Towards clinical applications of selected reaction monitoring for plasma protein biomarker studies

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.

Plasma-Based Proteomics Profiling of Patients with Hyperthyroidism after Antithyroid Treatment

Thyroid hormones critically modulate body homeostasis and haemostasis by regulating energy and metabolism. Previous studies have focused on individual pathways or proteins that are affected by increases in thyroid hormone levels, while an overall plasma proteomic signature of this increased level is lacking. Herein, an integrated untargeted proteomic approach with network analysis was used to identify changes in circulating proteins in the plasma proteome between hyperthyroid and euthyroid states. Plasma from 10 age-matched subjects at baseline (hyperthyroid) and post treatment with carbimazole (euthyroid) was compared by difference gel electrophoresis (DIGE) and matrix-assisted laser desorption/ionization time of flight (MALDI TOF) mass spectrometry (MS). A total of 20 proteins were identified with significant difference in abundance (analysis of variance (ANOVA) test, p ≤ 0.05; fold-change ≥ 1.5) between the two states (12 increased and 8 decreased in abundance in the hyperthyroid state). Twelve protein spots corresponding to ten unique proteins were significantly more abundant in the hyperthyroid state compared with the euthyroid state. These increased proteins were haptoglobin (HP), hemopexin (HPX), clusterin (CLU), apolipoprotein L1 (APOL1), alpha-1-B glycoprotein (A1BG), fibrinogen gamma chain (FGG), Ig alpha-1 chain C region (IGHA1), complement C6 (C6), leucine rich alpha 2 glycoprotein (LRG1), and carboxypeptidase N catalytic chain (CPN1). Eight protein spots corresponding to six unique proteins were significantly decreased in abundance in the hyperthyroid samples compared with euthyroid samples. These decreased proteins were apolipoprotein A1 (APOA1), inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4), plasminogen (PLG), alpha-1 antitrypsin (SERPINA1), fibrinogen beta chain (FGB), and complement C1r subcomponent (C1R). The differentially abundant proteins were investigated by ingenuity pathway analysis (IPA). The network pathway identified related to infectious disease, inflammatory disease, organismal injury and abnormalities, and the connectivity map focused around two central nodes, namely the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and p38 mitogen-activated protein kinase (MAPK) pathways. The plasma proteome of patients with hyperthyroidism revealed differences in the abundance of proteins involved in acute phase response signaling, and development of a hypercoagulable and hypofibrinolytic state. Our findings enhance our existing knowledge of the altered proteins and associated biochemical pathways in hyperthyroidism.

Total and isoform-specific quantitative assessment of circulating fibulin-1 using selected reaction monitoring MS and time-resolved immunofluorometry

PURPOSE

Targeted proteomics using SRM-MS combined with stable-isotope dilution has emerged as a promising quantitative technique for the study of circulating protein biomarkers. The purpose of this study was to develop and characterize robust quantitative assays for the emerging cardiovascular biomarker fibulin-1 and its circulating isoforms in human plasma.

EXPERIMENTAL DESIGN

We used bioinformatics analysis to predict total and isoform-specific tryptic peptides for absolute quantitation using SRM-MS. Fibulin-1 was quantitated in plasma by nanoflow-LC-SRM-MS in undepleted plasma and time-resolved immunofluorometric assay (TRIFMA). Both methods were validated and compared to a commercial ELISA (CircuLex). Molecular size determination was performed under native conditions by SEC analysis coupled to SRM-MS and TRIFMA.

RESULTS

Absolute quantitation of total fibulin-1, isoforms -1C, and -1D was performed by SRM-MS. Fibulin-1C was the most abundant isoform in plasma. Circulating fibulin-1 isoforms were homo -or hetero multimeric complexes (range 318-364 kDa). Good correlation was obtained between SRM-MS and TRIFMA but not CircuLex.

CONCLUSIONS AND CLINICAL RELEVANCE

For biomarker studies using smaller cohorts, SRM-MS provides an alternative measure of total and specific fibulin-1 isoforms in undepleted plasma. For larger cohorts TRIFMA provides a faster platform for fibulin-1 quantitation in plasma. While the correlation between these methods was acceptable, low correlation was obtained between the commercial CircuLex assay and SRM-MS or TRIFMA.

Mass spectrometry-based proteomic quest for diabetes biomarkers

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia, which affects hundreds of millions of individuals worldwide. Early diagnosis and complication prevention of DM are helpful for disease treatment. However, currently available DM diagnostic markers fail to achieve the goals. Identification of new diabetic biomarkers assisted by mass spectrometry (MS)-based proteomics may offer solution for the clinical challenges. Here, we review the current status of biomarker discovery in DM, and describe the pressure cycling technology (PCT)-Sequential Window Acquisition of all Theoretical fragment-ion (SWATH) workflow for sample-processing, biomarker discovery and validation, which may accelerate the current quest for DM biomarkers. This article is part of a Special Issue entitled: Medical Proteomics.

Combining bioinformatics and MS-based proteomics: clinical implications

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.