Immune response evolution in peanut epicutaneous immunotherapy for peanut-allergic children.

BACKGROUND: Epicutaneous immunotherapy with investigational Viaskin™ Peanut 250 µg (DBV712) has demonstrated statistically superior desensitization versus placebo in peanut-allergic children in clinical trials. It is unclear whether serologic biomarkers predict response. METHODS: Serum-specific IgG4 and IgE (whole peanut and components) from subjects enrolled in the phase 3 Efficacy and Safety of Viaskin Peanut in Children With IgE-Mediated Peanut Allergy study were examined by exploratory univariate and multivariate analyses to determine trajectories and predictors of treatment response, based upon peanut protein eliciting dose (ED) at Month (M) 12 double-blind placebo-controlled food challenge. RESULTS: Among Viaskin Peanut-treated subjects, peanut sIgG4 significantly increased from baseline through M12 and peanut sIgE peaked at M3 and fell below baseline by M12, with sIgG4 and sIgE peanut components mirroring these trajectories. Placebo subjects had no significant changes. By univariate analysis, M12 peanut sIgG4/sIgE was higher in treatment responders (p < 0.001) and had highest area under the curve (AUC) for predicting ED ≥300 mg and ≥1000 mg (AUC 69.5% and 69.9%, respectively). M12 peanut sIgG4/sIgE >20.1 predicted M12 ED ≥300 mg (80% positive predictive value). The best performing component was Ara h 1 sIgE <15.7 kUA /L (AUC 66.5%). A multivariate model combining Ara h 1 and peanut sIgG4/sIgE had an AUC of 68.2% (ED ≥300 mg) and 67.8% (ED ≥1000 mg). CONCLUSIONS: Peanut sIgG4 rise most clearly differentiated Viaskin Peanut versus placebo subjects. sIgG4/sIgE ratios >20.1 and the combination of Ara h 1 and peanut sIgG4/sIgE had moderate ability to predict treatment response and could potentially be useful for clinical monitoring. Additional data are needed to confirm these relationships.

Novel insights into the assessment of risk of upper gastrointestinal bleeding in decompensated cirrhotic children.

OBJECTIVES: Cirrhotic children wait-listed for liver transplant are prone to bleeding from gastrointestinal varices. Grade 2-3 esophageal varices, red signs, and gastric varices are well-known risk factors. However, the involvement of hemostatic factors remains controversial because of the rebalanced state of coagulation during cirrhosis. METHODS: Children suffering from decompensated cirrhosis were prospectively included while being on waitlist. Portal hypertension was assessed by ultrasound and endoscopy. Coagulopathy was evaluated through conventional tests, thromboelastometry, and platelet function testing. The included children were followed up until liver transplantation, and all bleeding episodes were recorded. Children with or without bleeding were compared according to clinical, radiological, endoscopic, and biological parameters. In addition, validation of a predictive model for risk of variceal bleeding comprising of grade 2-3 esophageal varices, red spots, and fibrinogen level <150 mg/dL was applied on this cohort. RESULTS: Of 20 enrolled children, 6 had upper gastrointestinal bleeding. Significant differences were observed in fibrinogen level, adenosine diphosphate, and thrombin-dependent platelet aggregation. The model used to compute the upper gastrointestinal bleeding risk had an estimated predictive performance of 81.0%. Platelet aggregation analysis addition improved the estimated predictive performance up to 89.0%. CONCLUSIONS: We demonstrated an association between hemostatic factors and the upper gastrointestinal bleeding risk. A low fibrinogen level and platelet aggregation dysfunction may predict the risk of bleeding in children with decompensated cirrhosis. A predictive model is available to assess the upper gastrointestinal bleeding risk but needs further investigations. Clinicaltrials.gov number: NCT03244332.

Development of a multivariate prediction model for nocturia, based on urinary tract etiologies.

PURPOSE: The main objective of our study was to determine which combination of modifiable and non-modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post-hoc analysis of 3 prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between patients with and without nocturia. METHOD: This was a post hoc analysis of three prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between adults with and without nocturia. Study 1: adults with and without nocturia (n = 148); Study 2: patients ≥65 years with and without nocturnal LUTS (n = 54); Study 3: menopausal women before and after hormone replacement therapy (n = 43). All eligible patients (n = 183) completed a FVC during 24 hours (n = 13), 48 hours (n = 30) or 72 hours (n = 140). The combination of algorithms and number of determinants obtaining the best average area under the receiver operating curve (AUC-ROC) led to the final model. Differences between groups were assessed using the AUC-ROC and Mann- Whitney-Wilcoxon tests. Holm corrections were applied for multiple statistical testing. Also, the stability of the feature selection was evaluated. RESULTS: The best discrimination was obtained when 13 determinants were included. However, a logistic regression model based on seven determinants selected with random forest had comparable discrimination including an optimal signature stability. It was able to discriminate almost perfectly between nights with and without nocturia. CONCLUSION: Relevant information to accomplish the excellent predictability of the model is; functional bladder capacity, 24 hours urine output, nocturnal output, age, BMI. The multivariate model used in this analysis provides new insights into combination therapy as it allows simulating the effect of different available treatment modalities and its combinations.

Assessment of risk of bleeding from esophageal varices during management of biliary atresia in children.

OBJECTIVES: The management of esophageal varices (EV) in children experiencing biliary atresia (BA) remains controversial. Recent studies in children proposed initiating a prophylactic treatment in patients with severe (grade III) EV and/or EV associated with red color signs. Our study was aimed at assessing the risk of bleeding from EV in a series of patients with BA, identifying risk factors for bleeding to develop a predictive model of bleeding. METHODS: This was a retrospective study including 83 eligible patients with BA. Clinical, ultrasonographic, endoscopic, and laboratory parameters were studied from the beginning of medical management up to the occurrence of upper gastrointestinal bleeding. In patients not presenting any bleeding, data were analyzed until liver transplantation, endoscopic treatment of EV was performed, or last follow-up. Risk factors were investigated using univariate and multivariate statistical analyses. RESULTS: Seventeen of 83 patients (20%) presented gastrointestinal bleeding, with a median age of 9.5 months (6-50 months). In univariate and multivariate analyses, high-grade EV, red color signs on endoscopic examination, and low fibrinogen levels, at first endoscopy, were identified as risk factors for bleeding. When tested in >10,000 different models, these 3 variables appeared to play the most significant role in predicting bleeding. CONCLUSIONS: Our study confirmed that grade III EV and red color signs are risk factors for bleeding in patients followed up for BA. We identified low fibrinogen levels as an additional risk factor. The relevance of these 3 factors to predict bleeding from EV requires validation in a prospective study.

Robust biomarker identification for cancer diagnosis with ensemble feature selection methods.

MOTIVATION: Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of biomarkers is an important issue, as it may greatly influence subsequent biological validations. In addition, a more robust set of markers may strengthen the confidence of an expert in the results of a selection method. RESULTS: Our first contribution is a general framework for the analysis of the robustness of a biomarker selection algorithm. Secondly, we conducted a large-scale analysis of the recently introduced concept of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features. We focus on selection methods that are embedded in the estimation of support vector machines (SVMs). SVMs are powerful classification models that have shown state-of-the-art performance on several diagnosis and prognosis tasks on biological data. Their feature selection extensions also offered good results for gene selection tasks. We show that the robustness of SVMs for biomarker discovery can be substantially increased by using ensemble feature selection techniques, while at the same time improving upon classification performances. The proposed methodology is evaluated on four microarray datasets showing increases of up to almost 30% in robustness of the selected biomarkers, along with an improvement of approximately 15% in classification performance. The stability improvement with ensemble methods is particularly noticeable for small signature sizes (a few tens of genes), which is most relevant for the design of a diagnosis or prognosis model from a gene signature. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Retreatment with Hyaluronic Acid Viscosupplementation in Knee Osteoarthritis: Agreement between EUROVISCO Guidelines and Current Medical Practice.

OBJECTIVES: This work studied if and how current clinical practice agrees with European Viscosupplementation Consensus Group (EUROVISCO) recommendations and how this agreement might be different according to physician's specialization. In addition, this work aimed to identify key decision factors that practitioners consider in their decision to retreat or not a patient with hyaluronic acid viscosupplementation. METHODS: Practitioners have been invited by e-mail to participate in an online exercise on viscosupplementation retreatment. They received a fictional patient case at random among a set of predefined fictional cases. The platform asked the practitioner if he/she would retreat the patient with viscosupplementation or not. To take a decision, the practitioner could select questions among a list of predefined questions. Among them, some were related to criteria used in the EUROVISCO decision tree and others served as confounding factors. RESULTS: A total of 506 practitioners participated to the exercise, of which 399 gave their decision about the case assigned to them by the platform. The observed agreement between practitioner decisions and EUROVISCO recommendations was 58.89 ± 4.95% (95% confidence interval [CI]). Overall, the decision to retreat was taken in 47.87% of the cases, while the EUROVISCO guidelines follow-up would have led to 55.89% retreatment for the same cases (P = 0.03). CONCLUSIONS: In current practice, physicians tended to reinject their patients less than recommended, although EUROVISCO guidelines for viscosupplementation retreatment consider decision criteria that clearly correspond to those of practitioners in real life. These include the patients' willingness to be treated or the patients' perception of the effectiveness of the treatment.

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation.

Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine. Our review article presents an overview of the data science domain while discussing the reasons for its emergence. We also present several perspectives of its applications in clinical laboratories, along with potential ethical challenges related to AI and data science.

DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients. METHODS: A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation. RESULTS: Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis. CONCLUSIONS: Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

Heterogeneity of synovial molecular patterns in patients with arthritis.

OBJECTIVES: Early diagnosis of rheumatoid arthritis (RA) is an unmet medical need in the field of rheumatology. Previously, we performed high-density transcriptomic studies on synovial biopsies from patients with arthritis, and found that synovial gene expression profiles were significantly different according to the underlying disorder. Here, we wanted to further explore the consistency of the gene expression signals in synovial biopsies of patients with arthritis, using low-density platforms. METHODS: Low-density assays (cDNA microarray and microfluidics qPCR) were designed, based on the results of the high-density microarray data. Knee synovial biopsies were obtained from patients with RA, spondyloarthropathies (SA) or osteoarthritis (OA) (n = 39), and also from patients with initial undifferentiated arthritis (UA) (n = 49). RESULTS: According to high-density microarray data, several molecular pathways are differentially expressed in patients with RA, SA and OA: T and B cell activation, chromatin remodelling, RAS GTPase activation and extracellular matrix regulation. Strikingly, disease activity (DAS28-CRP) has a significant influence on gene expression patterns in RA samples. Using the low-density assays, samples from patients with OA are easily discriminated from RA and SA samples. However, overlapping molecular patterns are found, in particular between RA and SA biopsies. Therefore, prediction of the clinical diagnosis based on gene expression data results in a diagnostic accuracy of 56.8%, which is increased up to 98.6% by the addition of specific clinical symptoms in the prediction algorithm. Similar observations are made in initial UA samples, in which overlapping molecular patterns also impact the accuracy of the diagnostic algorithm. When clinical symptoms are added, the diagnostic accuracy is strongly improved. CONCLUSIONS: Gene expression signatures are overall different in patients with OA, RA and SA, but overlapping molecular signatures are found in patients with these conditions. Therefore, an accurate diagnosis in patients with UA requires a combination of gene expression and clinical data.

A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling.

BACKGROUND: Temporal and local fluctuations in O2 in tumors require adaptive mechanisms to support cancer cell survival and proliferation. The transcriptome associated with cycling hypoxia (CycHyp) could thus represent a prognostic biomarker of cancer progression. METHOD: We exposed 20 tumor cell lines to repeated periods of hypoxia/reoxygenation to determine a transcriptomic CycHyp signature and used clinical data sets from 2,150 breast cancer patients to estimate a prognostic Cox proportional hazard model to assess its prognostic performance. RESULTS: The CycHyp prognostic potential was validated in patients independently of the receptor status of the tumors. The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations including those with a node negative status under treatment (HR=3.16) or not (HR=5.54). The CycHyp prognostic signature outperformed a signature derived from continuous hypoxia and major prognostic metagenes (P<0.001). The CycHyp signature could also identify ER+HER2 node-negative breast cancer patients at high risk based on clinicopathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based but who presented a negative outcome. CONCLUSIONS: The CycHyp signature is prognostic of breast cancer and offers a unique decision making tool to complement anatomopathologic evaluation.