Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation.

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

Proteomic signatures in plasma during early acute renal allograft rejection.

Acute graft rejection is an important clinical problem in renal transplantation and an adverse predictor for long term graft survival. Plasma biomarkers may offer an important option for post-transplant monitoring and permit timely and effective therapeutic intervention to minimize graft damage. This case-control discovery study (n = 32) used isobaric tagging for relative and absolute protein quantification (iTRAQ) technology to quantitate plasma protein relative concentrations in precise cohorts of patients with and without biopsy-confirmed acute rejection (BCAR). Plasma samples were depleted of the 14 most abundant plasma proteins to enhance detection sensitivity. A total of 18 plasma proteins that encompassed processes related to inflammation, complement activation, blood coagulation, and wound repair exhibited significantly different relative concentrations between patient cohorts with and without BCAR (p value <0.05). Twelve proteins with a fold-change >or=1.15 were selected for diagnostic purposes: seven were increased (titin, lipopolysaccharide-binding protein, peptidase inhibitor 16, complement factor D, mannose-binding lectin, protein Z-dependent protease and beta(2)-microglobulin) and five were decreased (kininogen-1, afamin, serine protease inhibitor, phosphatidylcholine-sterol acyltransferase, and sex hormone-binding globulin) in patients with BCAR. The first three principal components of these proteins showed clear separation of cohorts with and without BCAR. Performance improved with the inclusion of sequential proteins, reaching a primary asymptote after the first three (titin, kininogen-1, and lipopolysaccharide-binding protein). Longitudinal monitoring over the first 3 months post-transplant based on ratios of these three proteins showed clear discrimination between the two patient cohorts at time of rejection. The score then declined to baseline following treatment and resolution of the rejection episode and remained comparable between cases and controls throughout the period of quiescent follow-up. Results were validated using ELISA where possible, and initial cross-validation estimated a sensitivity of 80% and specificity of 90% for classification of BCAR based on a four-protein ELISA classifier. This study provides evidence that protein concentrations in plasma may provide a relevant measure for the occurrence of BCAR and offers a potential tool for immunologic 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.

Multiplexed LC-ESI-MRM-MS-based Assay for Identification of Coronary Artery Disease Biomarkers in Human Plasma.

PURPOSE: A highly-multiplexed LC-ESI-multiple reaction monitoring-MS-based assay is developed for the identification of coronary artery disease (CAD) biomarkers in human plasma. EXPERIMENTAL DESIGN: The assay is used to measure 107 stable isotope labeled peptide standards and native peptides from 64 putative biomarkers of cardiovascular diseases in tryptic digests of plasma from subjects with (n = 70) and without (n = 45) angiographic evidence of CAD and no subsequent cardiovascular mortality during follow-up. RESULTS: Extensive computational and statistical analysis reveals six plasma proteins associated with CAD, namely apolipoprotein CII, C reactive protein, CD5 antigen-like, fibronectin, inter alpha trypsin inhibitor heavy chain H1, and protein S. The identified proteins are combined into a LASSO-logistic score with high classification performance (cross-validated area under the curve = 0.74). When combined with a separate score computed from markers currently used in the clinic with similar performance, the area under the receiver operating curve increases to 0.84. Similar results are observed in an independent set of subjects (n = 87). CONCLUSIONS AND CLINICAL RELEVANCE: If externally validated, the assay and identified biomarkers can improve CAD risk stratification.

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.

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.

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.

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.

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.

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.

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.

Alteration of human blood cell transcriptome in uremia.

BACKGROUND: End-stage renal failure is associated with profound changes in physiology and health, but the molecular causation of these pleomorphic effects termed "uremia" is poorly understood. The genomic changes of uremia were explored in a whole genome microarray case-control comparison of 95 subjects with end-stage renal failure (n = 75) or healthy controls (n = 20). METHODS: RNA was separated from blood drawn in PAXgene tubes and gene expression analyzed using Affymetrix Human Genome U133 Plus 2.0 arrays. Quality control and normalization was performed, and statistical significance determined with multiple test corrections (qFDR). Biological interpretation was aided by knowledge mining using NIH DAVID, MetaCore and PubGene RESULTS: Over 9,000 genes were differentially expressed in uremic subjects compared to normal controls (fold change: -5.3 to +6.8), and more than 65% were lower in uremia. Changes appeared to be regulated through key gene networks involving cMYC, SP1, P53, AP1, NFkB, HNF4 alpha, HIF1A, c-Jun, STAT1, STAT3 and CREB1. Gene set enrichment analysis showed that mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and genes involved in tumor genesis were prominently lower in uremia, while insulin-like growth factor activity, neuroactive receptor interaction, the complement system, lipoprotein metabolism and lipid transport were higher in uremia. Pathways involving cytoskeletal remodeling, the clathrin-coated endosomal pathway, T-cell receptor signaling and CD28 pathways, and many immune and biological mechanisms were significantly down-regulated, while the ubiquitin pathway and certain others were up-regulated. CONCLUSIONS: End-stage renal failure is associated with profound changes in human gene expression which appears to be mediated through key transcription factors. Dialysis and primary kidney disease had minor effects on gene regulation, but uremia was the dominant influence in the changes observed. This data provides important insight into the changes in cellular biology and function, opportunities for biomarkers of disease progression and therapy, and potential targets for intervention in uremia.

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.

Plasma protein biosignatures for detection of cardiac allograft vasculopathy.

BACKGROUND: Coronary angiography remains the most widely used tool for routine screening and diagnosis of cardiac allograft vasculopathy (CAV), a major pathologic process that develops in 50% of cardiac transplant recipients beyond the first year after transplant. Given the invasiveness, expense, discomfort, and risk of complications associated with angiography, a minimally invasive alternative that is sensitive and specific would be highly desirable for monitoring CAV in patients. METHODS: Plasma proteomic analysis using isobaric tags for relative and absolute quantitation-matrix-assisted laser desorption ionization double time-of-flight mass spectrometry was carried out on samples from 40 cardiac transplant patients (10 CAV, 9 non-significant CAV, 21 possible CAV). Presence of CAV was defined as left anterior descending artery diameter stenosis ≥ 40% by digital angiography and quantitatively measured by blinded expert appraisal. Moderated t-test robust-linear models for microarray data were used to identify biomarkers that are significantly differentially expressed between patient samples with CAV and with non-significant CAV. A proteomic panel for diagnosis of CAV was generated using the Elastic Net classification method. RESULTS: We identified an 18-plasma protein biomarker classifier panel that was able to classify and differentiate patients with angiographically significant CAV from those without significant CAV, with an 80% sensitivity and 89% specificity, while providing insight into the possible underlying immune and non-alloimmune contributory mechanisms of CAV. CONCLUSION: Our results support of the potential utility of proteomic biomarker panels as a minimally invasive means to identify patients with significant, angiographically detectable coronary artery stenosis in the cardiac allograft, in the context of post-cardiac transplantation monitoring and screening for CAV. The potential biologic significance of the biomarkers identified may also help improve our understanding of CAV pathophysiology.

White blood cell differentials enrich whole blood expression data in the context of acute cardiac allograft rejection.

Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) ≤ 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data***.

Effects of sample timing and treatment on gene expression in early acute renal allograft rejection.

BACKGROUND: We have shown that genomic biomarkers in peripheral blood provide evidence of early graft rejection and may offer an important option for posttransplant monitoring, and we are working to improve this signature to maximize assay performance. METHODS: This clinical refinement study (n=79) used gene expression profiling in a case-control design to compare whole blood samples between normal subjects (n=20) and patients with (n=20) or without (n=39) biopsy-confirmed acute rejection (BCAR). RESULTS: Gene expression in peripheral blood from subjects with BCAR before treatment differed significantly from that of normal subjects and transplant recipients without BCAR. Hierarchical clustering and principal component analysis showed that samples obtained 1 to 5 days after the start of treatment of BCAR were segregated across both groups before treatment or without BCAR and that this was closely related to the time lag between treatment and sampling. Genes differentially expressed during BCAR included FKSG49, LMAN2, NFYC, LIMK2, JUNB, NASP, MALAT1, ITGAX, HLA-J, FKBP1A, and RBMS1, and gene ontology analysis highlighted changes in networks related to cytoskeletal reorganization, apoptosis, and immune signaling, whereas after treatment change highlighted pathways of cellular metabolism, cell-cycle regulation, DNA damage, and apoptosis. CONCLUSION: Gene expression in the peripheral blood is associated with BCAR, and the pattern of expression changes rapidly after treatment. This may offer a potential tool for diagnosis of rejection and immunologic monitoring of response to treatment, which is now being evaluated in a large multicenter international study.

Whole blood biomarkers of acute cardiac allograft rejection: double-crossing the biopsy.

BACKGROUND: Acute rejection is still a significant barrier to long-term survival of the allograft. Current acute rejection diagnostic methods are not specific enough or are invasive. There have been a number of studies that have explored the blood or the biopsy to discover genomic biomarkers of acute rejection; however, none of the studies to date have used both. METHODS: We analyzed endomyocardial biopsy tissue and whole blood-derived messenger RNA from 11 acute rejection and 20 nonrejection patients using Affymetrix Human Genome U133 Plus 2.0 chips. We used a novel approach and gained insight into the biology of rejection based on gene expression in the biopsy, and applied this knowledge to the blood analysis to identify novel blood biomarkers. RESULTS: We identified probesets that are differentially expressed between acute rejection and nonrejection patients in the biopsy and blood, and developed three biomarker panels: (1) based on biopsy-only (area under the curve=0.85), (2) based on biopsy-targeted whole blood (area under the curve=0.83), and (3) based on whole blood-only (area under the curve=0.60) analyses. CONCLUSIONS: Most of the probesets replicated between biopsy and blood are regulated in opposite direction between the two sources of information. We also observed that the biopsy-targeted blood biomarker discovery approach can improve performance of the biomarker panel. The biomarker panel developed using this targeted approach is able to diagnose acute cardiac allograft rejection almost as well as the biopsy-only based biomarker panel.

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.