Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Abstract

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Keywords: Biomarker discovery; Cancer; Clinical proteomics; Mass spectrometry; Proteogenomics; Targeted assay.

Fig. 1

Overview of clinical cancer proteomics strategies. a Various sample types are used for clinical proteomics. These include solid tumor tissues, patient body fluids, animal models and cell-based systems. Tumor tissues are obtained either as surgically resected samples or are biopsy based. There are a number of tissue processing approaches available, which include the analysis of “bulk” tissue or preferentially after pathological inspection, tissue macro-dissection or laser capture microdissection (LCM). Patient fluids are a popular source for the discovery of biomarkers. The most commonly used patient body fluids include blood (processed to plasma or serum) and urine. Animal models are a popular in vivo model system for clinical proteomics. The most common models include transgenic disease models and patient-derived xenografts (PDX). Cell-based systems continue to be popular model systems in cancer biology. They include immortalized cancer cell lines or more sophisticated organoid systems that are established using defined culture conditions and primary patient material. Samples obtain from these sources are homogenized and proteolytically digested prior to proteomic analyses (i.e. bottom-up proteomics). b Proteomic analyses can use several well-established workflows. These include label-free proteomics (LFQ), isobaric labelling strategies or the specific enrichment of post-translational modification such as phosphorylation, ubiquitination, glycosylation, etc. c Integration of proteomics data with publicly available resources such as the CPTAC proteomics data or transcriptional profiles from GTEx, CCLE and TCGA can be used for biomarker prioritization. d Bioinformatics analyses (clustering, enrichment, pathways, etc.) are used to extract biological content or further prioritize candidates for targeted proteomics validation, using multiple reaction monitoring (MRM) and Parallel reaction monitoring (PRM)