The impact of IgG subclass deficiency on the risk of mortality in hospitalized patients with COPD.

BACKGROUND: Immunoglobulin G (IgG) deficiency increases the risk of acute exacerbations and mortality in chronic obstructive pulmonary disease (COPD). However, the impact of IgG subclass deficiency on mortality in COPD is unknown. Here, we determined which IgG subclass, if any, is associated with increased risk of mortality in COPD. METHODS: We measured serum IgG subclass concentrations of 489 hospitalized patients with COPD who were enrolled in the Rapid Transition Program (clinicaltrials.gov identifier NCT02050022). To evaluate the impact of IgG subclass deficiency on 1-year mortality, Cox proportional hazards regression analyses were performed with adjustments for potential confounders. RESULTS: Deficiencies in IgG1, IgG2, IgG3, and IgG4 were present in 1.8%, 12.1%, 4.3%, and 11.2% of patients, respectively. One-year mortality was 56% in patients with IgG1 deficiency, 27% in IgG2 deficiency, 24% in IgG3 deficiency, and 31% in IgG4 deficiency. Cox proportional modeling showed that IgG1 and IgG4 deficiencies increased the 1-year mortality risk with an adjusted hazard ratio of 3.92 (95% confidence interval [CI] = 1.55-9.87) and 1.74 (95% CI = 1.02-2.98), respectively. Neither IgG2 nor IgG3 deficiency significantly increased 1-year mortality. Two or more IgG subclass deficiencies were observed in 5.3%. Patients with 2 or more IgG subclass deficiencies had a higher 1-year mortality than those without any deficiencies (46.2% vs. 19.7%, p < 0.001), with an adjusted hazard ratio of 2.22 (95% CI = 1.18-4.17). CONCLUSIONS: IgG1 and IgG4 deficiency was observed in 1.8% and 11.2% of hospitalized patients with COPD, respectively, and these deficiencies were associated with a significantly increased risk of 1-year mortality.

Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system.

BACKGROUND: Manually extracted data points from health records are collated on an institutional, provincial, and national level to facilitate clinical research. However, the labour-intensive clinical chart review process puts an increasing burden on healthcare system budgets. Therefore, an automated information extraction system is needed to ensure the timeliness and scalability of research data. METHODS: We used a dataset of 100 synoptic operative and 100 pathology reports, evenly split into 50 reports in training and test sets for each report type. The training set guided our development of a Natural Language Processing (NLP) extraction pipeline system, which accepts scanned images of operative and pathology reports. The system uses a combination of rule-based and transfer learning methods to extract numeric encodings from text. We also developed visualization tools to compare the manual and automated extractions. The code for this paper was made available on GitHub. RESULTS: A test set of 50 operative and 50 pathology reports were used to evaluate the extraction accuracies of the NLP pipeline. Gold standard, defined as manual extraction by expert reviewers, yielded accuracies of 90.5% for operative reports and 96.0% for pathology reports, while the NLP system achieved overall 91.9% (operative) and 95.4% (pathology) accuracy. The pipeline successfully extracted outcomes data pertinent to breast cancer tumor characteristics (e.g. presence of invasive carcinoma, size, histologic type), prognostic factors (e.g. number of lymph nodes with micro-metastases and macro-metastases, pathologic stage), and treatment-related variables (e.g. margins, neo-adjuvant treatment, surgical indication) with high accuracy. Out of the 48 variables across operative and pathology codebooks, NLP yielded 43 variables with F-scores of at least 0.90; in comparison, a trained human annotator yielded 44 variables with F-scores of at least 0.90. CONCLUSIONS: The NLP system achieves near-human-level accuracy in both operative and pathology reports using a minimal curated dataset. This system uniquely provides a robust solution for transparent, adaptable, and scalable automation of data extraction from patient health records. It may serve to advance breast cancer clinical research by facilitating collection of vast amounts of valuable health data at a population level.

Epigenetic marker of telomeric age is associated with exacerbations and hospitalizations in chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an age-related condition that has been associated with early telomere attrition; the clinical implications of telomere shortening in COPD are not well known. In this study we aimed to determine the relationship of the epigenetic regulation of telomeric length in peripheral blood with the risk of exacerbations and hospitalization in patients with COPD. METHODS: Blood DNA methylation profiles were obtained from 292 patients with COPD enrolled in the placebo arm of the Macrolide Azithromycin to Prevent Rapid Worsening of Symptoms Associated with Chronic Obstructive Pulmonary Disease (MACRO) Study and who were followed for 1-year. We calculated telomere length based on DNA methylation markers (DNAmTL) and related this biomarker to the risk of exacerbation and hospitalization and health status (St. George Respiratory Questionnaire [SGRQ]) score over time using a Cox proportional hazards model. We also used linear models to investigate the associations of DNAmTL with the rates of exacerbation and hospitalization (adjusted for chronological age, lung function, race, sex, smoking, body mass index and cell composition). RESULTS: Participants with short DNAmTL demonstrated increased risk of exacerbation (P = 0.02) and hospitalization (P = 0.03) compared to those with longer DNAmTL. DNAmTL age acceleration was associated with higher rates of exacerbation (P = 1.35 × 10-04) and hospitalization (P = 5.21 × 10-03) and poor health status (lower SGRQ scores) independent of chronological age (P = 0.03). CONCLUSION: Telomeric age based on blood DNA methylation is associated with COPD exacerbation and hospitalization and thus a promising biomarker for poor outcomes in COPD.

Analytical Validation of HEARTBiT: A Blood-Based Multiplex Gene Expression Profiling Assay for Exclusionary Diagnosis of Acute Cellular Rejection in Heart Transplant Patients.

BACKGROUND: HEARTBiT is a whole blood-based gene profiling assay using the nucleic acid counting NanoString technology for the exclusionary diagnosis of acute cellular rejection in heart transplant patients. The HEARTBiT score measures the risk of acute cellular rejection in the first year following heart transplant, distinguishing patients with stable grafts from those at risk for acute cellular rejection. Here, we provide the analytical performance characteristics of the HEARTBiT assay and the results on pilot clinical validation. METHODS: We used purified RNA collected from PAXgene blood samples to evaluate the characteristics of a 12-gene panel HEARTBiT assay, for its linearity range, quantitative bias, precision, and reproducibility. These parameters were estimated either from serial dilutions of individual samples or from repeated runs on pooled samples. RESULTS: We found that all 12 genes showed linear behavior within the recommended assay input range of 125 ng to 500 ng of purified RNA, with most genes showing 3% or lower quantitative bias and around 5% coefficient of variation. Total variation resulting from unique operators, reagent lots, and runs was less than 0.02 units standard deviation (SD). The performance of the analytically validated assay (AUC = 0.75) was equivalent to what we observed in the signature development dataset. CONCLUSION: The analytical performance of the assay within the specification input range demonstrated reliable quantification of the HEARTBiT score within 0.02 SD units, measured on a 0 to 1 unit scale. This assay may therefore be of high utility in clinical validation of HEARTBiT in future biomarker observational trials.

Sputum Microbiome Is Associated with 1-Year Mortality after Chronic Obstructive Pulmonary Disease Hospitalizations.

Rationale: Lung dysbiosis promotes airway inflammation and decreased local immunity, potentially playing a role in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Objectives: We sought to determine the relationship between sputum microbiome at the time of AECOPD hospitalization and 1-year mortality in a COPD cohort. Methods: We used sputum samples from 102 patients hospitalized because of AECOPD. All subjects were followed for 1 year after discharge. The microbiome profile was assessed through sequencing of 16S rRNA gene. Microbiome analyses were performed according to 1-year mortality status. To investigate the effect of α-diversity measures and taxon features on time to death, we applied Cox proportional hazards regression models and obtained hazard ratios (HRs) associated with these variables. Measurements and Main Results: We observed significantly lower values of α-diversity (richness, Shannon index, evenness, and Faith's Phylogenetic Diversity) among nonsurvivors (n = 19, 18.6%) than survivors (n = 83, 81.4%). ß-Diversity analysis also demonstrated significant differences between both groups (adjusted permutational multivariate ANOVA, P = 0.010). The survivors had a higher relative abundance of Veillonella; in contrast, nonsurvivors had a higher abundance of Staphylococcus. The adjusted HRs for 1-year mortality increased significantly with decreasing α-diversity. We also observed lower survival among patients in whom sputum samples were negative for Veillonella (HR, 13.5; 95% confidence interval, 4.2-43.9; P < 0.001) or positive for Staphylococcus (HR, 7.3; 95% confidence interval, 1.6-33.2; P = 0.01). Conclusions: The microbiome profile of sputum in AECOPD is associated with 1-year mortality and may be used to identify subjects with a poor prognosis at the time of hospitalization.

Thrombospondin-1 and disease progression in dysferlinopathy.

The purpose of this study was to determine whether thrombospondin (TSP)-1 promotes macrophage activity and disease progression in dysferlinopathy. First, we found that levels of TSP-1 are elevated in blood of non-ambulant dysferlinopathy patients compared with ambulant patients and healthy controls, supporting the idea that TSP-1 levels are correlated with disease progression. We then crossed dysferlinopathic BlaJ mice with TSP-1 knockout mice and assessed disease progression longitudinally with magnetic resonance imaging (MRI). In these mice, deletion of TSP-1 ameliorated loss in volume and mass of the moderately affected gluteal muscle but not of the severely affected psoas muscle. T2 MRI parameters revealed that loss of TSP-1 modestly inhibited inflammation only in gluteal muscle of male mice. Histological assessment indicated that deletion of TSP-1 reduced inflammatory cell infiltration of muscle fibers, but only early in disease progression. In addition, flow cytometry analysis revealed that, in males, TSP-1 knockout reduced macrophage infiltration and phagocytic activity, which is consistent with TSP-1-enhanced phagocytosis and pro-inflammatory cytokine induction in cultured macrophages. In summary, TSP-1 appears to play an accessory role in modulating Mp activity in BlaJ mice in a gender, age and muscle-dependent manner, but is unlikely a primary driver of disease progression of dysferlinopathy.

Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease.

BACKGROUND: Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS: After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS: Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case-control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases (P = 0.03). Derived within this same study, the pCAD model significantly improved classification (P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case-control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE (P = 0.015) and pCAD (P = 0.001) models. CONCLUSIONS: Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health.

Effect of short-term oral prednisone therapy on blood gene expression: a randomised controlled clinical trial.

BACKGROUND: Effects of systemic corticosteroids on blood gene expression are largely unknown. This study determined gene expression signature associated with short-term oral prednisone therapy in patients with chronic obstructive pulmonary disease (COPD) and its relationship to 1-year mortality following an acute exacerbation of COPD (AECOPD). METHODS: Gene expression in whole blood was profiled using the Affymetrix Human Gene 1.1 ST microarray chips from two cohorts: 1) a prednisone cohort with 37 stable COPD patients randomly assigned to prednisone 30 mg/d + standard therapy for 4 days or standard therapy alone and 2) the Rapid Transition Program (RTP) cohort with 218 COPD patients who experienced AECOPD and were treated with systemic corticosteroids. All gene expression data were adjusted for the total number of white blood cells and their differential cell counts. RESULTS: In the prednisone cohort, 51 genes were differentially expressed between prednisone and standard therapy group at a false discovery rate of < 0.05. The top 3 genes with the largest fold-changes were KLRF1, GZMH and ADGRG1; and 21 genes were significantly enriched in immune system pathways including the natural killer cell mediated cytotoxicity. In the RTP cohort, 27 patients (12.4%) died within 1 year after hospitalisation of AECOPD; 32 of 51 genes differentially expressed in the prednisone cohort significantly changed from AECOPD to the convalescent state and were enriched in similar cellular immune pathways to that in the prednisone cohort. Of these, 10 genes including CX3CR1, KLRD1, S1PR5 and PRF1 were significantly associated with 1-year mortality. CONCLUSIONS: Short-term daily prednisone therapy produces a distinct blood gene signature that may be used to determine and monitor treatment responses to prednisone in COPD patients during AECOPD. TRIAL REGISTRATION: The prednisone cohort was registered at clinicalTrials.gov ( NCT02534402 ) and the RTP cohort was registered at ClinicalTrials.gov ( NCT02050022 ).

Integrative Genomics of Emphysema-Associated Genes Reveals Potential Disease Biomarkers.

Chronic obstructive pulmonary disease is the third leading cause of death worldwide. Gene expression profiling across multiple regions of the same lung identified genes significantly related to emphysema. We sought to determine whether the lung and epithelial expression of 127 emphysema-related genes was also related to lung function in independent cohorts, and whether any of these genes could be used as biomarkers in the peripheral blood of patients with chronic obstructive pulmonary disease. To that end, we examined whether the expression levels of these genes were under genetic control in lung tissue (n = 1,111). We then determined whether the mRNA levels of these genes in lung tissue (n = 727), small airway epithelial cells (n = 238), and peripheral blood (n = 620) were significantly related to lung function measurements. The expression of 63 of the 127 genes (50%) was under genetic control in lung tissue. The lung and epithelial mRNA expression of a subset of the emphysema-associated genes, including ASRGL1, LPHN2, and EDNRB, was strongly associated with lung function. In peripheral blood, the expression of 40 genes was significantly associated with lung function. Twenty-nine of these genes (73%) were also associated with lung function in lung tissue, but with the opposite direction of effect for 24 of the 29 genes, including those involved in hypoxia and B cell-related responses. The integrative genomics approach uncovered a significant overlap of emphysema genes associations with lung function between lung and blood with opposite directions between the two. These results support the use of peripheral blood to detect disease biomarkers.

Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles.

BACKGROUND: Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform. RESULTS: We present 'Enumerateblood', a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data. CONCLUSIONS: 'Enumerateblood' significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website.

Airway hyperresponsiveness in chronic obstructive pulmonary disease: A marker of asthma-chronic obstructive pulmonary disease overlap syndrome?

BACKGROUND: The impact of airway hyperreactivity (AHR) on respiratory mortality and systemic inflammation among patients with chronic obstructive pulmonary disease (COPD) is largely unknown. We used data from 2 large studies to determine the relationship between AHR and FEV1 decline, respiratory mortality, and systemic inflammation. OBJECTIVES: We sought to determine the relationship of AHR with FEV1 decline, respiratory mortality, and systemic inflammatory burden in patients with COPD in the Lung Health Study (LHS) and the Groningen Leiden Universities Corticosteroids in Obstructive Lung Disease (GLUCOLD) study. METHODS: The LHS enrolled current smokers with mild-to-moderate COPD (n = 5887), and the GLUCOLD study enrolled former and current smokers with moderate-to-severe COPD (n = 51). For the primary analysis, we defined AHR by a methacholine provocation concentration of 4 mg/mL or less, which led to a 20% reduction in FEV1 (PC20). RESULTS: The primary outcomes were FEV1 decline, respiratory mortality, and biomarkers of systemic inflammation. Approximately 24% of LHS participants had AHR. Compared with patients without AHR, patients with AHR had a 2-fold increased risk of respiratory mortality (hazard ratio, 2.38; 95% CI, 1.38-4.11; P = .002) and experienced an accelerated FEV1 decline by 13.2 mL/y in the LHS (P = .007) and by 12.4 mL/y in the much smaller GLUCOLD study (P = .079). Patients with AHR had generally reduced burden of systemic inflammatory biomarkers than did those without AHR. CONCLUSIONS: AHR is common in patients with mild-to-moderate COPD, affecting 1 in 4 patients and identifies a distinct subset of patients who have increased risk of disease progression and mortality. AHR may represent a spectrum of the asthma-COPD overlap phenotype that urgently requires disease modification.

SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

BACKGROUND: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. RESULTS: The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. CONCLUSIONS: The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

Discovery of novel plasma protein biomarkers to predict imminent cystic fibrosis pulmonary exacerbations using multiple reaction monitoring mass spectrometry.

BACKGROUND: Despite the significant morbidity and mortality related to pulmonary exacerbations in cystic fibrosis (CF), there remains no reliable predictor of imminent exacerbation. OBJECTIVE: To identify blood-based biomarkers to predict imminent (<4 months from stable blood draw) CF pulmonary exacerbations using targeted proteomics. METHODS: 104 subjects provided plasma samples when clinically stable and were randomly split into discovery (n=70) and replication (n=34) cohorts. Multiple reaction monitoring mass spectrometry (MRM-MS) was used to measure 117 peptides (79 proteins) from plasma. Plasma proteins with differential abundance between subjects who did versus did not develop an imminent exacerbation were analysed and proteins with fold difference >1.5 between the groups were included in an MRM-MS classifier model to predict imminent exacerbations. Performance characteristics were compared with clinical predictors and candidate plasma protein biomarkers. RESULTS: Six proteins were included in the final MRM-MS protein panel. The area under the curve (AUC) for the prediction of imminent exacerbations was highest for the MRM-MS protein panel (AUC 0.74) in comparison to FEV1% predicted (AUC 0.55) and the top candidate plasma protein biomarkers, including C-reactive protein (AUC 0.61) and interleukin-6 (AUC 0.60). The MRM-MS protein panel performed similarly in the replication cohort (AUC 0.73). CONCLUSIONS: Using MRM-MS, a six-protein panel measured from plasma can distinguish individuals with versus without an imminent exacerbation. With further replication and assay development, this biomarker panel may be clinically applicable for prediction of exacerbations in individuals with CF.

The Effect of Different Case Definitions of Current Smoking on the Discovery of Smoking-Related Blood Gene Expression Signatures in Chronic Obstructive Pulmonary Disease.

INTRODUCTION: Smoking is the number one modifiable environmental risk factor for chronic obstructive pulmonary disease (COPD). Clinical, epidemiological and increasingly "omics" studies assess or adjust for current smoking status using only self-report, which may be inaccurate. Objective measures such as exhaled carbon monoxide (eCO) may also be problematic owing to limitations in the measurements and the relatively short half life of the molecule. In this study, we determined the impact of different case definitions of current cigarette smoking on gene expression in peripheral blood of patients with COPD. METHODS: Peripheral blood gene expression from 573 former- and current-smokers with COPD in the ECLIPSE study was used to find genes whose expression was associated with smoking status. Current smoking was defined using self-report, eCO concentrations, or both. Linear regression was used to determine the association of current smoking status with gene expression adjusting for age, sex and propensity score. Pathway enrichment analyses were performed on genes with P < .001. RESULT: Using self-report or eCO, only two genes were differentially expressed between current and ex-smokers, with no enrichment in biological processes. When current smoking was defined using both eCO and self-report, four genes were differentially expressed (LRRN3, PID1, FUCA1, GPR15) with enrichment in 40 biological pathways related to metabolic processes, response to hypoxia and hormonal stimulus. Additionally, the combined definition provided better distributions of test statistics for differential gene expression. CONCLUSION: A combined phenotype of eCO and self report allows for better discovery of genes and pathways related to current smoking. IMPLICATIONS: Studies relying only on self report of smoking status to assess or adjust for the impact of smoking may not fully capture its effect and will lead to residual confounding of results.

Biomarker Development for Chronic Obstructive Pulmonary Disease. From Discovery to Clinical Implementation.

Chronic obstructive pulmonary disease (COPD) is one of the major causes of morbidity and mortality in the world. Regrettably, there are no biomarkers to objectively diagnose COPD exacerbations, which are the major drivers of hospitalization and deaths from COPD. Moreover, there are no biomarkers to guide therapeutic choices or to risk stratify patients for imminent exacerbations and no objective biomarkers of disease activity or disease progression. Although there has been a tremendous investment in COPD biomarker discovery over the past 2 decades, clinical translation and implementation have not matched these efforts. In this article, we outline the challenges of biomarker development in COPD and provide an overview of a developmental pipeline that may be able to surmount these challenges and bring novel biomarker solutions to accelerate therapeutic discoveries and to improve the care and outcomes of the millions of individuals worldwide with COPD.

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.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation.

BACKGROUND: Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk. METHODS: We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility. RESULTS: Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability. CONCLUSIONS: We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.