HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft Patients.

BACKGROUND: Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT. METHODS: Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]). RESULTS: ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81). CONCLUSIONS: HEARTBiT's diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study.

BACKGROUND: Many risk models for predicting mortality, hospitalizations, or both in patients with heart failure have been developed but do not have sufficient discriminatory ability. The purpose of this study was to identify predictive biomarkers of hospitalizations in heart failure patients using omics-based technologies applied to blood and electrical monitoring of the heart. METHODS: Blood samples were collected from 58 heart failure patients during enrollment into this study. Each patient wore a 48-hour Holter monitor that recorded the electrical activity of their heart. The blood samples were profiled for gene expression using microarrays and protein levels using multiple reaction monitoring. Statistical deconvolution was used to estimate cellular frequencies of common blood cells. Classification models were developed using clinical variables, Holter variables, cell types, gene transcripts, and proteins to predict hospitalization status. RESULTS: Of the 58 patients recruited, 13 were hospitalized within 3 months after enrollment. These patients had lower diastolic and systolic blood pressures, higher brain natriuretic peptide levels, most had higher blood creatinine levels, and had been diagnosed with heart failure for a longer time period. The best-performing clinical model had an area under the receiver operating characteristic curve of 0.76. An ensemble biomarker panel consisting of Holter variables, cell types, gene transcripts, and proteins had an area under the receiver operating characteristic curve of 0.88. CONCLUSIONS: Molecular-based analyses as well as sensory data might provide sensitive biomarkers for the prediction of hospitalizations in heart failure patients. These approaches may be combined with traditional clinical models for the development of improved risk prediction models for heart failure.

Differentiating heart failure phenotypes using sex-specific transcriptomic and proteomic biomarker panels.

AIMS: Heart failure with preserved ejection fraction (HFpEF) accounts for 30-50% of patients with heart failure (HF). A major obstacle in HF management is the difficulty in differentiating between HFpEF and heart failure with reduced ejection fraction (HFrEF) using conventional clinical and laboratory investigations. The aim of this study is to develop robust transcriptomic and proteomic biomarker signatures that can differentiate HFpEF from HFrEF. METHODS AND RESULTS: A total of 210 HF patients were recruited in participating institutions from the Alberta HEART study. An expert clinical adjudicating panel differentiated between patients with HFpEF and HFrEF. The discovery cohort consisted of 61 patients, and the replication cohort consisted of 70 patients. Transcriptomic and proteomic data were analysed to find panels of differentiating HFpEF from HFrEF. In the discovery cohort, a 22-transcript panel was found to differentiate HFpEF from HFrEF in male patients with a cross-validation AUC of 0.74, as compared with 0.70 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) in those same patients. An ensemble of the transcript panel and NT-pro-BNP yielded a cross-validation AUC of 0.80. This performance improvement was also observed in the replication cohort. An ensemble of the transcriptomic panel with NT-proBNP produced a replication AUC of 0.90, as compared with 0.74 for NT-proBNP alone and 0.73 for the transcriptomic panel. CONCLUSIONS: We have identified a male-specific transcriptomic biomarker panel that can differentiate between HFpEF and HFrEF. These biosignatures could be further replicated on other patients and potentially be developed into a blood test for better management of HF patients.

Serum proteomics in multiple sclerosis disease progression.

Multiple sclerosis (MS) is associated with chronic degeneration of the central nervous system and may cause permanent neurological problems and considerable disability. While its causes remain unclear, its extensive phenotypic variability makes its prognosis and treatment difficult. The identification of serum proteomic biomarkers of MS progression could further our understanding of the molecular mechanisms related to MS disease processes. In the current study, we used isobaric tagging for relative and absolute protein quantification (iTRAQ) methodology and advanced multivariate statistical analysis to quantify and identify potential serum biomarker proteins of MS progression. We identified a panel of 11 proteins and combined them into a classifier that best classified samples into the two disease groups. The estimated area under the receiver operating curve of this classifier was 0.88 (p-value=0.017), with 86% sensitivity and specificity. The identified proteins encompassed processes related to inflammation, opsonization, and complement activation. Results from this study are in particular valuable to design a targeted Multiple Reaction Monitoring mass spectrometry based (MRM-MS) assay to conduct an external validation in an independent and larger cohort of patients. Validated biomarkers may result in the development of a minimally-invasive tool to monitor MS progression and complement current clinical practices. BIOLOGICAL SIGNIFICANCE: A hallmark of multiple sclerosis is the unpredictable disease course (progression). There are currently no clinically useful biomarkers of MS disease progression; most work has focused on the analysis of CSF, which requires an invasive procedure. Here, we explore the potential of proteomics to identify panels of serum biomarkers of disease progression in MS. By comparing the protein signatures of two challenging to obtain, but well-defined, MS phenotypic groups at the extremes of progression (benign and aggressive cases of MS), we identified proteins that encompass processes related to inflammation, opsonization, and complement activation. Findings require validation, but are an important step on the pathway to clinically useful biomarker discovery. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

Gender-specific plasma proteomic biomarkers in patients with Anderson-Fabry disease.

AIMS: Anderson-Fabry disease (AFD) is an important X-linked metabolic disease resulting in progressive end-organ involvement, with cardiac disease being the dominant determinant of survival in a gender-dependent manner. Recent epidemiological screening for AFD suggests the prevalence is much higher than previously recognized, with estimates of 1:3000. Our aim was to discover novel plasma biomarker signatures in adult patients with AFD. METHODS AND RESULTS: We used an unbiased proteomic screening approach to discover novel plasma biomarker signatures. In the discovery cohort of 46 subjects, 14 healthy controls and 32 patients with AFD, we used a mass spectrometry iTRAQ proteomic approach followed by multiple reaction monitoring (MRM) assays to identify biomarkers. Of the 38 protein groups discovered by iTRAQ, 18 already had existing MRM assays. Based on MRM, we identified an eight-protein biomarker panel (22 kDa protein, afamin, α1 antichymotrypsin, apolipoprotein E, ß-Ala His dipeptidase, haemoglobin α-2, isoform 1 of sex hormone-binding globulin, and peroxiredoxin 2) that was very specific and sensitive for male AFD patients. In female AFD patients, we identified a nine-marker panel of proteins with only three proteins, apolipoprotein E, haemoglobin α-2, and peroxiredoxin 2, common to both genders, suggesting a gender-specific alteration in plasma biomarkers in patients with AFD. The biomarkers were validated in plasma samples from 48 subjects using MRM, and they performed inferiorly in patients with heart failure. CONCLUSIONS: We have identified gender-specific plasma protein biomarker panels that are specific and sensitive for the AFD phenotype. The gender-specific panels offer important insight into potential differences in pathophysiology and prognosis between males and females with AFD.

Two-stage, in silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection.

Acute rejection is a major complication of solid organ transplantation that prevents the long-term assimilation of the allograft. Various populations of lymphocytes are principal mediators of this process, infiltrating graft tissues and driving cell-mediated cytotoxicity. Understanding the lymphocyte-specific biology associated with rejection is therefore critical. Measuring genome-wide changes in transcript abundance in peripheral whole blood cells can deliver a comprehensive view of the status of the immune system. The heterogeneous nature of the tissue significantly affects the sensitivity and interpretability of traditional analyses, however. Experimental separation of cell types is an obvious solution, but is often impractical and, more worrying, may affect expression, leading to spurious results. Statistical deconvolution of the cell type-specific signal is an attractive alternative, but existing approaches still present some challenges, particularly in a clinical research setting. Obtaining time-matched sample composition to biologically interesting, phenotypically homogeneous cell sub-populations is costly and adds significant complexity to study design. We used a two-stage, in silico deconvolution approach that first predicts sample composition to biologically meaningful and homogeneous leukocyte sub-populations, and then performs cell type-specific differential expression analysis in these same sub-populations, from peripheral whole blood expression data. We applied this approach to a peripheral whole blood expression study of kidney allograft rejection. The patterns of differential composition uncovered are consistent with previous studies carried out using flow cytometry and provide a relevant biological context when interpreting cell type-specific differential expression results. We identified cell type-specific differential expression in a variety of leukocyte sub-populations at the time of rejection. The tissue-specificity of these differentially expressed probe-set lists is consistent with the originating tissue and their functional enrichment consistent with allograft rejection. Finally, we demonstrate that the strategy described here can be used to derive useful hypotheses by validating a cell type-specific ratio in an independent cohort using the nanoString nCounter assay.

Variation in RNA-Seq transcriptome profiles of peripheral whole blood from healthy individuals with and without globin depletion.

BACKGROUND: The molecular profile of circulating blood can reflect physiological and pathological events occurring in other tissues and organs of the body and delivers a comprehensive view of the status of the immune system. Blood has been useful in studying the pathobiology of many diseases. It is accessible and easily collected making it ideally suited to the development of diagnostic biomarker tests. The blood transcriptome has a high complement of globin RNA that could potentially saturate next-generation sequencing platforms, masking lower abundance transcripts. Methods to deplete globin mRNA are available, but their effect has not been comprehensively studied in peripheral whole blood RNA-Seq data. In this study we aimed to assess technical variability associated with globin depletion in addition to assessing general technical variability in RNA-Seq from whole blood derived samples. RESULTS: We compared technical and biological replicates having undergone globin depletion or not and found that the experimental globin depletion protocol employed removed approximately 80% of globin transcripts, improved the correlation of technical replicates, allowed for reliable detection of thousands of additional transcripts and generally increased transcript abundance measures. Differential expression analysis revealed thousands of genes significantly up-regulated as a result of globin depletion. In addition, globin depletion resulted in the down-regulation of genes involved in both iron and zinc metal ion bonding. CONCLUSIONS: Globin depletion appears to meaningfully improve the quality of peripheral whole blood RNA-Seq data, and may improve our ability to detect true biological variation. Some concerns remain, however. Key amongst them the significant reduction in RNA yields following globin depletion. More generally, our investigation of technical and biological variation with and without globin depletion finds that high-throughput sequencing by RNA-Seq is highly reproducible within a large dynamic range of detection and provides an accurate estimation of RNA concentration in peripheral whole blood. High-throughput sequencing is thus a promising technology for whole blood transcriptomics and biomarker discovery.

Proteomic biomarkers of recovered heart function.

AIMS: Chronic heart failure is a costly epidemic that affects up to 2% of people in developed countries. The purpose of this study was to discover novel blood proteomic biomarker signatures of recovered heart function that could lead to more effective heart failure patient management by both primary care and specialty physicians. METHODS AND RESULTS: The discovery cohort included 41 heart transplant patients and 20 healthy individuals. Plasma levels of 138 proteins were detected in at least 75% of these subjects by iTRAQ mass spectrometry. Eighteen proteins were identified that had (i) differential levels between pre-transplant patients with end-stage heart failure and healthy individuals; and (ii) levels that returned to normal by 1 month post-transplant in patients with stable heart function after transplantation. Seventeen of the 18 markers were validated by multiple reaction monitoring mass spectrometry in a cohort of 39 heart failure patients treated with drug therapy, of which 30 had recovered heart function and 9 had not. This 17-protein biomarker panel had 93% sensitivity and 89% specificity, while the RAMP® NT-proBNP assay had the same specificity but 80% sensitivity. Performance further improved when the panel was combined with NT-proBNP, yielding a net reclassification index relative to NT-proBNP of 0.28. CONCLUSIONS: We have identified potential blood biomarkers of recovered heart function by harnessing data from transplant patients. These biomarkers can lead to the development of an inexpensive protein-based blood test that could be used by physicians to monitor response to therapy in heart failure, resulting in more personalized, front-line heart failure patient management.

Longitudinal analysis of whole blood transcriptomes to explore molecular signatures associated with acute renal allograft rejection.

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature.

Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.

BACKGROUND: Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. METHODS: Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. RESULTS: The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. CONCLUSIONS: Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.

Molecular signatures of end-stage heart failure.

BACKGROUND: To date, gene expression studies related to chronic heart failure (CHF) have mainly involved microarray analysis of myocardial tissues. The potential utility of blood to infer the etiology, pathogenesis, and course of CHF remains unclear. Further, the use of proteomic and metabolomic platforms for molecular profiling of CHF is relatively unexplored. METHODS: Microarray genomic, iTRAQ proteomic, and nuclear magnetic resonance metabolomic analyses were carried out on blood samples from 29 end-stage CHF patients (16 ischemic heart disease [IHD], 13 nonischemic cardiomyopathy [NICM]), and 20 normal cardiac function (NCF) controls. Robust statistical tests and bioinformatical tools were applied to identify and compare the molecular signatures among these subject groups. RESULTS: No genes or proteins, and only two metabolites, were differentially expressed between IHD and NICM patients at end stage. However, CHF versus NCF comparison revealed differential expression of 7,426 probe sets, 71 proteins, and 8 metabolites. Functional enrichment analyses of the CHF versus NCF results revealed several in-common biological themes and potential mechanisms underlying advanced heart failure. CONCLUSION: Multiple "-omic" analyses support the convergence of dramatic changes in molecular processes underlying IHD and NICM at end stage.

Evolutionary concepts in biobanking - the BC BioLibrary.

BACKGROUND: Medical research to improve health care faces a major problem in the relatively limited availability of adequately annotated and collected biospecimens. This limitation is creating a growing gap between the pace of scientific advances and successful exploitation of this knowledge. Biobanks are an important conduit for transfer of biospecimens (tissues, blood, body fluids) and related health data to research. They have evolved outside of the historical source of tissue biospecimens, clinical pathology archives. Research biobanks have developed advanced standards, protocols, databases, and mechanisms to interface with researchers seeking biospecimens. However, biobanks are often limited in their capacity and ability to ensure quality in the face of increasing demand. Our strategy to enhance both capacity and quality in research biobanking is to create a new framework that repatriates the activity of biospecimen accrual for biobanks to clinical pathology. METHODS: The British Columbia (BC) BioLibrary is a framework to maximize the accrual of high-quality, annotated biospecimens into biobanks. The BC BioLibrary design primarily encompasses: 1) specialized biospecimen collection units embedded within clinical pathology and linked to a biospecimen distribution system that serves biobanks; 2) a systematic process to connect potential donors with biobanks, and to connect biobanks with consented biospecimens; and 3) interdisciplinary governance and oversight informed by public opinion. RESULTS: The BC BioLibrary has been embraced by biobanking leaders and translational researchers throughout BC, across multiple health authorities, institutions, and disciplines. An initial pilot network of three Biospecimen Collection Units has been successfully established. In addition, two public deliberation events have been held to obtain input from the public on the BioLibrary and on issues including consent, collection of biospecimens and governance. CONCLUSION: The BC BioLibrary framework addresses common issues for clinical pathology, biobanking, and translational research across multiple institutions and clinical and research domains. We anticipate that our framework will lead to enhanced biospecimen accrual capacity and quality, reduced competition between biobanks, and a transparent process for donors that enhances public trust in biobanking.

Whole blood genomic biomarkers of acute cardiac allograft rejection.

BACKGROUND: Significant progress has been made in cardiac transplantation over the past 30 years; however, the means for detection of acute cardiac allograft rejection remains in need of improvement. At present, the endomyocardial biopsy, an invasive and inconvenient procedure for patients, is required for the surveillance and diagnosis of acute cardiac allograft rejection. In the Biomarkers in Transplantation initiative, we investigated gene expression profiles in peripheral blood of cardiac transplant subjects as potential biomarkers for diagnosis of allograft rejection. METHODS: Whole blood samples were obtained from 28 cardiac transplant subjects who consented to the study. Serial samples were collected from pre-transplant through 3 years post-transplant according to the standard protocol. Temporally correspondent biopsies were also collected, reviewed in a blinded manner, and graded according to current ISHLT guidelines. Blood samples were analyzed using Affymetrix microarrays. Genomic profiles were compared in subjects with acute rejection (AR; ISHLT Grade > or =2R) and no rejection (NR; Grade 0R). Biomarker panel genes were identified using linear discriminant analysis. RESULTS: We found 1,295 differentially expressed probe-sets between AR and NR samples and developed a 12-gene biomarker panel that classifies our internal validation samples with 83% sensitivity and 100% specificity. CONCLUSIONS: Based on our current results, we believe whole blood genomic biomarkers hold great potential in the diagnosis of acute cardiac allograft rejection. A prospective, Canada-wide trial will be conducted shortly to further evaluate the classifier panel in diverse patients and a range of clinical programs.