Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.

Automated summary

Proteomic data are valuable for drug response prediction and biomarker discovery due to the direct interaction between most drugs and proteins in target cells. This review highlights the opportunities of combining large-scale proteomic data with drug-related research, focusing on oncology. Successful applications of drug response prediction using molecular data are discussed, along with the technical advances in data-independent acquisition mass spectrometry (DIA-MS) for biomarker discovery. The potential of machine learning in pharmacoproteomics and the challenges of clinical validation are also explored.