Biomarkers serve as the cornerstone of clinical diagnostics, but the journey from discovery to clinical application is filled with many challenges. The limitations of traditional protein biomarker discovery methods are evident; while effective in scanning thousands of proteins, they often fall short during the crucial clinical validation step, especially for complex and multifactorial diseases like cancer. In fact, less than two biomarkers receive regulatory approval for clinical diagnostics annually.
While traditional, data-driven biomarker discovery methods have their merits, they often struggle to translate into clinically validated diagnostics. This is where a mechanism-based approach provides a significant advantage. By integrating multi-omics data (proteomics, genomics and transcriptomics) and advanced computational methods, this approach can elucidate the mechanism of a disease and the specific roles that candidate biomarkers play. As a result, the biomarkers identified not only show statistically significant differences between the studied conditions, but are directly related to the disease mechanism. This ensures a more robust and clinically applicable set of biomarkers by prioritizing the hits that are directly connected to the underlying mechanism.
To implement a successful mechanism-based biomarker discovery pipeline, four key elements must be integrated:
- Multi-omics data gathering (proteomics, transcriptomics, genomics) often from different but matched sample matrices.
- Knowledge base incorporation in the form of biological pathways, protein networks, clinical data and drug-disease/target associations.
- Advanced bioinformatics algorithms, such as pathway analysis, differential protein/gene expression and network prioritization.
- Experimental validation of the candidate biomarkers using assays on well-established platforms (e.g. Luminex).
In terms of multi-omics data gathering, the biomarker discovery pipeline can be further augmented by High-Plex protein analysis based on the Proximity Extension Assay (PEA) technology. This technology couples affinity-based proteomics with NGS readouts, which not only allows for the highly sensitive and specific high-throughput analysis of numerous protein candidates, but also enables the seamless integration of proteomics with additional NGS data sources (genomics & transcriptomics). By synergizing these elements, this approach can yield statistically and biologically significant biomarker candidates, enhancing their clinical applicability.
Mechanism-based biomarkers offer a new dimension in diagnostics by not only indicating the presence of a disease but also providing insights into its underlying mechanisms. Such biomarkers could be instrumental in tackling complex diseases and enhancing patient outcomes.
References: Antoranz, Asier, et al. “Mechanism-based biomarker discovery.” Drug discovery today 22.8 (2017): 1209-1215.