Literature DB >> 25392319

Prognostic biomarkers of IFNb therapy in multiple sclerosis patients.

Sergio E Baranzini1, Lohith R Madireddy2, Anne Cromer3, Mauro D'Antonio4, Lorenz Lehr3, Manolo Beelke3, Pierre Farmer3, Marco Battaglini5, Stacy J Caillier2, Maria L Stromillo6, Nicola De Stefano6, Emmanuel Monnet3, Bruce A C Cree2.   

Abstract

BACKGROUND: Interferon beta (IFNb) reduces relapse frequency and disability progression in patients with multiple sclerosis (MS).
OBJECTIVES: Early identification of prognostic biomarkers of IFNb-treated patients will allow more effective management of MS.
METHODS: The IMPROVE study evaluated subcutaneous IFNb versus placebo in 180 patients with relapsing-remitting MS. Magnetic resonance imaging scans, clinical assessments, and blood samples were obtained at baseline and every 4 weeks from every participant. Thirty-nine biomarkers (32 transcripts; seven proteins) were studied in 155 patients from IMPROVE. Therapeutic response was defined by absence of new combined unique lesions, relapses, and sustained increase in Expanded Disability Status Scale over 1 year. A machine learning approach was used to examine the association between biomarker expression and treatment response.
RESULTS: While baseline levels of individual genes were relatively poor predictors, combinations of three genes were able to identify subjects with sub-optimal therapeutic responses. The triplet CASP2/IRF4/IRF6, previously identified in an independent dataset, was tested among other combinations. This triplet showed acceptable predictive accuracy (0.68) and specificity (0.88), but had relatively low sensitivity (0.22) resulting in an area under the curve (AUC) of 0.63. Other combinations of biomarkers resulted in AUC of up to 0.80 (e.g. CASP2/IL10/IL12Rb1).
CONCLUSIONS: Baseline expression, or induction ratios, of specific gene combinations correlate with future therapeutic response to IFNb, and have the potential to be prognostically useful.
© The Author(s), 2014.

Entities:  

Keywords:  Biomarker; RNA; bioinformatics; interferon beta; multiple sclerosis; prognostic

Mesh:

Substances:

Year:  2014        PMID: 25392319     DOI: 10.1177/1352458514555786

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  7 in total

Review 1.  Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity.

Authors:  Volker M Lauschke; Yitian Zhou; Magnus Ingelman-Sundberg
Journal:  Pharmacol Ther       Date:  2019-01-22       Impact factor: 12.310

2.  Common variation near IRF6 is associated with IFN-β-induced liver injury in multiple sclerosis.

Authors:  Kaarina Kowalec; Galen E B Wright; Britt I Drögemöller; Folefac Aminkeng; Amit P Bhavsar; Elaine Kingwell; Eric M Yoshida; Anthony Traboulsee; Ruth Ann Marrie; Marcelo Kremenchutzky; Trudy L Campbell; Pierre Duquette; Naga Chalasani; Mia Wadelius; Pär Hallberg; Zongqi Xia; Philip L De Jager; Joshua C Denny; Mary F Davis; Colin J D Ross; Helen Tremlett; Bruce C Carleton
Journal:  Nat Genet       Date:  2018-07-16       Impact factor: 38.330

3.  Myxovirus Resistance Protein A mRNA Expression Kinetics in Multiple Sclerosis Patients Treated with IFNβ.

Authors:  Jana Libertinova; Eva Meluzinova; Ales Tomek; Dana Horakova; Ivana Kovarova; Vaclav Matoska; Simona Kumstyrova; Miroslav Zajac; Eva Hyncicova; Petra Liskova; Eva Houzvickova; Lukas Martinkovic; Martin Bojar; Eva Havrdova; Petr Marusic
Journal:  PLoS One       Date:  2017-01-12       Impact factor: 3.240

4.  Systematic review of prediction models in relapsing remitting multiple sclerosis.

Authors:  Fraser S Brown; Stella A Glasmacher; Patrick K A Kearns; Niall MacDougall; David Hunt; Peter Connick; Siddharthan Chandran
Journal:  PLoS One       Date:  2020-05-26       Impact factor: 3.240

5.  Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.

Authors:  Kelley M Swanberg; Abhinav V Kurada; Hetty Prinsen; Christoph Juchem
Journal:  Sci Rep       Date:  2022-08-16       Impact factor: 4.996

6.  The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

Authors:  Md Zakir Hossain; Elena Daskalaki; Anne Brüstle; Jane Desborough; Christian J Lueck; Hanna Suominen
Journal:  BMC Med Inform Decis Mak       Date:  2022-09-15       Impact factor: 3.298

Review 7.  A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.

Authors:  I S Stafford; M Kellermann; E Mossotto; R M Beattie; B D MacArthur; S Ennis
Journal:  NPJ Digit Med       Date:  2020-03-09
  7 in total

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