Biomarkers are endogenous molecules whose objective and quantitative measurement indicates the presence of a certain biological state, condition or the presence of disease. Because the majority of efforts for biomarker discovery of infectious responses are focused on biofluids, where the mRNA or DNA may be absent, proteomics hold special promise for biomarker discovery of airway diseases.
The protein biomarker development pipeline refers to at least four essential process components ─ discovery, verification, assay development, and clinical validation. Embedded in each step is statistical validation and modeling.
The initial discovery phase aims to produce a list of biomarker candidates resulting from disease-associated differential expression, aberrant posttranslational modifications (PTMs) or alterative splicing. The candidates in discovery datasets are prioritized and refined based on clinical information of the samples, the statistical significance of any difference between cases and controls, their relevance based on analysis of a biological knowledge base of the disease and bioinformatic analysis of relevant gene expression and protein interaction networks. Many robust discovery-phase platforms have been established, including two-dimensional gel electrophoresis (2DE), SILAC, iTRAQ, labeling free LC-MS, protein arrays, ELISA and aptamer methods.
The UTMB CPC has employed 2DE, iTRAQ and ELISA in its projects, with preseparations technologies to reduce sample complexity. The selection of approachs are made on the type and complexity of the sample, and the experimental needs. In several of its projects, the UTMB CPC has developed an integrated automated preseparations technology termed the biofluid analysis platform that enables 2DE and LC-MS peptide profiling.
Qualification is a step to address the high false discovery rate inherent discovery phase. Qualification is therefore involves the development of a highly specific quantitative assay to confirm the differential expression of a putative biomarker. The reasons for this high false discovery rate of differentially include the imprecision in quantifying low-abundance biomarkers in complex fluids such as plasma, random sampling characteristic of mass spectrometry data acquisition, low confidence identifications of low abundance proteins and limitations in study power. Qualification and verification are approached using targeted quantitative proteomics assay termed selected reaction monitoring (SRM).
Stable isotope dilution (SID) in combination with SRM has emerged as a favorable alternative to immunoassays for verification of candidate biomarkers. In a SID-SRM-MS assay, one or two signature proteotypic peptides are selected to stoichiometrically represent the protein candidates of interest. The SID-SRM analysis is performed on a triple quadrupole mass spectrometer (termed QQQ-MS). In this assay, the precursor ion is preselected in the first mass filter (Q1), and induced to fragment by collision-induced dissociation (CID) in second quadrupole (Q2). Several preselected fragments are mass analyzed by the third mass filter (Q3). The signals of the fragment ions are then monitored over the chromatographic elution time. SID-SRM-MS assays offers several attractive features. First, the noise level is significantly reduced and thereby MRM assays decrease the lower detection limit for peptides by up to 100-fold in comparison to full scan MS/MS analysis. Second, the precursor-product ion transition is unique to the protein of origin, producing a characteristic signature for the protein of interest. Therefore, the two filtering stages in MRM mode result in near-absolute structural specificity for the target protein, representing its most critical advantage over immunoassays.
In the verification phase, differential candidate expression observed in the discovery phase is confirmed through a quantitative approach and the correlation of these candidates to the disease is verified over a relatively large population of patients. The outcome of the verification phase is a short list of high-confidence candidate biomarkers which show differential expression between the disease and control, are consistently detected in a large population of patients, have independent and the highest predictive power.