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Unlocking RA treatment responses with predictive protein profiles

Antibody-based treatments targeting tumor necrosis factor alpha (TNF) are effective for autoimmune diseases like rheumatoid arthritis and inflammatory bowel disease. While most patients respond well to anti-TNF therapy, a significant minority do not.

A recent study by Prasad et al. used Olink-based plasma proteomics to look for protein profiles that could potentially predict which RA patients would or would not respond to six months of anti-TNF treatment. As summarized in the figure below, plasma proteomics identified a machine learning-derived identifier composed of 17 proteins that can predict treatment responders with high accuracy. Additionally, a principal component analysis (PCA) of the baseline samples clustered patients into two previously unknown RA endotypes, which could further help refine and guide future treatment.

This may aid clinicians in deciding about putting an RA patient under anti-TNF therapy. This will help in saving the treatment cost as well as preventing nonresponsive patients to go through refractory condition of the disease leading to poor quality of life.
Prasad B, et al., 2022

Predicting drug responders in rheumatoid arthritis (RA)

Background

  • Anti-TNF therapy is expensive with no benefit to 30% of RA patients.

Method

  • Plasma from 144 RA patients on anti-TNF therapy interrogated with four Olink Target 96 panels.
  • Baseline samples examined by PCA analysis – machine learning applied to samples grouped by response/non-response after 6 months – can protein biomarker profiles predict responders?

Results

  • PCA analysis – 2 clusters of RA patients with clinical differences
  • Machine learning identifies a 17-protein classifier to identify responders (AUC=0.88).

Conclusions

  • Identified novel endotypes (gender & disease severity) for RA that could aid future treatment development.
  • Predictive classifier for responders helps optimize treatment selection, reduce costs and improve quality of life for non-responders.

Citation:

Prasad B, McGeough C, Eakin A, et al. ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients. (2022) PLoS Computational Biology, DOI: 10.1371/journal.pcbi.1010204 – Read article