Integration of known DNA, RNA and protein biomarkers provides prediction of anti-TNF response in rheumatoid arthritis: results from the COMBINE study.

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Integration of known DNA, RNA and protein biomarkers provides prediction of anti-TNF response in rheumatoid arthritis : results from the COMBINE study.

Folkersen, Lasse (författare): Department of Bioinformatics, Technical University of Denmark, Lyngby, Denmark.; Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Brynedal, Boel (författare): Karolinska Institutet,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Diaz-Gallo, Lina Marcela (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

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Ramsköld, Daniel (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Shchetynsky, Klementy (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Westerlind, Helga (författare): Karolinska Institutet,nstitute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Sundström, Yvonne (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Schepis, Danika (författare): Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Hensvold, Aase (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Vivar, Nancy (författare): Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Eloranta, Maija-Leena (författare): Uppsala universitet,Science for Life Laboratory, SciLifeLab,Reumatologi

Rönnblom, Lars (författare): Uppsala universitet,Reumatologi,Science for Life Laboratory, SciLifeLab

Brunak, Søren (författare): Department of Bioinformatics, Technical University of Denmark, Lyngby, Denmark

Malmström, Vivianne (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Catrina, Anca (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Moerch, Ulrik Gw (författare)

Klareskog, Lars (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Padyukov, Leonid (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Berg, Louise (författare): Karolinska Institutet,Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

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Karolinska Institutet Department of Bioinformatics, Technical University of Denmark, Lyngby, Denmark; Unit of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden (creator_code:org_t)

2016-08-15
2016
Engelska.
Ingår i: Molecular Medicine. - : Springer Science and Business Media LLC. - 1076-1551 .- 1528-3658. ; 22, s. 322-328

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OBJECTIVE: In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measurements to test the claim that the current state-of-the-art precision medicine will benefit RA patients.METHODS: We collected 451 blood samples from 61 healthy individuals and 185 RA patients initiating treatment, before treatment initiation and at a 3 month follow-up time. All samples were subjected to high-throughput RNA sequencing, DNA genotyping, extensive proteomics and flow cytometry measurements, as well as comprehensive clinical phenotyping. Literature review identified 2 proteins, 52 single-nucleotide polymorphisms (SNPs) and 72 gene-expression biomarkers that had previously been proposed as predictors of TNF inhibitor response (∆DAS28-CRP).RESULTS: From these published TNFi biomarkers we found that 2 protein, 2 SNP and 8 mRNA biomarkers could be replicated in the 59 TNF initiating patients. Combining these replicated biomarkers into a single signature we found that we could explain 51% of the variation in ∆DAS28-CRP. This corresponds to a sensitivity of 0.73 and specificity of 0.78 for the prediction of three month ∆DAS28-CRP better than -1.2.CONCLUSIONS: The COMBINE biobank is currently the largest collection of multi-omics data from RA patients with high potential for discovery and replication. Taking advantage of this we surveyed the current state-of-the-art of drug-response stratification in RA, and identified a small set of previously published biomarkers available in peripheral blood which predicts clinical response to TNF blockade in this independent cohort.

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MEDICIN OCH HÄLSOVETENSKAP: MEDICIN OCH HÄLS ...; och Klinisk medicin; och Reumatologi och ...

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Integration of known DNA, RNA and protein biomarkers provides prediction of anti-TNF response in rheumatoid arthritis : results from the COMBINE study.

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