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)