RESUMO
Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in â¼one-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.
Assuntos
Anticorpos Monoclonais Humanizados/uso terapêutico , Artrite Reumatoide/tratamento farmacológico , Predisposição Genética para Doença/genética , Polimorfismo de Nucleotídeo Único , Fator de Necrose Tumoral alfa/antagonistas & inibidores , Adulto , Idoso , Anticorpos Monoclonais/uso terapêutico , Antirreumáticos/uso terapêutico , Artrite Reumatoide/genética , Artrite Reumatoide/patologia , Certolizumab Pegol/uso terapêutico , Estudos de Coortes , Crowdsourcing , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Resultado do Tratamento , Fator de Necrose Tumoral alfa/imunologiaRESUMO
Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).
