Go Beyond Genomics With NGS-Based Proteomics

Article How NGS-Based Proteomics Is Advancing Disease Insights and Precision Medicine Introduction For decades, scientists have studied genetic predispositions to better understand why diseases develop, when they emerge and how they progress. Precision medicine recognizes people’s genetic differences and creates prevention and treatment plans tailored to their unique biology. However, DNA is only part of the story, as our genetic code provides instructions for making proteins. These proteins are responsible for carrying out nearly every biological process in the body, from metabolizing sugars to regulating immune responses. Genetic differences can lead to variations in the quantity, structure and function of proteins. For example, a genetic variant might affect glucose metabolism, increasing the risk of developing type 2 diabetes. However, knowing that a person carries a particular variant doesn’t always reveal the full picture, as it doesn’t indicate how much of the protein is being produced or how well it is functioning. Proteomics addresses this gap by directly quantifying the proteins and revealing how genetic variation translates into biological activity. Unlike genomic data, which reflects potential risk, proteomic analysis captures dynamic, real-time physiological processes occurring in the body. In this article, Dr. Cindy Lawley, Senior Director of Global Population Health at Olink Proteomics, discusses the application of next-generation sequencing (NGS)-based proteomics to advance disease insights, personalize therapeutic journeys and improve disease risk prediction. Watch the complete Teach Me in 10 episode with Dr. Cindy Lawley here. Learn more about Olink’s NGS-based proteomics here . How NGS-based proteomics works NGS-based proteomics enables large-scale protein analysis and has only recently become advanced enough to perform proteomics profiling across large populations. One approach driving these advances is the Proximity Extension Assay (PEA). The PEA technology involves two antibodies, each tagged with a single DNA strand, that bind specifically to their target protein. When both antibodies attach to the same protein, their DNA tags hybridize and are extended to form a complete sequence. This DNA ‘barcode’ is an identifier for the protein in question, which can be detected and quantified using NGS (Figure 1). This process effectively converts a protein signal into a measurable DNA signal, with the number of DNA tags accurately reflecting the protein concentration in the sample. This high-throughput method enables the rapid, precise and large-scale measurement of thousands of proteins in as many samples. This data helps researchers identify protein biomarker patterns that may support more accurate disease prediction and therapeutic development.2 The UK Biobank: A proteomics treasure trove To uncover population-wide protein biomarker patterns and gain deeper insights into disease development and progression, high-throughput NGS-based proteomics is used to generate proteomics data from large-scale biobank samples. The UK Biobank is one of the world’s most valuable research resources, comprising biological samples and health data from around 600,000 participants who enrolled in the early 2000s. Initially, the UK Biobank collected genetic data on all participants, making it possible to study inherited disease risk. Later, the UK Biobank Pharma Proteomics Project (UKB-PPP) expanded on this by including protein biomarker profiling in the database, effectively combining genetic, proteomic and health data in a single resource. The pilot phase of the UKB-PPP was a collaboration between 14 pharmaceutical and biotech companies that characterized the plasma proteomic profiles of over 54,000 UK Biobank participants.1 The study demonstrated how protein-based risk scores – a likelihood prediction of developing a certain disease – could significantly outperform genetic risk scores alone for predicting many diseases. The study has been accessed over 168,000 times and cited around 700 times, reflecting its global impact. This flagship paper has laid the groundwork for numerous studies worldwide, establishing the UK Biobank as a vital international resource for advancing health research and precision medicine. Building on the success of the pilot phase, it was announced in January 2025 that Olink® Explore HT (covering over 5,400 proteins) would be used to profile all 500,000 UK Biobank participants. This expanded dataset promises to reveal even more about the intricate connections between proteins and disease, laying the groundwork for targeted therapies and enhanced diagnostic tools. How research takes a concept to the clinic Several research groups have conducted studies to explore the utility and validity of a proteomics approach for disease prediction. Many studies involve modeling how protein signals perform compared to genetic signals. For example, a research team built predictive models for 23 different diseases by combining multiple protein measurements into a single score (ProteinScores), estimating a person’s risk of developing a disease within 10 years. The team found that, for conditions such as type 2 diabetes, this score significantly improved prediction accuracy beyond traditional risk factors, lifestyle variables and even clinical and genetic biomarkers.2 Figure 1. Proximity extension assay (PEA) technology, whereby: 1) antibodies with DNA tags bind to the target protein, 2 and 3) the DNA tags hybridize when pairs are correctly matched and 4) a unique DNA barcode is created for each protein. This DNA tag is then quantified using NGS.3 NGS-based proteomics can also be used to predict cancer development. In another study, UK Biobank data was used to develop protein signatures that could predict 19 blood and solid tumor cancers as early as 12 years before clinical diagnosis.3 Beyond cancer, a similar approach was used for 67 common and rare diseases, where protein scores built from as few as 5 to 20 proteins outperformed current clinical tools for predicting who will develop these conditions.4 Together, these findings highlight the potential of proteomic profiling to enable earlier and more accurate disease risk prediction across a wide range of conditions. Yet, further clinical validation is required to be able to apply this approach in routine clinical diagnostics and patient care. One such example is the development of a multiple sclerosis test based on protein signatures. Beginning with the identification of 1,416 related proteins in a 2017 discovery study, several research teams were able to validate 18 proteins as part of a dashboard for monitoring the condition (Figure 2).5,6,7,8 By monitoring these indicative proteins, a personalized medication program can be made for each patient, supporting their therapeutic journey as they receive various medications throughout their life. NGS-based proteomics: A clinical crystal ball? NGS-based proteomics offers a powerful new tool for predicting disease risk and guiding personalized medicine. By capturing thousands of proteins at scale, researchers can see beyond genetic predisposition to the intertwining biological processes driving disease. This approach enables researchers to predict disease development several years before symptoms arise, as shown in studies predicting diabetes, cancer and neurodegenerative conditions.2,3,4,5,6,7,8 Combined with genetic approaches, proteomics allows researchers to understand not only inherited risk but also lifestyle and environmental factors, making it an exceptionally rich source of insight for precision medicine. Currently, validated clinical tools are translating these discoveries into real-world care, supporting treatment decisions tailored to individual patients. As large-scale resources like the UK Biobank continue to integrate genetic, proteomic and health data, the potential for discovering new biomarkers and refining risk scores will only grow. While challenges remain in clinical validation and implementation, NGS-based proteomics is poised to become a cornerstone of early detection, risk stratification and personalized treatment planning. By promoting this technology, the healthcare sector can move toward a future that is truly predictive, precise and personalized, giving clinicians a new tool that may amount to a molecular-level crystal ball for patient care. Learn more about Olink’s NGS-based proteomics here . Dr. Cindy Lawley, Senior Director of Global Population Health at Olink Proteomics, part of Thermo Fisher Scientific, has helped develop solutions to better understand genetic risk in diverse populations and holds several Excellence in Technology Transfer awards for her work. She currently drives population health initiatives, working closely with large cohort studies. Figure 2. The development pathway of the multiple sclerosis disease activity (MSDA) test. Discovery Study 2017 Panel Development 2018 Analytical Validation 2020 Clinical Validation 2023 300 Samples 650 Samples >3,000 Samples 600 Samples 1,416 Proteins 21 Proteins 18-plex MSDA Test4 References 1. Sun BB, Chiou J, Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329–338. doi:10.1038/s41586-023-06592-6 2. Gadd DA, Hillary RF, Kuncheva Z, et al. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. Nat Aging. 2024;4(7):939–948. doi:10.1038/s43587-024-00655-7 3. Papier K, Atkins JR, Tong TYN, et al. Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank. Nat Commun. 2024;15(1):4010. doi:10.1038/s41467-024-48017-6 4. Carrasco-Zanini J, Pietzner M, Davitte J, et al. Proteomic signatures improve risk prediction for common and rare diseases. Nat Med. 2024;30(9):2489–2498. doi:10.1038/s41591-024-03142-z 5. Qureshi F, Hu W, Loh L, et al. Analytical validation of a multi‐protein, serum‐based assay for disease activity assessments in multiple sclerosis. Proteomics Clin Appl. 2023;17(3):2200018. doi:10.1002/prca.202200018 6. Chitnis T, Foley J, Ionete C, et al. Clinical validation of a multi-protein, serum-based assay for disease activity assessments in multiple sclerosis. Clin Immunol. 2023;253:109688. doi:10.1016/j.clim.2023.109688 7. Sanchez A, Sheng E, Eagleman S, et al. Real-world clinical utility of a multi-protein, blood-based biomarker assay for disease activity assessments in multiple sclerosis. Mult Scler J Exp Transl Clin. 2025;11(2). doi:10.1177/20552173251331030 8. Chitnis T, Qureshi F, Gehman VM, et al. Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis. Nat Commun. 2024;15:4297. doi:10.1038/s41467-024-48602-9 www.olink.com Olink products and services are For Research Use Only and not for Use in Diagnostic Procedures. This document is not intended to convey any warranties, representations and/or recommendations of any kind, unless such warranties, representations and/or recommendations are explicitly stated. Olink assumes no liability arising from a prospective reader’s actions based on this document. OLINK and the Olink logotype are trademarks registered, or pending registration, by Olink Proteomics AB. © 2025 Olink Proteomics AB. All third-party trademarks are the property of their respective owners. Olink products and assay methods are covered by several patents and patent applications https://www.olink.com/patents/. Olink Proteomics, Dag Hammarskjölds väg 52B , SE-752 37 Uppsala, Sweden 1146, v2.0, 2022-10-1