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Opportunities for pharmacoproteomics in biomarker discovery
Author(s) -
Poulos Rebecca C.,
Cai Zhaoxiang,
Robinson Phillip J.,
Reddel Roger R.,
Zhong Qing
Publication year - 2023
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.202200031
Subject(s) - biomarker discovery , drug discovery , proteomics , computer science , data science , computational biology , biomarker , drug response , resource (disambiguation) , big data , precision medicine , field (mathematics) , bioinformatics , drug , data mining , biology , pharmacology , computer network , biochemistry , mathematics , pure mathematics , gene , genetics
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.