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

Solveig K. Sieberts; Fan Zhu; Javier García-García; Eli Stahl; Abhishek Pratap; Gaurav Pandey; Dimitrios Pappas; Daniel Aguilar; Bernat Anton; Jaume Bonet; Ridvan Eksi; Oriol Fornés; Emre Guney; Hongdong Li; Manuel Alejandro Marín; Bharat Panwar; Joan Planas-Iglesias; Daniel Poglayen; Jing Cui; Andre O. Falcao; Christine Suver; Bruce Hoff; Venkat S.K. 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 Ali Khan; Eric R. Kramer; Pekka Marttinen; Aziz M. Mezlini; Bhuvan Molparia; Matti Pirinen; Janna Saarela; Rheumatoid Arthritis Challenge Consortium

doi:10.1038/ncomms12460

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

Solveig K. Sieberts, Fan Zhu, Javier García-García, Eli Stahl, Abhishek Pratap, Gaurav Pandey, Dimitrios Pappas, Daniel Aguilar, Bernat Anton, Jaume Bonet, Ridvan Eksi, Oriol Fornés, Emre Guney, Hongdong Li, Manuel Alejandro Marín, Bharat Panwar, Joan Planas-Iglesias, Daniel Poglayen, Jing Cui, Andre O. FalcaoChristine Suver, Bruce Hoff, Venkat S.K. 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 Ali Khan, Eric R. Kramer, Pekka Marttinen, Aziz M. Mezlini, Bhuvan Molparia, Matti Pirinen, Janna Saarela, Rheumatoid Arthritis Challenge Consortium

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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 1/4one-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 (h 2 =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
Journal	Nature Communications
Volume	7
DOIs	https://doi.org/10.1038/ncomms12460
Publication status	Published - 23 Aug 2016
MoE publication type	A1 Journal article-refereed

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Sieberts, S. K., Zhu, F., García-García, 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. A., Panwar, B., Planas-Iglesias, J., Poglayen, D., Cui, J., ... Rheumatoid Arthritis Challenge Consortium (2016). Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nature Communications, 7, Article 12460. https://doi.org/10.1038/ncomms12460

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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 1/4one-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 (h 2 =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 = "Sieberts, {Solveig K.} and Fan Zhu and Javier Garc{\'i}a-Garc{\'i}a 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 Mar{\'i}n, {Manuel Alejandro} and Bharat Panwar and Joan Planas-Iglesias and Daniel Poglayen and Jing Cui and Falcao, {Andre O.} and Christine Suver and Bruce Hoff and Balagurusamy, {Venkat S.K.} and Donna Dillenberger and Neto, {Elias Chaibub} and Thea Norman and Tero Aittokallio and Muhammad Ammad-Ud-Din and Azencott, {Chloe Agathe} 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 Khan, {Suleiman Ali} and Kramer, {Eric R.} and Pekka Marttinen and Mezlini, {Aziz M.} and Bhuvan Molparia and Matti Pirinen and Janna Saarela and {Rheumatoid Arthritis Challenge Consortium}",

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Sieberts, SK, Zhu, F, García-García, J, Stahl, E, Pratap, A, Pandey, G, Pappas, D, Aguilar, D, Anton, B, Bonet, J, Eksi, R, Fornés, O, Guney, E, Li, H, Marín, MA, Panwar, B, Planas-Iglesias, J, Poglayen, D, Cui, J, Falcao, AO, Suver, C, Hoff, B, Balagurusamy, VSK, Dillenberger, D, Neto, EC, Norman, T, Aittokallio, T, Ammad-Ud-Din, M, Azencott, CA, 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, SA, Kramer, ER, Marttinen, P, Mezlini, AM, Molparia, B, Pirinen, M, Saarela, J & Rheumatoid Arthritis Challenge Consortium 2016, 'Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis', Nature Communications, vol. 7, 12460. 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 K.

AU - Zhu, Fan

AU - García-García, 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 Alejandro

AU - Panwar, Bharat

AU - Planas-Iglesias, Joan

AU - Poglayen, Daniel

AU - Cui, Jing

AU - Falcao, Andre O.

AU - Suver, Christine

AU - Hoff, Bruce

AU - Balagurusamy, Venkat S.K.

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 Ali

AU - Kramer, Eric R.

AU - Marttinen, Pekka

AU - Mezlini, Aziz M.

AU - Molparia, Bhuvan

AU - Pirinen, Matti

AU - Saarela, Janna

AU - Rheumatoid Arthritis Challenge Consortium

PY - 2016/8/23

Y1 - 2016/8/23

N2 - Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in 1/4one-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 (h 2 =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 1/4one-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 (h 2 =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.

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U2 - 10.1038/ncomms12460

DO - 10.1038/ncomms12460

M3 - Article

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SN - 2041-1723

VL - 7

JO - Nature Communications

JF - Nature Communications

M1 - 12460

ER -

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

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