Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium

Abstract

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Keywords: artificial intelligence in proteomics; biomarker discovery; clinical proteomics; mass spectrometry–based proteomic; personalized medicine.

Graphical abstract

Fig. 1

Participants of the 68th Benzon Foundation Symposium gathered at two historic venues: the iconic Radisson Collection Royal Hotel, formerly known as the SAS Hotel (left) and the Ceremonial Hall of the University of Copenhagen (right). Photo credits: Ashok Kumar Jayavelu, Jennifer Gillete, and Janina Albrecht.

Fig. 2

Spatial proteomics: From tissue to single-cell resolution. This schematic illustrates spatial proteomics technologies and applications discussed at the symposium. Spatial proteomics, depicted as a tissue section embedded in a microprocessor-inspired design, leverages companion technologies (left, blue hexagons) to study diverse clinical applications (right, beige hexagons). Community resources (top, green hexagons), such as the Human Protein Atlas and the upcoming Deep Visual Proteomics (DVP) Atlas, accelerate the field.

Fig. 3

Biomarker-discovery pipeline: From concept to clinic.A, schematic representation of key stakeholders in the clinical proteomics landscape, including patients, health care providers, epidemiologist, insurers, regulatory bodies, and scientists, all of whom influence the translation process. B, the five major stages of biomarker development (top) from identifying clinical need through to implementation with common roadblocks at different prevalence and scale scenarios (bottom). A common barrier is inappropriate study design, which prevents identification of meaningful biomarker candidates from the onset. Even with an appropriate study design, biomarker development can fail at multiple points: discovery-phase findings may not validate in broader populations; validated biomarker may lack sufficient cost–benefit advantages over existing methods. Beyond regulatory approval, successful translation into clinical practice requires careful consideration of study design, population representation, health care impact, and institutional support.

Fig. 4

Artificial intelligence (AI) in precision medicine: From biomedical data integration to improved patient care. The central brain-gear motif represents AI’s dual role in processing biomedical data (left) and improving patient care (right). On the data integration side, AI integrates proteomics data with advanced molecular diagnostics, clinical diagnostics, database searches, and electronic health records. This comprehensive analysis drives improved patient care through large language model (LLM)–assisted clinical decision support, patient monitoring, drug response prediction, and preventive medicine approaches.