Evaluation of a solid matrix for collection and ambient storage of RNA from whole blood.

BACKGROUND: Whole blood gene expression-based molecular diagnostic tests are becoming increasingly available. Conventional tube-based methods for obtaining RNA from whole blood can be limited by phlebotomy, volume requirements, and RNA stability during transport and storage. A dried blood spot matrix for collecting high-quality RNA, called RNA Stabilizing Matrix (RSM), was evaluated against PAXgene® blood collection tubes. METHODS: Whole blood was collected from 25 individuals and subjected to 3 sample storage conditions: 18 hours at either room temperature (baseline arm) or 37°C, and 6 days at room temperature. RNA was extracted and assessed for integrity by Agilent Bioanalyzer, and gene expression was compared by RT-qPCR across 23 mRNAs comprising a clinical test for obstructive coronary artery disease. RESULTS: RSM produced RNA of relatively high integrity across the various tested conditions (mean RIN ± 95% CI: baseline arm, 6.92 ± 0.24; 37°C arm, 5.98 ± 0.48; 6-day arm, 6.72 ± 0.23). PAXgene samples showed comparable RNA integrity in both baseline and 37°C arms (8.42 ± 0.17; 7.92 ± 0.1 respectively) however significant degradation was observed in the 6-day arm (3.19 ± 1.32). Gene expression scores on RSM were highly correlated between the baseline and 37°C and 6-day study arms (median r = 0.96, 0.95 respectively), as was the correlation to PAXgene tubes (median r = 0.95, p < 0.001). CONCLUSION: RNA obtained from RSM shows little degradation and comparable RT-qPCR performance to PAXgene RNA for the 23 genes analyzed. Further development of this technology may provide a convenient method for collecting, shipping, and storing RNA for gene expression assays.

CXCL13 is a predictive biomarker in idiopathic multicentric Castleman disease.

Idiopathic multicentric Castleman disease (iMCD) is a rare and poorly-understood cytokine storm-driven inflammatory disorder. Interleukin-6 (IL-6) is a known disease driver in some patients, but anti-IL-6 therapy with siltuximab is not effective in all patients, and biomarkers indicating success at an early time point following treatment initiation are lacking. Here we show, by comparison of levels of 1,178 proteins in sera of healthy participants (N = 42), patients with iMCD (N = 88), and with related diseases (N = 60), a comprehensive landscape of candidate disease mediators and predictors of siltuximab response. C-X-C Motif Chemokine Ligand-13 (CXCL13) is identified and validated as the protein most prominently up-regulated in iMCD. Early and significant decrease in CXCL13 levels clearly distinguishes siltuximab responders from non-responders; a 17% reduction by day 8 following siltuximab therapy initiation is predictive of response at later time points. Our study thus suggests that CXCL13 is a predictive biomarker of response to siltuximab in iMCD.

Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients.

BACKGROUND: Diagnosing obstructive coronary artery disease (CAD) in at-risk patients can be challenging and typically requires both noninvasive imaging methods and coronary angiography, the gold standard. Previous studies have suggested that peripheral blood gene expression can indicate the presence of CAD. OBJECTIVE: To validate a previously developed 23-gene, expression-based classification test for diagnosis of obstructive CAD in nondiabetic patients. DESIGN: Multicenter prospective trial with blood samples obtained before coronary angiography. (ClinicalTrials.gov registration number: NCT00500617) SETTING: 39 centers in the United States. PATIENTS: An independent validation cohort of 526 nondiabetic patients with a clinical indication for coronary angiography. MEASUREMENTS: Receiver-operating characteristic (ROC) analysis of classifier score measured by real-time polymerase chain reaction, additivity to clinical factors, and reclassification of patient disease likelihood versus disease status defined by quantitative coronary angiography. Obstructive CAD was defined as 50% or greater stenosis in 1 or more major coronary arteries by quantitative coronary angiography. RESULTS: The area under the ROC curve (AUC) was 0.70 ± 0.02 (P < 0.001); the test added to clinical variables (Diamond-Forrester method) (AUC, 0.72 with the test vs. 0.66 without; P = 0.003) and added somewhat to an expanded clinical model (AUC, 0.745 with the test vs. 0.732 without; P = 0.089). The test improved net reclassification over both the Diamond-Forrester method and the expanded clinical model (P < 0.001). At a score threshold that corresponded to a 20% likelihood of obstructive CAD (14.75), the sensitivity and specificity were 85% and 43% (yielding a negative predictive value of 83% and a positive predictive value of 46%), with 33% of patient scores below this threshold. LIMITATION: Patients with chronic inflammatory disorders, elevated levels of leukocytes or cardiac protein markers, or diabetes were excluded. CONCLUSION: A noninvasive whole-blood test based on gene expression and demographic characteristics may be useful for assessing obstructive CAD in nondiabetic patients without known CAD. PRIMARY FUNDING SOURCE: CardioDx.

Discovery and validation of a novel subgroup and therapeutic target in idiopathic multicentric Castleman disease.

Idiopathic multicentric Castleman disease (iMCD) is a poorly understood hematologic disorder involving cytokine-induced polyclonal lymphoproliferation, systemic inflammation, and potentially fatal multiorgan failure. Although the etiology of iMCD is unknown, interleukin-6 (IL-6) is an established disease driver in approximately one-third of patients. Anti-IL-6 therapy, siltuximab, is the only US Food and Drug Administration-approved treatment. Few options exist for siltuximab nonresponders, and no validated tests are available to predict likelihood of response. We procured and analyzed the largest-to-date cohort of iMCD samples, which enabled classification of iMCD into disease categories, discovery of siltuximab response biomarkers, and identification of therapeutic targets for siltuximab nonresponders. Proteomic quantification of 1178 analytes was performed on serum of 88 iMCD patients, 60 patients with clinico-pathologically overlapping diseases (human herpesvirus-8-associated MCD, N = 20; Hodgkin lymphoma, N = 20; rheumatoid arthritis, N = 20), and 42 healthy controls. Unsupervised clustering revealed iMCD patients have heterogeneous serum proteomes that did not cluster with clinico-pathologically overlapping diseases. Clustering of iMCD patients identified a novel subgroup with superior response to siltuximab, which was validated using a 7-analyte panel (apolipoprotein E, amphiregulin, serum amyloid P-component, inactivated complement C3b, immunoglobulin E, IL-6, erythropoietin) in an independent cohort. Enrichment analyses and immunohistochemistry identified Janus kinase (JAK)/signal transducer and activator of transcription 3 signaling as a candidate therapeutic target that could potentially be targeted with JAK inhibitors in siltuximab nonresponders. Our discoveries demonstrate the potential for accelerating discoveries for rare diseases through multistakeholder collaboration.

Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study.

BACKGROUND: Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population. OBJECTIVE: To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients. METHODS: Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application. RESULTS: Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and increased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (p < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (p < 0.03). CONCLUSION: These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.

Mitral annular size predicts Alfieri stitch tension in mitral edge-to-edge repair.

BACKGROUND AND AIM OF THE STUDY: Whilst increased 'Alfieri stitch' tension may reduce the durability of 'edge-to-edge' mitral repair, the factors affecting suture tension are unknown. In order to study hemodynamics and left ventricular (LV) and annular dynamics that determine suture tension, the central edge of the mitral leaflets was approximated with a miniature force transducer to measure leaflet tension (T) at the leaflet approximation point. METHODS: Eight sheep were studied under open-chest conditions immediately after surgical placement of a force transducer and implantation of radiopaque markers on the left ventricle and mitral annulus (MA). Hemodynamic variables were altered by two caval occlusion steps (deltaV1 and deltaV2) and dobutamine infusion. Three-dimensional marker coordinates were obtained by simultaneous biplane videofluoroscopy to measure LV volume, MA area (MAA) and septal-lateral (SL) annular dimension throughout the cardiac cycle. RESULTS: At baseline, peak Alfieri stitch tension (0.30 +/- 0.18 N) was observed 96 +/- 61 ms prior to end-diastole coincident with peak annular SL diameter (98 +/- 58 ms before end-diastole). Dobutamine infusion decreased suture tension (from 0.30 +/- 0.18 N to 0.20 +/- 0.12 N, p = 0.01), although peak systolic pressure increased significantly (138 +/- 19 versus 115 +/- 14 mmHg; p = 0.03). A regression model was fitted with the goal of interpreting the hemodynamic and geometric predictors of tension as their influence varied with time: Tt (N) = 0.1916 + 0.2115 x SL (cm) - 0.1996 x MAA/SL (cm2/cm) + ft x LVP (mmHg), where Tt is tension at any time during the cardiac cycle and ft is the time-varying coefficient of LVP. CONCLUSION: Tension on the leaflets in the edge-to-edge repair is determined primarily by MA SL size, and paradoxically is lower when the contractile state is enhanced. This indicates that annular and/or LV dilatation increase stitch tension and may adversely affect durability of the repair if concomitant ring annuloplasty is not performed.

Biological and analytical stability of a peripheral blood gene expression score for obstructive coronary artery disease in the PREDICT and COMPASS studies.

A gene expression score (GES) for obstructive coronary artery disease (CAD) has been validated in two multicenter studies. Receiver-operating characteristics (ROC) analysis of the GES on an expanded Personalized Risk Evaluation and Diagnosis in the Coronary Tree (PREDICT) cohort (NCT no. 00500617) with CAD defined by quantitative coronary angiography (QCA) or clinical reads yielded similar performance (area under the curve (AUC)=0.70, N=1,502) to the original validation cohort (AUC=0.70, N=526). Analysis of 138 non-Caucasian and 1,364 Caucasian patients showed very similar performance (AUCs=0.72 vs. 0.70). To assess analytic stability, stored samples of the original validation cohort (N=526) was re-tested after 5 years, and the mean score changed from 20.3 to 19.8 after 5 years (N=501, 95 %). To assess patient scores over time, GES was determined on samples from 173 Coronary Obstruction Detection by Molecular Personalized Gene Expression (COMPASS) study (NCT no. 01117506) patients at approximately 1 year post-enrollment. Mean scores increased slightly from 15.9 to 17.3, corresponding to a 2.5 % increase in obstructive CAD likelihood. Changes in cardiovascular medications did not show a significant change in GES.

A whole blood gene expression-based signature for smoking status.

BACKGROUND: Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. METHODS: Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. RESULTS: Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). CONCLUSION: We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression.

Identification of factors contributing to variability in a blood-based gene expression test.

BACKGROUND: Corus CAD is a clinically validated test based on age, sex, and expression levels of 23 genes in whole blood that provides a score (1-40 points) proportional to the likelihood of obstructive coronary disease. Clinical laboratory process variability was examined using whole blood controls across a 24 month period: Intra-batch variability was assessed using sample replicates; inter-batch variability examined as a function of laboratory personnel, equipment, and reagent lots. METHODS/RESULTS: To assess intra-batch variability, five batches of 132 whole blood controls were processed; inter-batch variability was estimated using 895 whole blood control samples. ANOVA was used to examine inter-batch variability at 4 process steps: RNA extraction, cDNA synthesis, cDNA addition to assay plates, and qRT-PCR. Operator, machine, and reagent lots were assessed as variables for all stages if possible, for a total of 11 variables. Intra- and inter-batch variations were estimated to be 0.092 and 0.059 Cp units respectively (SD); total laboratory variation was estimated to be 0.11 Cp units (SD). In a regression model including all 11 laboratory variables, assay plate lot and cDNA kit lot contributed the most to variability (p = 0.045; 0.009 respectively). Overall, reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to inter-batch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively), leaving 19.6% of the variance unexplained. CONCLUSION: Intra-batch variability inherent to the PCR process contributed the most to the overall variability in the study while reagent lot showed the largest contribution to inter-batch variability.

Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.

BACKGROUND: Alterations in gene expression in peripheral blood cells have been shown to be sensitive to the presence and extent of coronary artery disease (CAD). A non-invasive blood test that could reliably assess obstructive CAD likelihood would have diagnostic utility. RESULTS: Microarray analysis of RNA samples from a 195 patient Duke CATHGEN registry case:control cohort yielded 2,438 genes with significant CAD association (p < 0.05), and identified the clinical/demographic factors with the largest effects on gene expression as age, sex, and diabetic status. RT-PCR analysis of 88 CAD classifier genes confirmed that diabetic status was the largest clinical factor affecting CAD associated gene expression changes. A second microarray cohort analysis limited to non-diabetics from the multi-center PREDICT study (198 patients; 99 case: control pairs matched for age and sex) evaluated gene expression, clinical, and cell population predictors of CAD and yielded 5,935 CAD genes (p < 0.05) with an intersection of 655 genes with the CATHGEN results. Biological pathway (gene ontology and literature) and statistical analyses (hierarchical clustering and logistic regression) were used in combination to select 113 genes for RT-PCR analysis including CAD classifiers, cell-type specific markers, and normalization genes.RT-PCR analysis of these 113 genes in a PREDICT cohort of 640 non-diabetic subject samples was used for algorithm development. Gene expression correlations identified clusters of CAD classifier genes which were reduced to meta-genes using LASSO. The final classifier for assessment of obstructive CAD was derived by Ridge Regression and contained sex-specific age functions and 6 meta-gene terms, comprising 23 genes. This algorithm showed a cross-validated estimated AUC = 0.77 (95% CI 0.73-0.81) in ROC analysis. CONCLUSIONS: We have developed a whole blood classifier based on gene expression, age and sex for the assessment of obstructive CAD in non-diabetic patients from a combination of microarray and RT-PCR data derived from studies of patients clinically indicated for invasive angiography. CLINICAL TRIAL REGISTRATION INFORMATION: PREDICT, Personalized Risk Evaluation and Diagnosis in the Coronary Tree, http://www.clinicaltrials.gov, NCT00500617.