What researchers call “the largest proteomic study in the world” could lead to new clinical laboratory assays for determining genetic risk for multiple cancers
Examining blood proteins may be superior to clinical information in determining an individual’s risk for developing multiple diseases. That’s according to a new study conducted by researchers from the UK, America, and Germany who determined that measuring thousands of proteins from a single drop of blood can predict the onset of several illnesses.
The findings may provide clinical laboratories and physicians with new assays to more accurately predict an individual’s risk for more than 60 diseases.
“With data on genetic, imaging, lifestyle factors and health outcomes over many years, this will be the largest proteomic study in the world to be shared as a global scientific resource,” said Naomi Allen, MSc, DPhil, chief scientist at UK Biobank and professor of epidemiology, University of Oxford, in a UK Biobank news release. “These combined data could enable researchers to make novel scientific discoveries about how circulating proteins influence our health, and to better understand the link between genetics and human disease.”
The study was conducted through a collaboration between GlaxoSmithKline Research and Development (GSK), Queen Mary University of London, University College London (UCL), University of Cambridge, and the Berlin Institute of Health (BIH) in Germany.
The researchers published their findings in the journal Nature Medicine titled, “Proteomic Signatures Improve Risk Prediction for Common and Rare Diseases.”
“Measuring protein levels in the blood is crucial to understanding the link between genetic factors and the development of common life-threatening diseases,” said Naomi Allen, MSc, DPhil (above), chief scientist at UK Biobank and professor of epidemiology, University of Oxford, in a news release. With further study, this research could lead to new clinical laboratory assays that help physicians predict an individual’s risk for certain diseases including many forms of cancer. (Photo copyright: UK Biobank.)
Protein Signatures Outperform PSA Testing
To conduct their research, the team collected data from the UK Biobank Pharma Proteomics Project (UKB-PPP). This initiative is “one of the world’s largest studies of blood protein concentrations” and “aims to significantly enhance the field of ‘proteomics,’ enabling better understanding of disease processes and supporting innovative drug development,” according to the Biobank’s website.
The scientists analyzed the values of approximately 3,000 plasma proteins among 41,931 participants in the UKB-PPP. They examined the 10-year potential of developing certain diseases by measuring the plasma proteome and linking those observations to incident cases noted in electronic health records (EHRs).
The team specifically looked at the pathology types for several illnesses and utilized advanced techniques to identify a signature of proteins associated with those various diseases. They found their protein-based model exceeded traditional prediction methods when comparing the models with polygenic risk scores.
“Several of our protein signatures performed similar or even better than proteins already trialed for their potential as screening tests, such a prostate specific antigen (PSA) for prostate cancer,” said Julia Carrasco Zanini Sanchez, PhD, postdoctoral research assistant in computational genomics and multi-omics, Queen Mary University of London, and first author of the study, in a UCL news release.
“We are therefore extremely excited about the opportunities that our protein signatures may have for earlier detection and ultimately improved prognosis for many diseases, including severe conditions such as Multiple myeloma and idiopathic pulmonary fibrosis,” she added. “We identified so many promising examples; the next step is to select high priority diseases and evaluate their proteomic prediction in a clinical setting.”
Identifying Individuals at High Risk for Certain Diseases
Of the thousands of known proteins in humans, the team focused on about 20 proteins found in blood. With as few as five proteins and as many as 20, they were able to do a risk assessment on 67 diseases, including:
- Multiple myeloma
- Non-Hodgkin lymphoma
- Motor neuron disease
- Pulmonary fibrosis
- Dilated cardiomyopathy
The model could prove to be beneficial in the development of new therapies for certain diseases.
“A key challenge in drug development is the identification of patients most likely to benefit from new medicines. This work demonstrates the promise in the use of large-scale proteomic technologies to identify individuals at high risk across a wide range of diseases, and aligns with our approach to use tech to deepen our understanding of human biology and disease,” said Robert Scott, vice president and head of human genetics and genomics, GSK, and co-lead author of the study in the UCL news release.
“Further work will extend these insights and improve our understanding of how they are best applied to support improved success rates and increased efficiency in drug discovery and development,” he added.
“We are extremely excited about the opportunity to identify new markers for screening and diagnosis from the thousands of proteins circulating and now measurable in human blood,” said Claudia Langenberg, PhD, director of the Precision Healthcare University Research Institute (PHURI) at Queen Mary University of London and professor of computational medicine at the Berlin Institute of Health, in the UCL news release. “What we urgently need are proteomic studies of different populations to validate our findings, and effective tests that can measure disease relevant proteins according to clinical standards with affordable methods.”
More research and studies are needed before the protein-based model can be used to predict disease in clinical settings. However, the model could someday provide clinical laboratories, pathologists, and physicians with new assays that more accurately forecast an individual’s risk for certain illnesses.
—JP Schlingman
Related Information:
Blood Proteins Predict the Risk of Developing More than 60 Diseases
Proteomic Signatures Improve Risk Prediction for Common and Rare Diseases