Mass cytometry analysis reveals altered immune profiles in patients with coronary artery disease.

Objective: The importance of inflammation in atherosclerosis is well accepted, but the role of the adaptive immune system is not yet fully understood. To further explore this, we assessed the circulating immune cell profile of patients with coronary artery disease (CAD) to identify discriminatory features by mass cytometry. Methods: Mass cytometry was performed on patient samples from the BioHEART-CT study, gated to detect 82 distinct cell subsets. CT coronary angiograms were analysed to categorise patients as having CAD (CAD+) or having normal coronary arteries (CAD-). Results: The discovery cohort included 117 patients (mean age 61 ± 12 years, 49% female); 79 patients (68%) were CAD+. Mass cytometry identified changes in 15 T-cell subsets, with higher numbers of proliferating, highly differentiated and cytotoxic cells and decreases in naïve T cells. Five T-regulatory subsets were related to an age and gender-independent increase in the odds of CAD incidence when expressing CCR2 (OR 1.12), CCR4 (OR 1.08), CD38 and CD45RO (OR 1.13), HLA-DR (OR 1.06) and Ki67 (OR 1.22). Markers of proliferation and differentiation were also increased within B cells, while plasmacytoid dendritic cells were decreased. This combination of changes was assessed using SVM models in discovery and validation cohorts (area under the curve = 0.74 for both), confirming the robust nature of the immune signature detected. Conclusion: We identified differences within immune subpopulations of CAD+ patients which are indicative of a systemic immune response to coronary atherosclerosis. This immune signature needs further study via incorporation into risk scoring tools for the precision diagnosis of CAD.

Lipidomics Profiling and Risk of Coronary Artery Disease in the BioHEART-CT Discovery Cohort.

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD- (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals.

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk.

Cardiovascular disease remains a leading cause of mortality with an estimated half a billion people affected in 2019. However, detecting signals between specific pathophysiology and coronary plaque phenotypes using complex multi-omic discovery datasets remains challenging due to the diversity of individuals and their risk factors. Given the complex cohort heterogeneity present in those with coronary artery disease (CAD), we illustrate several different methods, both knowledge-guided and data-driven approaches, for identifying subcohorts of individuals with subclinical CAD and distinct metabolomic signatures. We then demonstrate that utilizing these subcohorts can improve the prediction of subclinical CAD and can facilitate the discovery of novel biomarkers of subclinical disease. Analyses acknowledging cohort heterogeneity through identifying and utilizing these subcohorts may be able to advance our understanding of CVD and provide more effective preventative treatments to reduce the burden of this disease in individuals and in society as a whole.

Third-party CMV- and EBV-specific T-cells for first viral reactivation after allogeneic stem cell transplant.

Virus-specific T-cells (VSTs) from third-party donors mediate short- and long-term antiviral effects in allogeneic hematopoietic stem cell transplant (HSCT) recipients with relapsed or refractory viral infections. We investigated early administration of third-party VSTs, together with antiviral therapy in patients requiring treatment for first cytomegalovirus (CMV) or Epstein-Barr virus (EBV) infection. Thirty HSCT patients were treated with 1 to 4 VST infusions (2 × 107 cells/m2; CMV n=27, EBV n=3) at a median of 4 days after initiation of antiviral treatment. The overall viral response rate was 100%, with a complete response (CR) rate of 94%. Of the 28 patients who achieved a CR, 23 remained virus PCR negative (n=9) or below quantitation limit (n=14) for the duration of follow-up. Four patients had brief episodes of quantifiable reactivation not requiring additional therapy, and one required a second infusion after initial CR, remaining PCR negative thereafter. All 3 patients treated for EBV post-transplant lymphoproliferative disorder achieved sustained CR. Rates of aGVHD and cGVHD after infusion were 13% and 23%, respectively. There were no serious infusion-related adverse events. VST infusion was associated with rapid recovery of CD8+CD45RA-CD62L- and a slower recovery of CD4+CD45RA-CD62L- effector memory T-cells; CMV-specific T-cells comprised up to 13% of CD8+ cells. At 1 year post-transplant, non-relapse mortality was 10%, cumulative incidence of relapse was 7%, overall survival was 88% and 25 of 27 patients had ECOG status of 0 or 1. Early administration of third-party VSTs in conjunction with antiviral treatment appears safe and leads to excellent viral control and clinical outcomes. Registered on Australian New Zealand Clinical Trials Registry as #ACTRN12618000343202.

Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study.

Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12-1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02-0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09-2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04-0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23-1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01-3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53-0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26-2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59-3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14-0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.