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

• Published in: Nature communications 2016-08, Vol.7 (1), p.12460-12460, Article 12460
• 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: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2413; 631/208/2489/144; Adult; Aged; Antibodies, Monoclonal - therapeutic use; Antibodies, Monoclonal, Humanized - therapeutic use; Antirheumatic Agents - therapeutic use; Arthritis; Arthritis, Rheumatoid - drug therapy; Arthritis, Rheumatoid - genetics; Arthritis, Rheumatoid - pathology; Artritis reumatoide; Biomarkers; Certolizumab Pegol - therapeutic use; Cohort Studies; Communities; Crowdsourcing; Female; Genetic Predisposition to Disease - genetics; Heritability; Humanities and Social Sciences; Humans; Life Sciences; Male; Medical treatment; Medicin och hälsovetenskap; Middle Aged; multidisciplinary; Patients; Performance prediction; Polymorphism, Single Nucleotide; Prognosis; Quantitative Methods; Rheumatoid arthritis; Science; Science (multidisciplinary); Single-nucleotide polymorphism; Tractament; Treatment Outcome; Tumor Necrosis Factor-alpha - antagonists & inhibitors; Tumor Necrosis Factor-alpha - immunology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms12460

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 ( 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. Rheumatoid arthritis patients respond differently to anti-TNF treatment. Using community-based challenge, the authors show that currently available data does not reveal meaningful genetic predictors of response to anti-TNF therapy, thus confirming clinical observations.