Baseline expression of immune gene modules in blood is associated with primary response to anti-TNF therapy in Crohn's disease patients
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T. Ahmad | C. Anderson | M. Reppell | N. Smaoui | B. Bai | C. Bewshea | H. Guay | J. Goodhand | V. Pivorunas | J. Waring | N. Kennedy | N. Chanchlani | S. Lin | UK Inflammatory Bowel Disease Pharmacogenetics Stu
[1] T. Ahmad,et al. Whole Blood DNA Methylation Changes Are Associated with Anti-TNF Drug Concentration in Patients with Crohn’s Disease , 2023, medRxiv.
[2] Kenneth G. C. Smith,et al. Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease , 2022, Genome Medicine.
[3] M. Gazouli,et al. Type I and II Interferon Signatures Can Predict the Response to Anti-TNF Agents in Inflammatory Bowel Disease Patients: Involvement of the Microbiota. , 2020, Inflammatory bowel diseases.
[4] M. Sanjurjo-Sáez,et al. Gene Signatures of Early Response to Anti-TNF Drugs in Pediatric Inflammatory Bowel Disease , 2020, International journal of molecular sciences.
[5] S. Mirarab,et al. Sequence Analysis , 2020, Encyclopedia of Bioinformatics and Computational Biology.
[6] M. Parkes,et al. Predictors of anti-TNF treatment failure in anti-TNF-naive patients with active luminal Crohn's disease: a prospective, multicentre, cohort study. , 2019, The lancet. Gastroenterology & hepatology.
[7] J. Sanderson,et al. Reduced CD27−IgD− B Cells in Blood and Raised CD27−IgD− B Cells in Gut-Associated Lymphoid Tissue in Inflammatory Bowel Disease , 2019, Front. Immunol..
[8] S. Vermeire,et al. Low TREM1 expression in whole blood predicts anti-TNF response in inflammatory bowel disease , 2019, EBioMedicine.
[9] R. Alberts,et al. The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1 , 2019, BMC Gastroenterology.
[10] Gabriel E. Hoffman,et al. dream: Powerful differential expression analysis for repeated measures designs , 2018, bioRxiv.
[11] S. Vermeire,et al. TREM-1, the ideal predictive biomarker for endoscopic healing in anti-TNF-treated Crohn’s disease patients? , 2018, Gut.
[12] Kathryn Prame Kumar,et al. Partners in crime: neutrophils and monocytes/macrophages in inflammation and disease , 2018, Cell and Tissue Research.
[13] M. van Lookeren Campagne,et al. Selective autophagy of the adaptor TRIF regulates innate inflammatory signaling , 2018, Nature Immunology.
[14] J. Fellay,et al. Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges , 2017, Proceedings of the National Academy of Sciences.
[15] Teresa Domaszewska,et al. tmod: an R package for general and multivariate enrichment analysis , 2016 .
[16] Andreas Heger,et al. UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy , 2016, bioRxiv.
[17] E. Seidman,et al. Predicting durable response or resistance to antitumor necrosis factor therapy in inflammatory bowel disease , 2016, Therapeutic advances in gastroenterology.
[18] Eric E. Schadt,et al. variancePartition: interpreting drivers of variation in complex gene expression studies , 2016, BMC Bioinformatics.
[19] N. Ding,et al. Systematic review: predicting and optimising response to anti‐TNF therapy in Crohn's disease – algorithm for practical management , 2016, Alimentary pharmacology & therapeutics.
[20] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[21] F. Marincola,et al. Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses , 2014, Cell.
[22] C. Rice,et al. Interferon-stimulated genes: a complex web of host defenses. , 2014, Annual review of immunology.
[23] G. Smyth,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[24] M F Neurath,et al. New targets for mucosal healing and therapy in inflammatory bowel diseases , 2013, Mucosal Immunology.
[25] G. Lichtenstein. Comprehensive review: antitumor necrosis factor agents in inflammatory bowel disease and factors implicated in treatment response , 2013, Therapeutic advances in gastroenterology.
[26] B. Meskó,et al. Peripheral blood derived gene panels predict response to infliximab in rheumatoid arthritis and Crohn's disease , 2013, Genome Medicine.
[27] William P. Accomando,et al. DNA methylation arrays as surrogate measures of cell mixture distribution , 2012, BMC Bioinformatics.
[28] G. Cheng,et al. Systematic identification of type I and type II interferon-induced antiviral factors , 2012, Proceedings of the National Academy of Sciences.
[29] Xavier Robin,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.
[30] S. Schreiber,et al. The London Position Statement of the World Congress of Gastroenterology on Biological Therapy for IBD With the European Crohn's and Colitis Organization: When to Start, When to Stop, Which Drug to Choose, and How to Predict Response? , 2011, The American Journal of Gastroenterology.
[31] Davis J. McCarthy,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[32] G Van Assche,et al. Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis , 2009, Gut.
[33] Virginia Pascual,et al. A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. , 2008, Immunity.
[34] W Domschke,et al. Infliximab induces apoptosis in monocytes from patients with chronic active Crohn's disease by using a caspase-dependent pathway. , 2001, Gastroenterology.
[35] U. Kopylov,et al. Optimizing anti-TNF treatments in inflammatory bowel disease. , 2014, Autoimmunity reviews.
[36] R. Tibshirani,et al. Estimating the number of clusters in a data set via the gap statistic , 2000 .
[37] Sandra Romero-Steiner,et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines , 2022 .