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

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...

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Main Authors:	Sieberts, Solveig K., Zhu, Fan, García-García, Javier, Stahl, Eli, Pratap, Abhishek, Pandey, Gaurav, Pappas, Dimitrios, Aguilar, Daniel, Anton, Bernat, Bonet, Jaume, Eksi, Ridvan, Fornés, Oriol, Guney, Emre, Li, Hongdong, Marín, Manuel Alejandro, Panwar, Bharat, Planas-Iglesias, Joan, Poglayen, Daniel, Cui, Jing, Falcao, Andre O., Suver, Christine, Hoff, Bruce, Balagurusamy, Venkat S. K., Dillenberger, Donna, Neto, Elias Chaibub, Norman, Thea, Aittokallio, Tero, Ammad-ud-din, Muhammad, Azencott, Chloe-Agathe, Bellón, Víctor, Boeva, Valentina, Bunte, Kerstin, Chheda, Himanshu, Cheng, Lu, Corander, Jukka, Dumontier, Michel, Goldenberg, Anna, Gopalacharyulu, Peddinti, Hajiloo, Mohsen, Hidru, Daniel, Jaiswal, Alok, Kaski, Samuel, Khalfaoui, Beyrem, Khan, Suleiman Ali, Kramer, Eric R., Marttinen, Pekka, Mezlini, Aziz M., Molparia, Bhuvan, Pirinen, Matti, Saarela, Janna, Samwald, Matthias, Stoven, Véronique, Tang, Hao, Tang, Jing, Torkamani, Ali, Vert, Jean-Phillipe, Wang, Bo, Wang, Tao, Wennerberg, Krister, Wineinger, Nathan E., Xiao, Guanghua, Xie, Yang, Yeung, Rae, Zhan, Xiaowei, Zhao, Cheng, Greenberg, Jeff, Kremer, Joel, Michaud, Kaleb, Barton, Anne, Coenen, Marieke, Mariette, Xavier, Miceli, Corinne, Shadick, Nancy, Weinblatt, Michael, de Vries, Niek, Tak, Paul P., Gerlag, Danielle, Huizinga, Tom W. J., Kurreeman, Fina, Allaart, Cornelia F., Louis Bridges Jr., S., Criswell, Lindsey, Moreland, Larry, Klareskog, Lars, Saevarsdottir, Saedis, Padyukov, Leonid, Gregersen, Peter K., Friend, Stephen, Plenge, Robert, Stolovitzky, Gustavo, Oliva, Baldo, Guan, Yuanfang, Mangravite, Lara M.
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996969/

id	pubmed-4996969
recordtype	oai_dc
spelling	pubmed-49969692016-09-07 Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis Sieberts, Solveig K. Zhu, Fan García-García, Javier Stahl, Eli Pratap, Abhishek Pandey, Gaurav Pappas, Dimitrios Aguilar, Daniel Anton, Bernat Bonet, Jaume Eksi, Ridvan Fornés, Oriol Guney, Emre Li, Hongdong Marín, Manuel Alejandro Panwar, Bharat Planas-Iglesias, Joan Poglayen, Daniel Cui, Jing Falcao, Andre O. Suver, Christine Hoff, Bruce Balagurusamy, Venkat S. K. Dillenberger, Donna Neto, Elias Chaibub Norman, Thea Aittokallio, Tero Ammad-ud-din, Muhammad Azencott, Chloe-Agathe Bellón, Víctor Boeva, Valentina Bunte, Kerstin Chheda, Himanshu Cheng, Lu Corander, Jukka Dumontier, Michel Goldenberg, Anna Gopalacharyulu, Peddinti Hajiloo, Mohsen Hidru, Daniel Jaiswal, Alok Kaski, Samuel Khalfaoui, Beyrem Khan, Suleiman Ali Kramer, Eric R. Marttinen, Pekka Mezlini, Aziz M. Molparia, Bhuvan Pirinen, Matti Saarela, Janna Samwald, Matthias Stoven, Véronique Tang, Hao Tang, Jing Torkamani, Ali Vert, Jean-Phillipe Wang, Bo Wang, Tao Wennerberg, Krister Wineinger, Nathan E. Xiao, Guanghua Xie, Yang Yeung, Rae Zhan, Xiaowei Zhao, Cheng Greenberg, Jeff Kremer, Joel Michaud, Kaleb Barton, Anne Coenen, Marieke Mariette, Xavier Miceli, Corinne Shadick, Nancy Weinblatt, Michael de Vries, Niek Tak, Paul P. Gerlag, Danielle Huizinga, Tom W. J. Kurreeman, Fina Allaart, Cornelia F. Louis Bridges Jr., S. Criswell, Lindsey Moreland, Larry Klareskog, Lars Saevarsdottir, Saedis Padyukov, Leonid Gregersen, Peter K. Friend, Stephen Plenge, Robert Stolovitzky, Gustavo Oliva, Baldo Guan, Yuanfang Mangravite, Lara M. Article 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. Nature Publishing Group 2016-08-23 /pmc/articles/PMC4996969/ /pubmed/27549343 http://dx.doi.org/10.1038/ncomms12460 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Sieberts, Solveig K. Zhu, Fan García-García, Javier Stahl, Eli Pratap, Abhishek Pandey, Gaurav Pappas, Dimitrios Aguilar, Daniel Anton, Bernat Bonet, Jaume Eksi, Ridvan Fornés, Oriol Guney, Emre Li, Hongdong Marín, Manuel Alejandro Panwar, Bharat Planas-Iglesias, Joan Poglayen, Daniel Cui, Jing Falcao, Andre O. Suver, Christine Hoff, Bruce Balagurusamy, Venkat S. K. Dillenberger, Donna Neto, Elias Chaibub Norman, Thea Aittokallio, Tero Ammad-ud-din, Muhammad Azencott, Chloe-Agathe Bellón, Víctor Boeva, Valentina Bunte, Kerstin Chheda, Himanshu Cheng, Lu Corander, Jukka Dumontier, Michel Goldenberg, Anna Gopalacharyulu, Peddinti Hajiloo, Mohsen Hidru, Daniel Jaiswal, Alok Kaski, Samuel Khalfaoui, Beyrem Khan, Suleiman Ali Kramer, Eric R. Marttinen, Pekka Mezlini, Aziz M. Molparia, Bhuvan Pirinen, Matti Saarela, Janna Samwald, Matthias Stoven, Véronique Tang, Hao Tang, Jing Torkamani, Ali Vert, Jean-Phillipe Wang, Bo Wang, Tao Wennerberg, Krister Wineinger, Nathan E. Xiao, Guanghua Xie, Yang Yeung, Rae Zhan, Xiaowei Zhao, Cheng Greenberg, Jeff Kremer, Joel Michaud, Kaleb Barton, Anne Coenen, Marieke Mariette, Xavier Miceli, Corinne Shadick, Nancy Weinblatt, Michael de Vries, Niek Tak, Paul P. Gerlag, Danielle Huizinga, Tom W. J. Kurreeman, Fina Allaart, Cornelia F. Louis Bridges Jr., S. Criswell, Lindsey Moreland, Larry Klareskog, Lars Saevarsdottir, Saedis Padyukov, Leonid Gregersen, Peter K. Friend, Stephen Plenge, Robert Stolovitzky, Gustavo Oliva, Baldo Guan, Yuanfang Mangravite, Lara M.
spellingShingle	Sieberts, Solveig K. Zhu, Fan García-García, Javier Stahl, Eli Pratap, Abhishek Pandey, Gaurav Pappas, Dimitrios Aguilar, Daniel Anton, Bernat Bonet, Jaume Eksi, Ridvan Fornés, Oriol Guney, Emre Li, Hongdong Marín, Manuel Alejandro Panwar, Bharat Planas-Iglesias, Joan Poglayen, Daniel Cui, Jing Falcao, Andre O. Suver, Christine Hoff, Bruce Balagurusamy, Venkat S. K. Dillenberger, Donna Neto, Elias Chaibub Norman, Thea Aittokallio, Tero Ammad-ud-din, Muhammad Azencott, Chloe-Agathe Bellón, Víctor Boeva, Valentina Bunte, Kerstin Chheda, Himanshu Cheng, Lu Corander, Jukka Dumontier, Michel Goldenberg, Anna Gopalacharyulu, Peddinti Hajiloo, Mohsen Hidru, Daniel Jaiswal, Alok Kaski, Samuel Khalfaoui, Beyrem Khan, Suleiman Ali Kramer, Eric R. Marttinen, Pekka Mezlini, Aziz M. Molparia, Bhuvan Pirinen, Matti Saarela, Janna Samwald, Matthias Stoven, Véronique Tang, Hao Tang, Jing Torkamani, Ali Vert, Jean-Phillipe Wang, Bo Wang, Tao Wennerberg, Krister Wineinger, Nathan E. Xiao, Guanghua Xie, Yang Yeung, Rae Zhan, Xiaowei Zhao, Cheng Greenberg, Jeff Kremer, Joel Michaud, Kaleb Barton, Anne Coenen, Marieke Mariette, Xavier Miceli, Corinne Shadick, Nancy Weinblatt, Michael de Vries, Niek Tak, Paul P. Gerlag, Danielle Huizinga, Tom W. J. Kurreeman, Fina Allaart, Cornelia F. Louis Bridges Jr., S. Criswell, Lindsey Moreland, Larry Klareskog, Lars Saevarsdottir, Saedis Padyukov, Leonid Gregersen, Peter K. Friend, Stephen Plenge, Robert Stolovitzky, Gustavo Oliva, Baldo Guan, Yuanfang Mangravite, Lara M. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
author_facet	Sieberts, Solveig K. Zhu, Fan García-García, Javier Stahl, Eli Pratap, Abhishek Pandey, Gaurav Pappas, Dimitrios Aguilar, Daniel Anton, Bernat Bonet, Jaume Eksi, Ridvan Fornés, Oriol Guney, Emre Li, Hongdong Marín, Manuel Alejandro Panwar, Bharat Planas-Iglesias, Joan Poglayen, Daniel Cui, Jing Falcao, Andre O. Suver, Christine Hoff, Bruce Balagurusamy, Venkat S. K. Dillenberger, Donna Neto, Elias Chaibub Norman, Thea Aittokallio, Tero Ammad-ud-din, Muhammad Azencott, Chloe-Agathe Bellón, Víctor Boeva, Valentina Bunte, Kerstin Chheda, Himanshu Cheng, Lu Corander, Jukka Dumontier, Michel Goldenberg, Anna Gopalacharyulu, Peddinti Hajiloo, Mohsen Hidru, Daniel Jaiswal, Alok Kaski, Samuel Khalfaoui, Beyrem Khan, Suleiman Ali Kramer, Eric R. Marttinen, Pekka Mezlini, Aziz M. Molparia, Bhuvan Pirinen, Matti Saarela, Janna Samwald, Matthias Stoven, Véronique Tang, Hao Tang, Jing Torkamani, Ali Vert, Jean-Phillipe Wang, Bo Wang, Tao Wennerberg, Krister Wineinger, Nathan E. Xiao, Guanghua Xie, Yang Yeung, Rae Zhan, Xiaowei Zhao, Cheng Greenberg, Jeff Kremer, Joel Michaud, Kaleb Barton, Anne Coenen, Marieke Mariette, Xavier Miceli, Corinne Shadick, Nancy Weinblatt, Michael de Vries, Niek Tak, Paul P. Gerlag, Danielle Huizinga, Tom W. J. Kurreeman, Fina Allaart, Cornelia F. Louis Bridges Jr., S. Criswell, Lindsey Moreland, Larry Klareskog, Lars Saevarsdottir, Saedis Padyukov, Leonid Gregersen, Peter K. Friend, Stephen Plenge, Robert Stolovitzky, Gustavo Oliva, Baldo Guan, Yuanfang Mangravite, Lara M.
author_sort	Sieberts, Solveig K.
title	Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
title_short	Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
title_full	Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
title_fullStr	Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
title_full_unstemmed	Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
title_sort	crowdsourced assessment of common genetic contribution to predicting anti-tnf treatment response in rheumatoid arthritis
description	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.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996969/
_version_	1613634188195397632

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

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