Introduction: Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient's disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.
Objectives: We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.
Methods: A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.
Results: We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.
Conclusion: These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.
Keywords: AIR, acute inflammatory response; ALK, anaplastic lymphoma kinase; ANG, angiogenesis; APR, acute phase reaction; BRCA1/2, Breast Cancer Gene 1, Breast Cancer Gene 2; Biological scores; Biomarker; CA, complement activation; CI, confidence interval; CPH, Cox proportional hazards; CV, coefficient of variation; ECM, extracellular matrix organization; EGFR, epidermal growth factor receptor; FDA, US Food and Drug Administration; GLY, glycolysis; HR, hazard ratio; HbA1c, hemoglobin A1c; IFN1, interferon type 1 signaling and response; IFNg, Interferon γ signaling and response; IRn, type n immune response; IT, immune tolerance; LC MS-MS, liquid chromatography with tandem mass spectrometry; MALDI ToF, matrix-assisted laser desorption/ionization time of flight; MRM, multiple reaction monitoring; MS, mass spectral; Mass spectrometry; NSCLC, non-small cell lung cancer; OS, overall survival; PC, principal component; PCA, principal component analysis; PCn, principal component n; PD-1, programmed cell death protein 1; PD-L1, programmed death-ligand 1; Proteomics; QC, quality control; Serum proteome; Set enrichment analysis; WH, wound healing; m/Z, mass/charge.
© 2020 The Authors.