The Pharma Proteomics Project, backed by 14 pharmaceutical companies, has launched a major population proteomics study using UK Biobank patient samples. This initiative will measure up to 5,400 proteins in each of 600,000 patient samples, creating a comprehensive dataset to understand how individual protein levels change over lifetimes and during illness.
The sample set includes 500,000 UK Biobank volunteers who have provided samples since the organization's inception over 20 years ago, plus 100,000 second samples taken from some of these patients up to 15 years later. This follows a pilot study published in 2023, which analyzed data from around 3,000 proteins across 54,000 UK Biobank volunteers, identifying over 14,000 links between common genetic variants and altered protein levels. These findings have already contributed to discoveries in disease prediction and potential therapies for cardiovascular disease, neurological disorders, and cancer.
The full-scale project aims to significantly expand on the pilot's findings, with actionable results anticipated within the next five years. The study will begin by testing 300,000 samples at the Regeneron Genetics Center in the US, funded by the project's initial pharmaceutical backers. The data will be made available to scientists worldwide, with sponsoring companies receiving a brief period of exclusive access.
Professor Sir Rory Collins, who has led UK Biobank since 2002, emphasized the importance of pre-competitive research in advancing health innovation. The study will integrate proteomic and genetic data, building on the whole genome sequencing of UK Biobank's half a million participants published in November 2023. This data will be combined with imaging and anonymized health and lifestyle information from nearly 100,000 volunteers who have undergone MRI scanning.
Dr. Chris Whelan of J&J, leading the Pharma Proteomics Project, highlighted the potential of adding proteomic data to the UK Biobank cohort as a game changer for disease prediction and prognosis, especially for neglected diseases lacking good prospective data. The project also holds promise for the discovery of artificial intelligence algorithms capable of predicting diseases years earlier than currently possible, alongside potential personalized treatments.
