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Pilot study identifying distinct circulating proteomic profiles associated with longitudinal CT-defined fibrotic and inflammatory sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis exhibits heterogeneous clinical trajectories ranging from self-limited disease resolution to chronic progressive fibrosis, yet reliable biomarkers capable of distinguishing these disease patterns remain lacking. Whether longitudinal CT-defined sarcoidosis phenotypes are associated with distinct circulating molecular signatures remains unknown. METHODS: We performed high-throughput plasma proteomics (SomaScan 11K) in participants with pulmonary sarcoidosis classified into longitudinal chest CT-defined progressive fibrosis, progressive nodular inflammatory disease, or resolving disease trajectories, along with healthy controls. CT phenotypes were assigned based on predefined longitudinal changes in reticulation, traction bronchiectasis, nodular involvement, and mediastinal lymphadenopathy across serial CT scans. One plasma sample per participant was selected from the study visit corresponding to the CT time point at which criteria for the assigned longitudinal phenotype were met. Principal component analysis, hierarchical clustering, pathway enrichment, and correlation-based analyses linking protein expression to quantitative CT features were used to evaluate whether distinct longitudinal CT phenotypes were associated with divergent proteomic signatures. RESULTS: Principal component analysis and hierarchical clustering suggested partial segregation by CT-defined phenotype. Longitudinal CT phenotypes were associated with distinct pathway-level proteomic signatures, with progressive fibrosis enriched for epithelial-mesenchymal transition signaling, and progressive nodular inflammatory disease enriched for mTORC1, MYC, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways. Correlation analyses showed coordinated protein-expression patterns associated with fibrotic CT features and mediastinal lymph node enlargement. DISCUSSION: These findings suggest that longitudinal CT-defined fibrotic and inflammatory sarcoidosis phenotypes are associated with distinct pathway-level proteomic signatures. This pilot study provides preliminary proof-of-concept evidence that integrating longitudinal CT imaging phenotypes with plasma proteomics may serve as a framework for future mechanistic studies and biomarker discovery in pulmonary sarcoidosis.

Humans↗

Plasma proteomics and coronary artery calcium score: synergistic, concordant and contrasting predictions of cardiovascular outcomes in The Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Coronary artery calcium (CAC) scores inform subclinical atherosclerotic cardiovascular disease (ASCVD) burden, helping guide preventative treatments. However, prediction of cardiovascular (CV) events by CAC is largely limited to ASCVD outcomes. This study investigated whether a previously validated proteomic test for predicting a broad composite of four-year CV events could enhance the prognostic utility of CAC. METHODS: We used a 27-protein CV risk score (Prot-CVR), derived from ~5,000 SomaScan&#x2122; Assay plasma protein measurements, to predict four-year risk of a composite CV and mortality outcome (myocardial infarction, stroke/TIA, heart failure hospitalization, death) in 2,122 participants with &#x2265;1 CV risk factors from the Multi-Ethnic Study of Atherosclerosis (MESA) observational cohort at exam 5 and compared predictions to CAC Agatston scores. Discriminatory performance was assessed using C-Index and 4-year area under the curve (AUC). Cox Proportional Hazard (CoxPH) ratios were calculated for the composite outcome, ASCVD outcome (myocardial infarction, resuscitated cardiac arrest, stroke, coronary heart disease death), and individual events. Changes in Prot-CVR and CAC scores from baseline to MESA exam 5 (+10-years) in CV event versus event-free participants were assessed using 2-tailed paired t-tests. CoxPH regression models of CV event status distributed by Prot-CVR, CAC, and relevant co-variates were evaluated for performance relative to individual models. RESULTS: Individual Prot-CVR and CAC models predicting the composite outcome had comparable 4-year AUCs, but Prot-CVR had a higher C-index (0.68 (0.65-0.70) versus 0.63 (0.60-0.65), p=0.001) and greater hazard ratios for the composite outcome (p<0.001), death (p<0.001), and heart failure (p=0.015). A combined CoxPH model of Prot-CVR + CAC + Age had a higher 4-year AUC (0.72, p<0.05) and C-Index (0.71, p<0.05) than Prot-CVR or CAC alone. Both Prot-CVR and CAC scores detected an increase in risk prior to an approaching CV event in ~10-year sensitivity-to-change analysis. For 49.6% of MESA population with CAC=0 at baseline, Prot-CVR was greater in composite event versus event free participants at 4 years (0.23 versus 0.15, p=0.006) and full follow-up (0.18 versus 0.13, p<0.001). CONCLUSION: Protein testing complements CAC for CV risk assessment although the improvement is modest. Prot-CVR may resolve which patients with CAC=0 are at heightened CV risk.

Journal Article↗

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology.

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Humans↗