Evaluation of Technology-Enabled Monitoring of Patient-Reported Outcomes to Detect and Treat Toxic Effects Linked to Immune Checkpoint Inhibitors.

Importance: Immune checkpoint inhibitors can produce distinct toxic effects that require prompt recognition and timely management. Objective: To develop a technology-enabled, dynamically adaptive protocol that can provide the accurate information needed to inform specific remedies for immune toxic effects in patients treated with immune checkpoint inhibitors. Design, Setting, and Participants: An open-label cohort study was conducted at a single tertiary referral center from September 6, 2019, to September 3, 2020. The median follow-up duration was 63 (interquartile range, 35.5-122) days. Fifty patients with genitourinary cancers treated with immune checkpoint inhibitors were enrolled. Interventions: A fit-for-purpose electronic platform was developed to enable active patient and care team participation. A smartphone application downloaded onto patients' personal mobile devices prompted them to report their symptoms at least 3 times per week. The set of symptoms and associated queries were paired with alert thresholds for symptoms requiring clinical action. Main Outcomes and Measures: The primary end point of this interim analysis was feasibility, as measured by patient and care team adherence, and lack of increase in care team staffing. Operating characteristics were estimated for each symptom alert and used to dynamically adapt the alert thresholds to ensure sensitivity while reducing unnecessary alerts. Results: Of the 50 patients enrolled, 47 had at least 1 follow-up visit and were included in the analysis. Median age was 65 years (range, 37-86), 39 patients (83%) were men, and 39 patients (83%) had metastatic cancer, with the most common being urothelial cell carcinoma and renal cell carcinoma (22 [47%] patients each). After initial onboarding, no further care team training or additional care team staffing was required. Patients had a median study adherence rate of 74% (interquartile range, 60%-86%) and 73% of automated alerts were reviewed within 3 days by the clinic team. Symptoms with the highest positive predictive value for adverse events requiring acute intervention included dizziness (21%), nausea/vomiting (26%), and shortness of breath (14%). The symptoms most likely to result in unnecessary alerts were arthralgia and myalgia, fatigue, and cough. Conclusions and Relevance: The findings of this cohort study suggest an acceptable and fiscally sound method can be developed to create a dynamic learning system to detect and manage immune-related toxic effects.

A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer.

BACKGROUND: Bronchoscopy is frequently nondiagnostic in patients with pulmonary lesions suspected to be lung cancer. This often results in additional invasive testing, although many lesions are benign. We sought to validate a bronchial-airway gene-expression classifier that could improve the diagnostic performance of bronchoscopy. METHODS: Current or former smokers undergoing bronchoscopy for suspected lung cancer were enrolled at 28 centers in two multicenter prospective studies (AEGIS-1 and AEGIS-2). A gene-expression classifier was measured in epithelial cells collected from the normal-appearing mainstem bronchus to assess the probability of lung cancer. RESULTS: A total of 639 patients in AEGIS-1 (298 patients) and AEGIS-2 (341 patients) met the criteria for inclusion. A total of 43% of bronchoscopic examinations were nondiagnostic for lung cancer, and invasive procedures were performed after bronchoscopy in 35% of patients with benign lesions. In AEGIS-1, the classifier had an area under the receiver-operating-characteristic curve (AUC) of 0.78 (95% confidence interval [CI], 0.73 to 0.83), a sensitivity of 88% (95% CI, 83 to 92), and a specificity of 47% (95% CI, 37 to 58). In AEGIS-2, the classifier had an AUC of 0.74 (95% CI, 0.68 to 0.80), a sensitivity of 89% (95% CI, 84 to 92), and a specificity of 47% (95% CI, 36 to 59). The combination of the classifier plus bronchoscopy had a sensitivity of 96% (95% CI, 93 to 98) in AEGIS-1 and 98% (95% CI, 96 to 99) in AEGIS-2, independent of lesion size and location. In 101 patients with an intermediate pretest probability of cancer, the negative predictive value of the classifier was 91% (95% CI, 75 to 98) among patients with a nondiagnostic bronchoscopic examination. CONCLUSIONS: The gene-expression classifier improved the diagnostic performance of bronchoscopy for the detection of lung cancer. In intermediate-risk patients with a nondiagnostic bronchoscopic examination, a negative classifier score provides support for a more conservative diagnostic approach. (Funded by Allegro Diagnostics and others; AEGIS-1 and AEGIS-2 ClinicalTrials.gov numbers, NCT01309087 and NCT00746759.).

Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy.

BACKGROUND: The gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort. METHODS: We collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study. RESULTS: We found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy. CONCLUSION: We developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.

Mcl-1 dependence predicts response to vorinostat and gemtuzumab ozogamicin in acute myeloid leukemia.

Older adults with acute myeloid leukemia (AML) are commonly considered for investigational therapies, which often only benefit subsets of patients. In this study, we assessed whether BH3 profiling of apoptotic functionality could predict outcomes following treatment with vorinostat (histone deacetylase inhibitor) and gemtuzumab ozogamicin (GO; CD33-targeted immunoconjugate). Flow cytometry of BH3 peptide priming with Noxa (anti-apoptotic protein Mcl-1 modulator) correlated with remission induction (p=.026; AUC=0.83 [CI: 0.65-1.00; p=.00042]: AUC=0.88 [CI:0.75-1.00] with age adjustment) and overall survival (p=.027 logistic regression; AUC=0.87 [0.64-1.00; p=.0017]). This Mcl-1-dependence suggests a pivotal role of Bcl-2 family protein-mediated apoptosis to vorinostat/GO in AML patients.

A peripheral blood gene expression score is associated with atherosclerotic Plaque Burden and Stenosis by cardiovascular CT-angiography: results from the PREDICT and COMPASS studies.

OBJECTIVE: We previously validated a gene expression score (GES) based on age, sex and peripheral blood cell expression levels of 23 genes measured by quantitative real-time PCR (qRT-PCR) for diagnosis of obstructive coronary artery disease (CAD) (≥ 50% luminal diameter stenosis). In this study we sought to determine the association between the GES and coronary arterial Plaque Burden and Stenosis by CT-angiography. METHODS: A total of 610 patients (mean age: 57 ± 11; 50% male) from the PREDICT and COMPASS studies from 59 centers were analyzed. Coronary artery calcium (CAC) scoring, CT angiography (CTA)-based plaque and stenosis and GES measurements were performed. CAC was expressed as Agatston score and CTA evaluated for stenosis severity: 0. None; 1. Minimal, 2. Mild, 3. Moderate, 4. Severe and 5. Occluded. Correlation analysis, one-way analysis of variance (ANOVA) and receiver operating characteristics (ROC) analyses were performed. RESULTS: GES was significantly associated with plaque burden by CAC (r = 0.50; p < 0.001) and CTA (segment involvement score index: r = 0.37, p < 0.001); a low score (≤ 15) had sensitivity of 0.71 and a high score (≥ 28) a specificity of 0.97 for the prediction of zero vs. non-zero CAC. Increasing GES was associated with a greater degree of categorical stenosis by ANOVA (p < 0.001); GES significantly correlated with maximum luminal stenosis (r = 0.41; p < 0.01) and segment stenosis score index (r = 0.38; p < 0.01). A low score had sensitivity of 0.90 and a high score a specificity of 0.87 for ≥ 70% stenosis. CONCLUSIONS: A previously validated GES is significantly associated with Plaque Burden and Stenosis by CT. CLINICAL TRIAL REGISTRATION: (PREDICT [NCT00500617] and COMPASS [NCT01117506]), www.clinicaltrials.gov.

Clinical implications of referral bias in the diagnostic performance of exercise testing for coronary artery disease.

BACKGROUND: Exercise testing with echocardiography or myocardial perfusion imaging is widely used to risk-stratify patients with suspected coronary artery disease. However, reports of diagnostic performance rarely adjust for referral bias, and this practice may adversely influence patient care. Therefore, we evaluated the potential impact of referral bias on diagnostic effectiveness and clinical decision-making. METHODS AND RESULTS: Searching PubMed and EMBASE (1990-2012), 2 investigators independently evaluated eligibility and abstracted data on study characteristics and referral patterns. Diagnostic performance reported in 4 previously published meta-analyses of exercise echocardiography and myocardial perfusion imaging was adjusted using pooled referral rates and Bayesian methods. Twenty-one studies reported referral patterns in 49 006 patients (mean age 60.7 years, 39.6% women, and 0.8% prior history of myocardial infarction). Catheterization referral rates after normal and abnormal exercise tests were 4.0% (95% CI, 2.9% to 5.0%) and 42.5% (36.2% to 48.9%), respectively, with odds ratio for referral after an abnormal test of 14.6 (10.7 to 19.9). After adjustment for referral, exercise echocardiography sensitivity fell from 84% (80% to 89%) to 34% (27% to 41%), and specificity rose from 77% (69% to 86%) to 99% (99% to 100%). Similarly, exercise myocardial perfusion imaging sensitivity fell from 85% (81% to 88%) to 38% (31% to 44%), and specificity rose from 69% (61% to 78%) to 99% (99% to 100%). Summary receiver operating curve analysis demonstrated only modest changes in overall discriminatory power but adjusting for referral increased positive-predictive value and reduced negative-predictive value. CONCLUSIONS: Exercise echocardiography and myocardial perfusion imaging are considerably less sensitive and more specific for coronary artery disease after adjustment for referral. Given these findings, future work should assess the comparative ability of these and other tests to rule-in versus rule-out coronary artery disease.

Apoprotein B, small-dense LDL and impaired HDL remodeling is associated with larger plaque burden and more noncalcified plaque as assessed by coronary CT angiography and intravascular ultrasound with radiofrequency backscatter: results from the ATLANTA I study.

BACKGROUND: Apoprotein B-containing lipoproteins are atherogenic, but atheroprotective functions of apoprotein A-containing high-density lipoprotein (HDL) particles are poorly understood. The association between lipoproteins and plaque components by coronary computed tomography angiography (CTA) and intravascular ultrasound with radiofrequency backscatter (IVUS/VH) has not been evaluated. METHODS AND RESULTS: Quantitative, 3-dimensional plaque measurements were performed in 60 patients with CTA and IVUS/VH. Apoproteins, lipids, and HDL subpopulations were measured with 2-dimensional (2D) gel electrophoresis, and correlation was assessed with univariate and multivariable models. ApoB particles were associated with a higher proportion of noncalcified plaque (NCP) and a lower proportion of calcified plaque (small, dense low-density lipoprotein cholesterol and high-density NCP: r=0.3, P=0.03; triglycerides and low-density NCP: r=0.34, P=0.01). Smaller, dense, lipid-poor HDL particles were associated with a shift from calcified plaque to NCP on CTA (α3-HDL% and low-density NCP: r=0.32, P=0.02) and with larger plaque volume on IVUS/VH (α4-HDL%: r=0.41, P=0.01; α3-HDL%: r=0.37, P=0.03), because of larger dense calcium (α4-HDL%: r=0.37, P=0.03), larger fibrous tissue (α4-HDL%: r=0.34, P=0.04), and larger necrotic core (α4-HDL%: r=0.46, P<0.01; α3-HDL%: r=0.37, P=0.03). Larger lipid-rich HDL particles were associated with less low-density NCP on CTA (α2-HDL%: r=-0.34, P=0.02; α1-HDL%: r=-0.28, P=0.05), with smaller plaque volume on IVUS/VH (pre-α2-HDL: r=-0.33, P=0.05; α1-HDL%: r=-0.41, P=0.01; pre-α2-HDL: r=-0.33, P=0.05) and with less necrotic core (α1-HDL: r=-0.42, P<0.01; pre-α2-HDL: r=-0.38, P=0.02; α2-HDL: r=-0.35, P=0.03; pre-α1-HDL: r=-0.34, P=0.04). Pre-ß2-HDL was associated with less calcification and less stenosis by both modalities. CONCLUSIONS: ApoB and small HDL particles are associated with larger plaque burden and more noncalcified plaque, whereas larger HDL and pre-ß2-HDL particles are associated with plaque burden and less noncalcified plaque by both CTA and IVUS/VH.

A peripheral blood gene expression score is associated with plaque volume and phenotype by intravascular ultrasound with radiofrequency backscatter analysis: results from the ATLANTA study.

BACKGROUND: A composite, peripheral gene expression score based on quantitative RNA-measurements has been validated for detecting stenosis against invasive coronary X-ray angiography. IVUS/VH has been validated for quantitative measurements of coronary plaque volume and composition and has been shown to be predictive of outcomes and treatment effects. The correlation between peripheral gene expression and coronary plaque composition by intravascular ultrasound with radiofrequency backscatter (IVUS/VH) is unknown. METHODS: Peripheral blood gene expression score (GES) was prospectively measured in 18 patients undergoing IVUS/VH. Plaque volume and composition [fibrous tissue (FI), fibro-fatty tissue (FF), necrotic core (NC) and dense calcium (DC)] were quantified in 3 dimensions in all plaques within the entire pullback. The relationship to GES was assessed by Spearman rank correlation. RESULTS: Mean age was 61.1±8.6 years; 67% were male. 1,158 mm of coronary anatomy was imaged by IVUS/VH. Using a validated scale of 1-40, mean GES was 21.6±9.4. GES was associated with plaque volume (R(2)=0.55; P=0.018), NC volume (R(2)=0.56; P=0.015), DC volume (R(2)=0.60; P=0.007), and non-calcified plaque volume (R(2)=0.50; P=0.036) by Spearman rank correlation. CONCLUSIONS: In this preliminary report, increased GES was associated with higher plaque volume and a more vulnerable plaque phenotype as evidenced by NC and DC. This composite GES is not only associated with obstructive coronary disease, but also with higher plaque volume and vulnerable phenotype.

BH3 profiling discriminates response to cytarabine-based treatment of acute myelogenous leukemia.

As acute myelogenous leukemia (AML) patient response to cytarabine-based standard-of-care treatment is variable, stratification into subgroups by biomarker-predicted response may lead to improved clinical outcomes. Here, we assess cell mitochondrial depolarization to proapoptotic signaling BH3-only peptides as a surrogate for the function of Bcl-2 family proteins to address clinical response to cytarabine-based therapy in patients with AML (N = 62). Peripheral blood mononuclear cell (PBMC) or bone marrow aspirate specimens were obtained from newly diagnosed patients with AML, viably preserved, and assayed by flow cytometry following BH3 profile assay with individual BH3 peptides. Mann-Whitney analysis indicates biomarker correlation with response to induction therapy: Notably, BIM priming was highly significant (P = 2 × 10(-6)) with a compelling sensitivity/specificity profile [area under curve (AUC) = 0.83; 95% confidence interval (CI), 0.73-0.94; P = 2 × 10(-10)]. Multivariate analysis indicates improved profiles for BIM readout + patient age (AUC = 0.89; 95% CI, 0.81-0.97) and BIM + patient age + cytogenetic status (AUC = 0.91; 95% CI, 0.83-0.98). When patients were stratified by cytogenetic status, BIM readout was significant for both intermediate (P = 0.0017; AUC = 0.88; 95% CI, 0.71-1.04) and unfavorable (P = 0.023; AUC = 0.79; 95% CI, 0.58-1.00) risk groups, demonstrating predictive power independent of cytogenetics. Additional analyses of secondary clinical endpoints displayed correlation between overall survival (P = 0.037) and event-free survival (P = 0.044) when patients were stratified into tertiles by BIM peptide response. Taken together, these results highlight the potential utility of BH3 profiling in personalized diagnostics of AML by offering actionable information for patient management decisions.

A critical analysis of the clinical use of incretin-based therapies: Are the GLP-1 therapies safe?

There is no question that incretin-based glucose-lowering medications have proven to be effective glucose-lowering agents. Glucagon-like peptide 1 (GLP-1) receptor agonists demonstrate an efficacy comparable to insulin treatment and appear to do so with significant effects to promote weight loss with minimal hypoglycemia. In addition, there are significant data with dipeptidyl peptidase 4 (DPP-4) inhibitors showing efficacy comparable to sulfonylureas but with weight neutral effects and reduced risk for hypoglycemia. However, over the recent past there have been concerns reported regarding the long-term consequences of using such therapies, and the issues raised are in regard to the potential of both classes to promote acute pancreatitis, to initiate histological changes suggesting chronic pancreatitis including associated preneoplastic lesions, and potentially, in the long run, pancreatic cancer. Other issues relate to a potential risk for the increase in thyroid cancer. There are clearly conflicting data that have been presented in preclinical studies and in epidemiologic studies. To provide an understanding of both sides of the argument, we provide a discussion of this topic as part of this two-part point-counterpoint narrative. In the point narrative below, Dr. Butler and colleagues provide their opinion and review of the data to date and that we need to reconsider the use of incretin-based therapies because of the growing concern of potential risk and based on a clearer understanding of the mechanism of action. In the counterpoint narrative following the contribution by Dr. Butler and colleagues, Dr. Nauck provides a defense of incretin-based therapies and that the benefits clearly outweigh any concern of risk.

A blood-based gene expression test for obstructive coronary artery disease tested in symptomatic nondiabetic patients referred for myocardial perfusion imaging the COMPASS study.

BACKGROUND- Obstructive coronary artery disease diagnosis in symptomatic patients often involves noninvasive testing before invasive coronary angiography. A blood-based gene expression score (GES) was previously validated in nondiabetic patients referred for invasive coronary angiography but not in symptomatic patients referred for myocardial perfusion imaging (MPI). METHODS AND RESULTS- This prospective, multicenter study obtained peripheral blood samples for GES before MPI in 537 consecutive patients. Patients with abnormal MPI usually underwent invasive coronary angiography; all others had research coronary computed tomographic angiography, with core laboratories defining coronary anatomy. A total of 431 patients completed GES, coronary imaging (invasive coronary angiography or computed tomographic angiography), and MPI. Mean age was 56±10 years (48% women). The prespecified primary end point was GES receiver-operating characteristics analysis to discriminate ≥50% stenosis (15% prevalence by core laboratory analysis). Area under the receiver-operating characteristics curve for GES was 0.79 (95% confidence interval, 0.73-0.84; P<0.001), with sensitivity, specificity, and negative predictive value of 89%, 52%, and 96%, respectively, at a prespecified threshold of ≤15 with 46% of patients below this score. The GES outperformed clinical factors by receiver-operating characteristics and reclassification analysis and showed significant correlation with maximum percent stenosis. Six-month follow-up on 97% of patients showed that 27 of 28 patients with adverse cardiovascular events or revascularization had GES >15. Site and core-laboratory MPI had areas under the curve of 0.59 and 0.63, respectively, significantly less than GES. CONCLUSIONS- GES has high sensitivity and negative predictive value for obstructive coronary artery disease. In this population clinically referred for MPI, the GES outperformed clinical factors and MPI. Clinical Trial Registration- URL: http://www.clinicaltrials.gov. Unique identifier: NCT01117506.

Markers of inflammation and bone remodelling associated with improvement in clinical response measures in psoriatic arthritis patients treated with golimumab.

OBJECTIVE: To determine serum biomarker associations with clinical response to golimumab treatment in patients with psoriatic arthritis (PsA). METHODS: GO-REVEAL was a randomised, placebo-controlled study of golimumab in patients with active PsA. Samples were collected from 100 patients at baseline, week 4 and week 14, and analysed for serum-based biomarkers and protein profiling (total 92 markers); data were correlated with clinical measures at week 14. RESULTS: Serum levels of a subset of proteins (apolipoprotein C III, ENRAGE, IL-16, myeloperoxidase, vascular endothelial growth factor, pyridinoline, matrix metalloproteinase 3, C-reactive protein (CRP), carcinoembryonic antigen, intercellular adhesion molecule 1 and macrophage inflammatory protein 1α) at baseline or week 4 were strongly associated with American College of Rheumatology 20% improvement (ACR20) response and/or disease activity score in 28 joints (DAS28) at week 14. A smaller subset of proteins was significantly associated with a 75% improvement in the psoriasis area and severity index score (PASI75) at week 14, (adiponectin, apolipoprotein CIII, serum glutamic oxaloacetic transaminase, and tumour necrosis factor α). Subsets of proteins were identified as potentially predictive of clinical response for each of the clinical measures, and the power of these biomarker panels to predict clinical response to golimumab treatment was stronger than for CRP alone. CONCLUSIONS: This analysis provides insight into several panels of markers that may have utility in identifying PsA patients likely to have ACR20, DAS28, or PASI75 responses following golimumab treatment.

Personalized reproductive medicine on the brink: progress, opportunities and challenges ahead.

Significant progress has been made in several fields of medicine towards personalizing treatment recommendations based on individual patient genotype. As the number of clinical and genetic biomarkers available to physicians has increased, predictive models able to integrate the contributions of multiple variables simultaneously have become valuable tools for medical decision making. Leveraging genotype information and multivariate predictive models holds the promise of bringing greater efficiency to, and reducing the costs of, fertility treatments. This work reviews the advances that have been made in genetic biomarker discovery and predictive modelling for fertility treatment outcomes. We also discuss some of the limitations of these studies for translation to clinical diagnostics and the challenges that remain.Personalized medicine holds the promise of allowing doctors to create 'bespoke' treatment recommendations for each patient based on multiple clinical variables such as age and hormone concentrations combined with the patient's genetic sequence information. A number of challenges remain for the field of reproductive medicine to make the research discoveries necessary to usher in this new era of personalized fertility care. Here, we discuss some of these challenges and make recommendations for overcoming them.

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.

A gender-specific blood-based gene expression score for assessing obstructive coronary artery disease in nondiabetic patients: results of the Personalized Risk Evaluation and Diagnosis in the Coronary Tree (PREDICT) trial.

BACKGROUND: Currently available noninvasive tests to risk stratify patients for obstructive coronary disease result in many unnecessary cardiac catheterizations, especially in women. We sought to compare the diagnostic accuracy of presenting symptoms, noninvasive test results, and a gene expression score (GES) in identifying obstructive coronary artery disease (CAD) according to gender, using quantitative coronary angiography as the criterion standard. METHODS: The PREDICT trial is a prospective multicenter observational study designed to develop and validate gene expression algorithms to assess obstructive CAD, defined as at least one ≥50% diameter stenosis measured by quantitative coronary angiography. Patients referred for diagnostic cardiac catheterization with suspected but previously unknown CAD were enrolled. Noninvasive myocardial perfusion imaging (MPI) was available in 60% of patients. The GES, comprising gender-specific age functions and 6 gene expression terms containing 23 genes, was performed for all patients. RESULTS: A total of 1,160 consecutive patients (57.6% men and 42.4% women) were enrolled in PREDICT. The prevalence of obstructive CAD was 46.7% in men and 22.0% in women. Chest pain symptoms were a discriminator of obstructive CAD in men (P < .001) but not in women. The positive predictive value of MPI was significantly higher in men (45%) than in women (22%). An abnormal site-read MPI was not significantly associated with obstructive or severity of CAD. The GES was significantly associated with a 2-fold increase in the odds of obstructive CAD for every 10-point increment in the GES and had a significant association with all measures of severity and burden of CAD. By multivariable analysis, GES was an independent predictor of obstructive CAD in the overall population (odds ratio [OR] 2.53, P = .001) and in the male (OR 1.99, P = .001) and female (OR 3.45, P = .001) subgroups separately, whereas MPI was not. CONCLUSIONS: Commonly used diagnostic approaches including symptom evaluation and MPI performed less well in women than in men for identifying significant CAD. In contrast, gender-specific GES performed similarly in women and men. Gene expression score offers a reliable diagnostic approach for the assessment of nondiabetic patients and, in particular, women with suspected obstructive CAD.

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.

Whole blood gene expression testing for coronary artery disease in nondiabetic patients: major adverse cardiovascular events and interventions in the PREDICT trial.

The majority of first-time angiography patients are without obstructive coronary artery disease (CAD). A blood gene expression score (GES) for obstructive CAD likelihood was validated in the PREDICT study, but its relation to major adverse cardiovascular events (MACE) and revascularization was not assessed. Patients (N = 1,160) were followed up for MACE and revascularization 1 year post-index angiography and GES, with 1,116 completing follow-up. The 30-day event rate was 23% and a further 2.2% at 12 months. The GES was associated with MACE/revascularizations (p < 0.001) and added to clinical risk scores. Patients with GES >15 trended towards increased >30 days MACE/revascularization likelihood (odds ratio = 2.59, 95% confidence interval = 0.89-9.14, p = 0.082). MACE incidence overall was 1.5% (17 of 1,116) and 3 of 17 patients had GES ≤ 15. For the total low GES group (N = 396), negative predictive value was 90% for MACE/revascularization and >99% for MACE alone, identifying a group of patients without obstructive CAD and highly unlikely to suffer MACE within 12 months.

Serum markers associated with clinical improvement in patients with ankylosing spondylitis treated with golimumab.

OBJECTIVE: Identify serum biomarkers modulated by golimumab treatment and associated with clinical response in patients with ankylosing spondylitis (AS). METHODS: Sera were collected at weeks 0, 4 and 14 from 100 patients with active AS in the GO-RAISE study. Patients were randomly assigned subcutaneous injections of placebo, golimumab 50 mg, or golimumab 100 mg every 4 weeks. Samples were tested for select inflammatory, bone and cartilage markers, and protein profiling was also performed. RESULTS: Golimumab treatment resulted in significant decreases in several serum proteins at weeks 4 and 14 compared with placebo. Patients who achieved clinical response at week 14, as assessed by a ≥20% improvement in the Assessment in SpondyloArthitis international Society response criteria (ASAS 20), demonstrated a distinct biomarker profile with lower levels of acute phase reactants and inflammatory biomarkers compared with patients who did not. Notably, combinations of two or three biomarkers assessed at baseline were predictive of various clinical outcomes (ASAS 20, Bath ankylosing spondylitis disease activity index 50 or Bath ankylosing spondylitis functional index) using a logistic regression analysis, and the overall predictive values for these combined biomarkers were greater than observed for C-reactive protein (CRP) alone. CONCLUSION: Golimumab modulated acute phase reactants and inflammatory markers in patients with active AS. Specific combinations of biomarkers at baseline demonstrated a stronger prediction for clinical efficacy than CRP alone. These data provide insights into the mechanism of golimumab on inflammatory processes driving AS pathology, and may have utility in managing the treatment of patients with AS.

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.

Pancreatitis, pancreatic, and thyroid cancer with glucagon-like peptide-1-based therapies.

BACKGROUND & AIMS: Glucagon-like peptide-1-based therapy is gaining widespread use for type 2 diabetes, although there are concerns about risks for pancreatitis and pancreatic and thyroid cancers. There are also concerns that dipeptidyl peptidase-4 inhibitors could cause cancer, given their effects on immune function. METHODS: We examined the US Food and Drug Administration's database of reported adverse events for those associated with the dipeptidyl peptidase-4 inhibitor sitagliptin and the glucagon-like peptide-1 mimetic exenatide, from 2004-2009; data on adverse events associated with 4 other medications were compared as controls. The primary outcomes measures were rates of reported pancreatitis, pancreatic and thyroid cancer, and all cancers associated with sitagliptin or exenatide, compared with other therapies. RESULTS: Use of sitagliptin or exenatide increased the odds ratio for reported pancreatitis 6-fold as compared with other therapies (P<2×10(-16)). Pancreatic cancer was more commonly reported among patients who took sitagliptin or exenatide as compared with other therapies (P<.008, P<9×10(-5)). All other cancers occurred similarly among patients who took sitagliptin compared with other therapies (P=.20). CONCLUSIONS: These data are consistent with case reports and animal studies indicating an increased risk for pancreatitis with glucagon-like peptide-1-based therapy. The findings also raise caution about the potential long-term actions of these drugs to promote pancreatic cancer.