung, R, Zhan, X, Zhao, C, Greenberg, J, Kremer, J, Michaud, K, Barton, A, Coenen, M, Mariette, X, Miceli, C, Shadick, N, Weinblatt, M, de Vries, N, Tak, P, Gerlag, D, Huizinga, TWJ, Kurreeman, F, Allaart, C, Bridges, S, Criswell, L, Moreland, L, Klareskog, L, Saevarsdottir, S, Padyukov, L, Gregersen, P, Friend, S, Plenge, R, Stolovitzky, G, Oliva, B, Guan, Y & Mangravite, L 2016, 'Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis', Nature Communications, vol. 7, 12460 (2016). https://doi.org/10.1038/ncomms12460

TY - JOUR

T1 - Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

AU - Sieberts, Solveig

AU - Zhu, Fan

AU - Garcia-Garcia, Javier

AU - Stahl, Eli

AU - Pratap, Abhishek

AU - Pandey, Gaurav

AU - Pappas, Dimitrios

AU - Aguilar, Daniel

AU - Anton, Bernat

AU - Bonet, Jaume

AU - Eksi, Ridvan

AU - Fornés, Oriol

AU - Guney, Emre

AU - Li, Hongdong

AU - Marín, Manuel

AU - Panwar, Bharat

AU - Planas-Iglesias, Joan

AU - Poglayen, Daniel

AU - Cui, Jing

AU - Falcao, Andre

AU - Suver, Christine

AU - Hoff, Bruce

AU - Balagurusamy, Venkat

AU - Dillenberger, Donna

AU - Neto, Elias Chaibub

AU - Norman, Thea

AU - Aittokallio, Tero

AU - Ammad-ud-din, Muhammad

AU - Azencott, Chloe-Agathe

AU - Bellón, Víctor

AU - Boeva, Valentina

AU - Bunte, Kerstin

AU - Chheda, Himanshu

AU - Cheng, Lu

AU - Corander, Jukka

AU - Dumontier, Michel

AU - Goldenberg, Anna

AU - Gopalacharyulu, Peddinti

AU - Hajiloo, Mohsen

AU - Hidru, Daniel

AU - Jaiswal, Alok

AU - Kaski, Samuel

AU - Khalfaoui, Beyrem

AU - Khan, Suleiman

AU - Kramer, Eric

AU - Marttinen, Pekka

AU - Mezlini, Aziz

AU - Molparia, Bhuvan

AU - Pirinen, Matti

AU - Saarela, Janna

AU - Samwald, Matthias

AU - Stoven, Véronique

AU - Tang, Hao

AU - Tang, Jing

AU - Torkamani, Ali

AU - Vert, Jean-Philippe

AU - Wang, Bo

AU - Wang, Tao

AU - Wennerberg, Krister

AU - Wineinger, Nathan

AU - Xiao, Guanghua

AU - Xie, Yang

AU - Yeung, Rae

AU - Zhan, Xiaowei

AU - Zhao, Cheng

AU - Greenberg, Jeff

AU - Kremer, Joel

AU - Michaud, Kaleb

AU - Barton, Anne

AU - Coenen, Marieke

AU - Mariette, Xavier

AU - Miceli, Corinne

AU - Shadick, Nancy

AU - Weinblatt, Michael

AU - de Vries, Niek

AU - Tak, Paul

AU - Gerlag, Danielle

AU - Huizinga, Tom W. J.

AU - Kurreeman, Fina

AU - Allaart, Cornelia

AU - Bridges, Stanley

AU - Criswell, Lindsey

AU - Moreland, Larry

AU - Klareskog, Lars

AU - Saevarsdottir, Saedis

AU - Padyukov, Leonid

AU - Gregersen, Peter

AU - Friend, Stephen

AU - Plenge, Robert

AU - Stolovitzky, Gustavo

AU - Oliva, Baldomero

AU - Guan, Yuanfang

AU - Mangravite, Lara

PY - 2016

Y1 - 2016

N2 - Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

AB - Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

U2 - 10.1038/ncomms12460

DO - 10.1038/ncomms12460

M3 - Article

C2 - 27549343

SN - 2041-1723

VL - 7

JO - Nature Communications

JF - Nature Communications

M1 - 12460 (2016)

ER -

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

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