Identify predictive markers for drug response

One of the major drivers for precision medicine has been that the majority of drugs and therapies developed to date do not work equally well for all patients diagnosed with the same condition, with significant variations in efficacy and safety among individuals. The increase in biopharmaceutical development has resulted in many new, highly specific therapeutics such as monoclonal antibodies, many of which are highly effective in treating the majority of patients. It is still a common feature, however, that a significant proportion of patients simply fail to respond, resulting in wasted treatment costs with no benefit to the patient. A simple test that could accurately predict whether an individual will respond positively to a given therapy would be of great benefit to all, patients, clinicians and reimbursement authorities alike, and also provide new drug development opportunities to target non-responsive patient groups.

Case study - response to anti-TNF therapy in RA

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One intensively investigated example is antibody-based treatment targeting tumor necrosis factor alpha (TNF), which has been successfully used to treat a range of autoimmune/inflammatory diseases such as rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). For both of these diseases, most patients respond very well to anti-TNF therapy, whereas a significant minority show no response at all. 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.

Predicting drug responders in rheumatoid arthritis (RA)

Background

  • Anti-TNF therapy is expensive – no benefit to 1/3 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

Prasad et al. (2022) PLoS Computational Biology, DOI: 10.1371/journal.pcbi.1010204

  • Machine learning identifies a 17-protein classifier to identify responders (AUC=0.88).

Prasad et al. (2022) PLoS Computational Biology, DOI: 10.1371/journal.pcbi.1010204

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.
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Webinar – predicting drug responders in juvenile IBD

The Crohn’s & Colitis Foundation recently ran a major study to find prognostic markers that can determine if children with Crohn’s disease will develop serious complications such as fibrosis and/or fistulas, and markers that predict whether they are likely to respond to anti-TNF therapy. The first phase of the project used RNAseq data from mucosal biopsy samples to identify gene expression signatures linked to development of complications and anti-TNF response, before employing Olink panels to look for less invasive blood-based markers. Machine learning algorithms identified both a 14-protein prognostic signature that predicts fibrosis/fistula developmet with an accuracy of 84%, as well as a signature of just 3 proteins that predicts responders to anti-TNFα therapy with an accuracy of 90%.

andres-hurtado-lorenzo

Dr. Hurtado-Lorenzo, VP of Translational Research for the Crohn’s & Colitis Foundation has presented this impressive work in a recent webinar. You can listen to snippet from the webinar HERE or access the full presentation below.

Proteomics at the heart of multiomics strategies

Systems biology approaches addressing multiple molecular and cellular components are adding vital insights into the dynamic biology underlying human health and disease.

Hear from industry experts on how proteomics is changing the way we develop new drugs and study disease.

The power of proteomics in multiomic studies

Download the eBook, ‘Proteomics at the heart of multiomic studies’ to learn more about how this trend is the next step in advancing precision medicine.

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Protein data points generated

1068

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