A review published in Science Bulletin and a separate review in Advanced Cancer Research examine the application of high-throughput proteomics and artificial intelligence in precision medicine, with a specific focus on disease prediction, drug discovery, and oncology.
Background on Proteomics
Proteomics is the study of proteins, the functional molecules that link genetic information (genomics) to physiological processes. Unlike genomics, which provides static information, proteomics offers a dynamic view of biological changes. High-throughput technology enables the measurement of thousands of proteins from a single sample.
Different biological samples provide distinct insights:
- Blood: Facilitates large-scale studies.
- Cerebrospinal fluid: Provides specific neurological information.
- Urine and tissue samples: Offer data relevant to specific diseases.
Applications in Disease Prediction
Proteomic models can outperform conventional risk scores for cardiovascular disease and identify neurodegeneration years before clinical diagnosis.
Proteomic models can be used to predict disease risk by reflecting real-time biological states influenced by both genetics and external factors. Studies indicate that protein-based models can outperform conventional risk scores for cardiovascular disease.
Protein profiles may also identify individuals at risk of developing neurodegenerative diseases, such as dementia or Parkinson's disease, years before a clinical diagnosis. Smaller protein panels have been used to predict multiple disease processes simultaneously, including metabolic, cardiovascular, and neurodegenerative conditions. The concept of a "biological aging clock" uses protein patterns to estimate biological age, which may differ from chronological age.
Role in Oncology
A review published in Advanced Cancer Research discusses proteomics-driven precision oncology. The review states that proteomics helps bridge the gap between genotype and phenotype that is not fully captured by genomic data alone, by profiling protein abundance, post-translational modifications, and signaling pathway activities.
Key points from this review include:
- Technological advancement: Advances in mass spectrometry enable high-resolution, large-scale analysis across bulk tissues, single cells, and spatial contexts.
- Biomarker discovery: Mass spectrometry-based proteomics facilitates cancer biomarker discovery.
- Accelerated translation: Artificial intelligence-assisted multi-omics integration is expected to accelerate clinical translation in precision oncology, enabling predictive and clinically interpretable models.
Drug Discovery
In the context of drug discovery, proteomics enables improved identification of therapeutic targets. Methods such as activity-based protein profiling and thermal proteome profiling are used to identify drug targets and potential side effects. In cancer treatment, proteomics may help guide personalized therapy for heterogeneous diseases.
Artificial Intelligence Analysis
AI techniques, including LASSO, recursive feature elimination, and support vector machines, are used to identify biomarkers and build predictive models from proteomic data. The tool AlphaFold predicts protein structures to improve understanding of protein function and interaction. AI can integrate proteomics with genomics and clinical records for a comprehensive health assessment.
Challenges and Future Directions
Several challenges currently limit the widespread clinical adoption of proteomics, including technical variability, cost, and regulatory barriers.
Several challenges currently limit the widespread clinical adoption of proteomics:
- Technical variability: Variability in sample preparation, a lack of standardized protocols, and differences in analytical methods affect data reliability.
- Cost and complexity: High costs and technical complexity limit clinical implementation.
- Statistical challenges: Overfitting in machine learning models and data heterogeneity present statistical hurdles.
- Regulatory barriers: Regulatory processes slow the translation of proteomic research into clinical practice.
Future progress depends on developing standardized workflows, improving data sharing, strengthening external validation, and integrating proteomics with other omics techniques.
References
- The review in Science Bulletin was conducted by Chinese researchers.
- The review in Advanced Cancer Research is referenced as: Shi Y, Yang S, Chen Y, Chen J, Hao B. Proteomics-driven precision oncology: from molecular profiling to biomarker discovery. Adv. Cancer Res. 2026(1):0002.