An analysis of biological samples from more than 54,000 people in the UK could help develop a better understanding of how and why diseases develop and aid development of new diagnostics and treatments, researchers reported.
For a study, published in the journal Nature, scientists performed the "world's largest and most comprehensive" investigation into the effects of common genetic variation on circulating proteins, and how these associations could contribute to disease.
A team from the Pharma Proteomics Project — a collaboration between 13 biopharmaceutical companies — conducted proteomic profiling on blood plasma samples collected from 54,219 people enrolled in the UK Biobank. They measured 2941 protein analytes, captured 2923 unique proteins, and identified 14,287 primary genetic associations with these proteins – 81% of which were "previously undescribed", they reported.
Genetic studies of human populations were increasingly used as research tools for drug discovery and development, explained the study authors, who emphasised that these studies could facilitate the identification and validation of therapeutic targets, help to predict long-term consequences of pharmacological intervention, improve patient stratification for clinical trials, and repurpose existing drugs.
"Our analysis identified approximately twentyfold more associations than all previous antibody-based studies," according to the authors. They highlighted that they had constructed an updated genetic atlas of the plasma proteome and revealed biological insights into prevalent illnesses. They would also make the findings available to the wider scientific community as an open-access, population-scale proteomics resource.
Commenting for Medscape News UK, Dr Chris Whelan, director, neuroscience, data science and digital health at Janssen Research & Development, who leads the Pharma Proteomics Project, enthused that the "world's largest blood biomarker dataset" had been created.
New Avenues of Research Opened Up
"Most modern medicines are developed with a single target protein in mind, Dr Whelan pointed out. "In most cases, however, multiple proteins interact to drive the disease." The dataset would help identify these types of interactions and would use the insights to develop medicines that could potentially "enhance the ability to treat, cure, or even prevent diseases, before they developed", he said.
Making the findings available to other scientists would enable better understanding of how and why diseases develop and help "drive the development" of new diagnostics and treatments for a wide range of health conditions, enthused the authors.
The dataset demonstrated the "vast potential" for future research, which the researchers said included:
- Genome-wide association studies to build an open access library of all the common gene variants that influenced blood protein levels. This could be used to study complex biological processes, find proteins involved in causing disease, identify new drug targets, and potentially shorten development time for earlier-stage drug candidates
- Profiling of blood protein levels across the most common health conditions in the UK Biobank – for example, how inflammatory proteins were significantly higher in patients with depression
- Training machine learning models to determine how successfully blood proteins can predict demographic factors. For example, analysis found that blood proteins can predict age, sex and body mass index (BMI) with very high accuracy
Professor Naomi Allen, chief scientist of UK Biobank, commented: "This momentous study offers whole new avenues of research to the biomedical community, and is a leading example of how cross-sector collaboration can bring about results that are so much greater than the sum of their parts."
More Nuanced and Detailed Picture
Beyond drug development, proteomics could be used as a "prediction tool", suggested Dr Whelan. For example, "we could predict if and when a person might develop a disease and intervene early. Or we could predict safety, efficacy, and toxicity prior to real-life clinical trials even starting, helping us advance the most promising candidates, which could help shorten clinical development times".
The dataset would help "paint a much more nuanced and detailed picture" of how the human genome and proteins circulating in the blood influenced human health and disease," Dr Whelan added. This would enable biomedical researchers to identify new biological associations, find new drug targets, and build blood-based diagnostics.