Publication highlights July 2024

These are just some selected articles to highlight from a wealth of recent publications citing the use of Olink’s PEA technology.

Proteomic insights into aspirin-exacerbated respiratory disease

Researchers at Brigham and Women’s Hospital, Boston, MA used the Olink® Target 48 Cytokine panel to examine clinical trial samples to gain important new insights into the pathobiology and treatment of aspirin-exacerbated respiratory disease (AERD). This condition is known to involve type 2 inflammation and responds well to the IL-4Rα inhibitor, dupilumab, but knowledge regarding the underlying molecular pathways remains incomplete. The Olink panel was used to measure longitudinal proteomics in nasal fluid from AERD patients before and during dupilumab therapy.

Key highlights.

Multiple proteins were significantly higher in AERD patients vs controls at baseline.
Several of these were markedly reduced after 3 months of therapy.
Associated pathways suggested involvement of innate immune responses, inflammation, and mediators of epithelial dysregulation in AERD pathobiology.
While dupilumab primarily targets type 2 inflammation, the additional affect on the novel biomarkers identified may help to explain the strong efficacy of this drug against AERD.

Citation
Chen C., Buchheit K., Lee P., et al. (2024) IL-4Rα signaling promotes barrier-altering oncostatin M and IL-6 production in aspirin-exacerbated respiratory disease. Journal of Allergy and Clinical Immunology.

This study provides evidence that even therapies targeted specifically to type 2 inflammation may have wide-ranging effects on other inflammatory pathways.

CHEN ET AL. (2024)

Improving disease risk prediction through broad protein biomarker discovery

In this population health study, a team from the MRC Epidemiology Unit in Cambridge used the Olink® Explore platform to analyze baseline serum proteomics measured in healthy volunteers who went on to develop a range of different diseases over a follow-up period of up to 10 years.

Key highlights.

Machine learning identified predictive risk models for 24 different diseases.
Models based on as few as 5 proteins outperformed polygenic risk scores for 17 of these.
The protein models improved predictive performance when added to clinical parameter-based models for seven different outcomes.
A composite 10-protein model showed high performance prediction across 21 individual diseases, with a median C-index of 0.72.

Citation
Carrasco-Zanini J., Pietzner M., Koprulu M., (2024) Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study. The Lancet Digital Health.

Our study highlights the potential of broad-capture proteomics for the development of sparse signatures to improve prediction strategies, including common panels of biomarkers for the prediction of multiple diseases, and provides a guide for future studies on disease causes.

CARRASCO-ZANINI ET AL. (2024)

A proteomics-empowered genomic study identifies a predictive drug response biomarker for AUD

Researchers from the Mayo Clinic, Rochester used the Olink® Explore 384 Inflammation panel in a proteomics-informed genomics study aimed at identifying therapy response biomarkers in patients with Alcohol Use Disorder (AUD) treated with the FDA-approved drug, acamprosate . This was a clinical trial instigated specifically with the primary aim to “collect diverse omics data for subsequent analysis and to pinpoint biomarkers linked to the outcomes of acamprosate treatment”. Plasma proteomics were measured at baseline in patients undergoing a 3-month treatment period, who were then evaluated as showing a positive response (abstinence) or relapse to alcohol consumption.

Key highlights.

12 proteins were significantly associated with relapse to alcohol during the 3-month trial
GWAS identified protein expression-associated gene variants (pQTLs) for 8 markers, including IL-17RB
Genotype/phenotype analysis identified several gene variants associated with acamprosate response located close to IL-17RB gene.
The association of IL-17RB protein levels with drug response was also seen at the mRNA level.

Citation
Ho M., Zhang C., Cohan J., et al. (2024) IL17RB genetic variants are associated with acamprosate treatment response in patients with alcohol use disorder: A proteomics-informed genomics study. Brain, Behavior, and Immunity.

Our results revealed that the application of multi-omics approaches may be a feasible and helpful strategy for identifying biomarkers that could potentially aid in predicting acamprosate treatment response.

HO ET AL. (2024)

Prognostic biomarkers for primary sclerosing cholangitis

Proteomic analysis of clinical trial samples enables the discovery of new biomarkers to identify diseases, monitor disease severity and provide prognostic indications. In this study, scientists from Chemomab Therapeutics Ltd used Olink® Explore 3072 to analyze serum samples from primary sclerosing cholangitis (PSC) patients participating in two clinical trials and applied machine learning analysis to identify proteins associated with disease progression.

Key highlights

A multi-protein model discriminated PSC vs controls with very high accuracy (AUC=0.99).
These biomarkers have known links to biological processes of significant relevance to PSC (cell adhesion, immune response, and inflammation).
Multiple proteins showed strong associations with a clinical score (ELF) for disease severity – top 5 proteins with individual AUCs>0.8 to discriminate high vs low ELF scores.
Higher serum levels of CCL24 were significantly associated with cirrhosis, an indicator of progression to severe PSC.

Citation
Snir T., Greenman R., Aricha R., et al. (2024) Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis. International Journal of Molecular Sciences.

The biomarkers identified through this study hold significant translational importance, offering potential advancements in the monitoring of disease progression in clinical settings. Moreover, the identified serum biomarkers could enhance the precision of disease monitoring, potentially leading to earlier interventions, tailored treatment plans, and improved patient outcomes.

SNIR ET AL. (2024)