Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

ABSTRACT

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. 

Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. 

Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

KEYWORDS:

• Spondyloarthritis
• ankylosing spondylitis
• chronic inflammatory disease
• rheumatoid arthritis
• inflammatory bowel disease
• anti-TNF therapy
• biomarkers
• treatment response
• prediction
• transcriptome
• immune responses

Article highlights

•Anti-TNF therapy has strong effects on several immune response pathways, modulating gene expression, cell population frequencies, and serum protein levels.

•Several biomarkers for disease progression in patients undergoing anti-TNF treatment have been identified; however, no biomarker has been validated for clinical use to predict response to treatment at baseline.

•Genetic biomarkers based on single nucleotide polymorphisms have demonstrated limited power to predict response to treatment.

•The combination of several biomarkers may improve the prediction power of statistical models of response to anti-TNF therapy. In particular, the inclusion of different types of biomarkers (genetic, transcriptional, protein, cellular) may be necessary to capture the biological complexity of response to treatment.

•New bioinformatic tools, including machine learning approaches, are necessary to handle the complexity of the large data sets being currently explored.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

I.M. is supported by the Pasteur – Paris University (PPU) International PhD Program. Work in the authors’ laboratory is supported by grants from Institut Pasteur, FOREUM Foundation for Research in Rheumatology, MSD Avenir (Project iCARE-SpA), and a Sanofi Innovation Award Europe.