A proposal for score assignment to characterize biological processes from mass spectral analysis of serum

Abstract

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.

Fig. 1

Flowchart of the score creation process.

Fig. 2

Histograms showing score distributions for the Score Development Set (“DEVELOPMENT”) and the Score Validation Set (“VALIDATION”), with inset percentiles, for the biological processes: complement activation, glycolysis, wound healing (all from PC1) and type 17 immune response (from PC3).

Fig. 3

Concordance plots showing the reproducibility of the scores generated from three independent spectral acquisitions (Run 1, Run 2, and Run 3) for samples in the Early Stage Lung Cancer Set. Scores shown are A – Complement Activation, B – Glycolysis, C – Wound Healing, (all from first principal component) and D – Type 17 Immune Response (from third principal component (PC3)). The corresponding least squares regression lines and statistics are shown for Run 2 vs Run 1 (in red) and Run 3 vs Run 1 (in blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Heatmaps of correlation matrix between pairs of scores for different biological processes across samples from different cohorts: Late Stage Lung Cancer (Score Validation Set), Early Stage Lung Cancer Set, Melanoma Set, Ovarian Cancer Set. The biological processes are abbreviated as AIR: Acute inflammatory response, ANG: Angiogenesis, APR: Acute phase reaction, CA: Complement activation, ECM: Extracellular matrix organization, GLY: Glycolysis, IFNg: Interferon γ signaling and response, IFN1: Interferon type 1, IT: Immune tolerance, WH: Wound healing, IR1: Type 1 immune response, IR2: Type 2 immune response, IR17: Type 17 immune response. Correlation matrix elements <0.5 are shown in dark blue. All scores are derived from the first principal component vector, apart from IR17, which is from the third principal component vector. Only one score per process (lowest PC) is illustrated. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Histograms of distributions of Complement Activation Score, Glycolysis Score, Wound Healing Score, and Type 17 Immune Response Score for four indications: ovarian cancer patients (Ovarian Cancer Set), advanced melanoma patients (Melanoma Set), early stage lung cancer patients (Early Stage Lung Cancer Set), late stage lung cancer patients (Score Validation Set).

Fig. 6

Kaplan-Meier plots of overall survival for the Ovarian Cancer Set, the Melanoma Set, and the Score Validation Set (advanced stage NSCLC) stratified by score high (above median for cohort) and score low (below median for cohort) for Complement Activation Score, Glycolysis Score, Wound Healing Score (all from first PC) and Type 17 Immune Response (Type 17 IR) Score (from third PC).