Opportunities for pharmacoproteomics in biomarker discovery

Abstract

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.

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

“…100 The advantages and disadvantages of using such model systems for biomarker studies are reviewed in ref. 21…”

“…14,16,17 This reveals a need for better biomarkers to improve survival and quality of life. 18,19 Large-scale proteomic studies provide potential avenues for revealing such biomarkers and novel therapeutic targets 20,21 (Figure 1).…”

“…There is an imperative requirement for studies of proteins as drug targets or response biomarkers in cancer, because proteins are ultimately responsible for cellular phenotypes and most anticancer drugs interact directly with proteins rather than DNA or RNA. [20][21][22] Proteomics can reveal insights not obtained from other 'omics. For example, there is an imperfect correlation between the transcriptome and proteome.…”

“…24 Cancer cell lines, organoids and PDXs are particularly informative for drug response studies, as these model systems can be used for drugscreening experiments assessing multiple anticancer agents. 21,79 The ProCan-DepMapSanger dataset was generated by a pan-cancer, multiomic study of 949 human cancer cell lines across 28 tissue types using DIA-MS. 25 The cell lines were also screened against 625 anticancer drugs, with deep learning used for drug response prediction. 25 More than 10% of the cell lines are from paediatric cancers, and ProCan-DepMapSanger data are available at http://cellmodelpassports.sanger.…”

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

“…They discussed the hurdles and proposed actions to move forward including integration of multiomics measurements, and development of targeted proteomic assays [14]. Poulos et al., from ProCan too, discussed the potential application of DIA‐MS from a different angle, that is drug discovery [15]. High‐throughput DIA analyses can be applied to analyze the perturbation proteome in tumor cells.…”