Literature DB >> 23486412

The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis.

Samantha Louise Smith1, Darren Plant, Stephen Eyre, Anne Barton.   

Abstract

Whole genome expression profiling, or transcriptomics, is a high throughput technology with the potential for major impacts in both clinical settings and drug discovery and diagnostics. In particular, there is much interest in this technique as a mechanism for predicting treatment response. Gene expression profiling entails the quantitative measurement of messenger RNA levels for thousands of genes simultaneously with the inherent possibility of identifying biomarkers of response to a particular therapy or by singling out those at risk of serious adverse events. This technology should contribute to the era of stratified medicine, in which patient specific populations are matched to potentially beneficial drugs via clinical tests. Indeed, in the oncology field, gene expression testing is already recommended to allow rational use of therapies to treat breast cancer. However, there are still many issues surrounding the use of the various testing platforms available and the statistical analysis associated with the interpretation of results generated. This review will discuss the implications this promising technology has in predicting treatment response and outline the various advantages and pitfalls associated with its use.

Entities:  

Keywords:  Infections; Rheumatoid Arthritis; Treatment

Mesh:

Substances:

Year:  2013        PMID: 23486412     DOI: 10.1136/annrheumdis-2012-202743

Source DB:  PubMed          Journal:  Ann Rheum Dis        ISSN: 0003-4967            Impact factor:   19.103


  17 in total

Review 1.  Management of juvenile idiopathic arthritis: hitting the target.

Authors:  Claas Hinze; Faekah Gohar; Dirk Foell
Journal:  Nat Rev Rheumatol       Date:  2015-01-06       Impact factor: 20.543

Review 2.  Synovial tissue research: a state-of-the-art review.

Authors:  Carl Orr; Elsa Vieira-Sousa; David L Boyle; Maya H Buch; Christopher D Buckley; Juan D Cañete; Anca I Catrina; Ernest H S Choy; Paul Emery; Ursula Fearon; Andrew Filer; Danielle Gerlag; Frances Humby; John D Isaacs; Søren A Just; Bernard R Lauwerys; Benoit Le Goff; Antonio Manzo; Trudy McGarry; Iain B McInnes; Aurélie Najm; Constantino Pitzalis; Arthur Pratt; Malcolm Smith; Paul P Tak; Rogier Thurlings; João E Fonseca; Douglas J Veale; Sander W Tas
Journal:  Nat Rev Rheumatol       Date:  2017-07-13       Impact factor: 20.543

3.  Modular analysis of peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in tumor necrosis factor responders.

Authors:  Michaela Oswald; Mark E Curran; Sarah L Lamberth; Robert M Townsend; Jennifer D Hamilton; David N Chernoff; John Carulli; Michael J Townsend; Michael E Weinblatt; Marlena Kern; Cassandra M Pond; Annette Lee; Peter K Gregersen
Journal:  Arthritis Rheumatol       Date:  2015-02       Impact factor: 10.995

4.  Efficacy, safety and cost-effectiveness of a web-based platform delivering the results of a biomarker-based predictive model of biotherapy response for rheumatoid arthritis patients: a protocol for a randomized multicenter single-blind active controlled clinical trial (PREDIRA).

Authors:  Dalifer Freites-Núñez; Athan Baillet; Luis Rodriguez-Rodriguez; Minh Vu Chuong Nguyen; Isidoro Gonzalez; Jose Luis Pablos; Alejandro Balsa; Monica Vazquez; Philippe Gaudin; Benjamín Fernandez-Gutierrez
Journal:  Trials       Date:  2020-08-31       Impact factor: 2.279

Review 5.  Biomarkers in rheumatic diseases: how can they facilitate diagnosis and assessment of disease activity?

Authors:  Chandra Mohan; Shervin Assassi
Journal:  BMJ       Date:  2015-11-26

6.  Meta-analysis of gene expression profiles to predict response to biologic agents in rheumatoid arthritis.

Authors:  Young Ho Lee; Sang-Cheol Bae; Gwan Gyu Song
Journal:  Clin Rheumatol       Date:  2014-03-05       Impact factor: 2.980

Review 7.  Genetic and epigenetic predictors of responsiveness to treatment in RA.

Authors:  Darren Plant; Anthony G Wilson; Anne Barton
Journal:  Nat Rev Rheumatol       Date:  2014-02-18       Impact factor: 20.543

8.  Defining response to TNF-inhibitors in rheumatoid arthritis: the negative impact of anti-TNF cycling and the need for a personalized medicine approach to identify primary non-responders.

Authors:  Keith J Johnson; Helia N Sanchez; Nancy Schoenbrunner
Journal:  Clin Rheumatol       Date:  2019-09-13       Impact factor: 2.980

Review 9.  Pharmacogenetics of treatment response in psoriatic arthritis.

Authors:  Meghna Jani; Anne Barton; Pauline Ho
Journal:  Curr Rheumatol Rep       Date:  2015-07       Impact factor: 4.592

10.  A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study.

Authors:  Stanley Cohen; Alvin F Wells; Jeffrey R Curtis; Rajat Dhar; Theodore Mellors; Lixia Zhang; Johanna B Withers; Alex Jones; Susan D Ghiassian; Mengran Wang; Erin Connolly-Strong; Sarah Rapisardo; Zoran Gatalica; Dimitrios A Pappas; Joel M Kremer; Alif Saleh; Viatcheslav R Akmaev
Journal:  Rheumatol Ther       Date:  2021-06-19
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