Multiplexed measurement of candidate blood protein biomarkers of heart failure.

AIMS: There is a critical need for better biomarkers so that heart failure can be diagnosed at an earlier stage and with greater accuracy. The purpose of this study was to design a robust mass spectrometry (MS)-based assay for the simultaneous measurement of a panel of 35 candidate protein biomarkers of heart failure, in blood. The overall aim was to evaluate the potential clinical utility of this biomarker panel for prediction of heart failure in a cohort of 500 patients. METHODS AND RESULTS: Multiple reaction monitoring (MRM) MS assays were designed with Skyline and Spectrum Mill PeptideSelector software and developed using nanoflow reverse phase C18 chromatographic Chip Cube-based separation, coupled to a 6460 triple quadrupole mass spectrometer. Optimized MRM assays were applied, in a sample-blinded manner, to serum samples from a cohort of 500 patients with heart failure and non-heart failure (non-HF) controls who had cardiovascular risk factors. Both heart failure with reduced ejection fraction (HFrEF) patients and heart failure with preserved ejection fraction (HFpEF) patients were included in the study. Peptides for the Apolipoprotein AI (APOA1) protein were the most significantly differentially expressed between non-HF and heart failure patients (P = 0.013 and P = 0.046). Four proteins were significantly differentially expressed between non-HF and the specific subtypes of HF (HFrEF and HFpEF); Leucine-rich-alpha-2-glycoprotein (LRG1, P < 0.001), zinc-alpha-2-glycoprotein (P = 0.005), serum paraoxanse/arylesterase (P = 0.013), and APOA1 (P = 0.038). A statistical model found that combined measurements of the candidate biomarkers in addition to BNP were capable of correctly predicting heart failure with 83.17% accuracy and an area under the curve (AUC) of 0.90. This was a notable improvement on predictive capacity of BNP measurements alone, which achieved 77.1% accuracy and an AUC of 0.86 (P = 0.005). The protein peptides for LRG1, which contributed most significantly to model performance, were significantly associated with future new onset HF in the non-HF cohort [Peptide 1: odds ratio (OR) 2.345 95% confidence interval (CI) (1.456-3.775) P = 0.000; peptide 2: OR 2.264 95% CI (1.422-3.605), P = 0.001]. CONCLUSIONS: This study has highlighted a number of promising candidate biomarkers for (i) diagnosis of heart failure and subtypes of heart failure and (ii) prediction of future new onset heart failure in patients with cardiovascular risk factors. Furthermore, this study demonstrates that multiplexed measurement of a combined biomarker signature that includes BNP is a more accurate predictor of heart failure than BNP alone.

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer.

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave-one-out cross-validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C-Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.

Genetic variants in PPARGC1B and CNTN4 are associated with thromboxane A2 formation and with cardiovascular event free survival in the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT).

BACKGROUND AND AIMS: Elevated urinary 11-dehydro thromboxane B2 (TxB2), a measure of thromboxane A2 formation in vivo, predicts future atherothrombotic events. To further understand this relationship, the genetic determinants of 11-dehydro TxB2 and their associations with cardiovascular morbidity were investigated in this study. METHODS: Genome-wide and targeted genetic association studies of urinary 11-dehydro TxB2 were conducted in 806 Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) participants. RESULTS: The strongest associations were in PPARGC1B (rs4235745, rs32582, rs10515638) and CNTN4 (rs10510230, rs4684343), these 5 single nucleotide polymorphisms (SNPs) were independently associated with 11-dehydro TxB2 formation. Haplotypes of 11-dehydro TxB2 increasing alleles for both PPARGC1B and CNTN4 were significantly associated with 11-dehydro TxB2, explaining 5.2% and 4.5% of the variation in the whole cohort, and 8.8% and 7.9% in participants not taking aspirin, respectively. In a second ASCOT population (n = 6199), addition of these 5 SNPs significantly improved the covariate-only Cox proportional hazards model for cardiovascular events (chisq = 14.7, p=0.01). Two of the risk alleles associated with increased urinary 11-dehydro TxB2 were individually associated with greater incidences of cardiovascular events - rs10515638 (HR = 1.31, p=0.01) and rs10510230 (HR = 1.25, p=0.007); effect sizes were larger in those not taking aspirin. CONCLUSIONS: PPARGC1B and CNTN4 genotypes are associated with elevated thromboxane A2 formation and with an excess of cardiovascular events. Aspirin appears to blunt these associations. If specific protection of PPARGC1B and CNTN4 variant carriers by aspirin is confirmed by additional studies, PPARGC1B and CNTN4 genotyping could potentially assist in clinical decision making regarding the use of aspirin in primary prevention.

Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis.

OBJECTIVE: Biological therapies, which include antitumour necrosis factor-α and T-cell inhibitors, are potentially effective treatments for psoriatic arthritis (PsA) but are costly and may induce a number of side effects. Response to treatment in PsA is variable and difficult to predict. Here, we sought to identify a panel of protein biomarkers that could be used to predict which patients diagnosed with PsA will respond to biologic treatment. METHODS: An integrated discovery to targeted proteomics approach was used to investigate the protein profiles of good and non-responders to biological treatments in patients with PsA. Reverse-phase liquid chromatography coupled to tandem mass spectrometry was used to generate protein profiles of synovial tissue obtained at baseline from 10 patients with PsA. Targeted proteomics using multiple reaction monitoring (MRM) was used to confirm and prevalidate a potential protein biomarker panel in 18 and 7 PsA patient samples, respectively. RESULTS: A panel of 107 proteins was selected, and targeted mass spectrometry MRM assays were successfully developed for 57 of the proteins. The 57 proteins include S100-A8, S100-A10, Ig kappa chain C fibrinogen-α and γ, haptoglobin, annexin A1 and A2, collagen alpha-2, vitronectin, and alpha-1 acid glycoprotein. The proteins were measured simultaneously and confirmed to be predictive of response to treatment with an area under the curve of 0.76. In a blinded study using a separate cohort of patients, the panel was able to predict response to treatment. CONCLUSIONS: The approach reported here and the initial data provide evidence that a multiplexed protein assay of a panel of biomarkers that predict response to treatment could be developed. TRIAL REGISTRATION NUMBER: ISRCTN23328456.

Development of a label-free LC-MS/MS strategy to approach the identification of candidate protein biomarkers of disease recurrence in prostate cancer patients in a clinical trial of combined hormone and radiation therapy.

PURPOSE: Combined hormone and radiation therapy (CHRT) is one of the principle curative regimes for localised prostate cancer (PCa). Following treatment, many patients subsequently experience disease recurrence however; current diagnostics tests fail to predict the onset of disease recurrence. Biomarkers that address this issue would be of significant advantage. EXPERIMENTAL DESIGN: Label-free LC-MS/MS for protein biomarker discovery and MRM for targeted confirmation were applied to patient serum samples accrued in a non-interventional clinical trial of CHRT. RESULTS: Analysis of time-matched patient samples from a patient with disease recurrence compared with a time match disease-free individual supported the identification of 287 proteins. Of these, 141 proteins were quantified, 95 proteins changed in their expression (P ≤ 0.05 and ≥1.5-fold change) and of these 16 were selected for MRM confirmation. The protein expression changes observed in the label-free LC-MS/MS and MRM analysis were found to be highly correlated (R(2) = 0.85). CONCLUSIONS AND CLINICAL RELEVANCE: The establishment of a clinical trial to support the acquisition of samples and development of a pipeline for MS-based biomarker discovery and validation should contribute to the identification of a serum protein signature to predict or monitor the outcome of treatment of patients with PCa.

Dose- and time-dependent antiplatelet effects of aspirin.

Aspirin is widely used, but dosages in different clinical situations and the possible importance of "aspirin resistance" are debated. We performed an open cross-over study comparing no treatment (baseline) with three aspirin dosage regimens--37.5 mg/day for 10 days, 320 mg/day for 7 days, and, finally, a single 640 mg dose (cumulative dose 960 mg)--in 15 healthy male volunteers. Platelet aggregability was assessed in whole blood (WB) and platelet rich plasma (PRP). The urinary excretions of stable thromboxane (TxM) and prostacyclin (PGI-M) metabolites, and bleeding time were also measured. Platelet COX inhibition was nearly complete already at 37.5 mg aspirin daily, as evidenced by >98% suppression of serum thromboxane B2 and almost abolished arachidonic acid (AA) induced aggregation in PRP 2-6 h after dosing. Bleeding time was similarly prolonged by all dosages of aspirin. Once daily dosing was associated with considerable recovery of AA induced platelet aggregation in WB after 24 hours, even after 960 mg aspirin. Collagen induced aggregation in WB with normal extracellular calcium levels (hirudin anticoagulated) was inhibited <40% at all dosages. TxM excretion was incompletely suppressed, and increased <24 hours after the cumulative 960 mg dose. Aspirin treatment reduced PGI-M already at the lowest dosage (by approximately 25%), but PGI-M excretion and platelet aggregability were not correlated. Antiplatelet effects of aspirin are limited in WB with normal calcium levels. Since recovery of COX-dependent platelet aggregation occurred within 24 hours, once daily dosing of aspirin might be insufficient in patients with increased platelet turnover.