Data Analysis Pipelines, Potential Pitfalls, and Troubleshooting for Mass Spectrometry-Based Biomarker Discovery and Validation

Protein biomarkers can be used in a clinical setting to assess the efficacy of therapeutic intervention or disease progression. In addition, they can be used to monitor compliance with treatment regimens and adverse effects associated with a particular medication, vaccine, or drug. Mass spectrometry (MS)-based proteomics technologies for biomarker discovery have yielded positive results because changes in protein expression and abundance, function, or structure can be used as indicators of pathological abnormalities. However, some significant challenges remain, which need to be overcome to achieve high-throughput biomarker discovery, as the integration of protein profiling data into a comprehensive entity is still challenging. In addition, knowledge about the specific functions of proteins and their biological interactions is still missing. Thus, the result and interpretation have to be carefully handled. This chapter highlights the advantages of proteomics in biomarker discovery, the pipeline used for proteomic data analysis, and the current pitfalls ranging from data interpretation to translation of results into a biologically useful meaning. It also focuses on some methods that can be used to overcome the pitfalls in MS-based biomarker discovery.