In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis

ABSTRACT

Introduction

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease. Early referral and treatment are key to the effective management of the disease. This makes imperative the identification of biomarkers and of pathobiological endotypes.

Areas covered

This review describes recent efforts to integrate large-scale datasets for the identification of disease endotypes for precision medicine in early, seropositive RA. We conducted a search for systems and multi-omics papers in early RA patients through to 1 January 2020. We reviewed investigations of multiple technologies such as transcriptomic, proteomic and metabolomic platforms as well as extensive clinical datasets. We outline progress made and describe some of the advantages and limitations of current computational and statistical methods.

Expert opinion

The search for pathobiological endotypes in early RA is rapidly developing. While currently, studies tend to be small, reliant upon new technologies and unproven analytical tools, as the technology becomes cheaper and more reliable, and the properties of analytical tools for the integration of cross-platform biology become better understood, it seems likely that better biomarkers of disease, remission and response to individual therapies will emerge.

KEYWORDS:

• Rheumatoid arthritis
• systems
• stratified medicine
• endotypes
• pathogenesis

Article highlights

• Outline key steps in the discovery and validation of pathobiological endotypes in early, drug-naïve, seropositive RA.

• Review existing data and technology platforms.

• Outline the analytical challenges integrating data across platforms.

• Provide a Glossary of Statistical Machine Learning tools to aid researchers in this field.

• Note that currently clinical measures are more predictive of clinical outcomes.

• Expect that as the technologies mature, the data improve, and analytical tools refined we are likely to see significant progress in the next ten years.

Acknowledgments

We thank the RA-MAP Data Analysis team and members of the Bioinformatics Support Unit at Newcastle University for lively discussion and their insights into statistical machine learning and data analytics methods.

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

This paper was supported in part by the NIHR Biomedical Research Centre at Newcastle University and Newcastle upon Tyne Hospitals NHS Foundation Trust and in part through the Versus Arthritis Research into Inflammatory Arthritis Centre of Excellence (RACE) and the Immune-Mediated Inflammatory Diseases Bio Bank (IMID-BIO-UK).