Disentangling shared and unique variation in multiplatform hazelnut volatilomics using JIVE

In food science, volatile metabolites play a crucial role in determining sensory quality, acceptability and traceability. Fully characterizing the volatilome often requires combining multiple analytical techniques. However, reliably integrating the outcomes of these independent analyses to identify shared and unique information remains a significant challenge. In this paper, we illustrate how the multivariate Joint and Individual Variation Explained (JIVE) approach could be used to face this problem on a multiplatform VOC dataset obtained characterizing the volatilome of hazelnut pastes with GC-MS, PTR-ToF-MS and GC-IMS. While standardized data processing strategies were applied for GC-MS and PTR-ToF-MS, an automated pipeline was developed for GC-IMS to extract untargeted peak tables. The samples, representing three geographical origins, were collected during roasting to capture a wide range of intensities, offering a challenging case study for the proposed approach. The results showed that JIVE effectively separated the variability of each dataset into joint and individual components. A high-level comparison of the three analytical methods, based on variation decomposition and variable distribution, confirmed their complementarity. Additionally, identifying latent variables facilitated the visualization of analytical patterns - both shared and platform-specific - and the selection of related key variable trends, supporting the chemical interpretation of the results. This unsupervised data exploration strategy, based on JIVE, provides clearer interpretation of both shared and technique-specific insights. It supports an objective evaluation of the potential of a multiplatform analysis while offering guidance for selecting the most suitable analytical method in studies constrained to a single techniqu

Burden of Uncontrolled Severe Asthma With and Without Elevated Type-2 Inflammatory Biomarkers

Background: Many patients with asthma have type-2 airway inflammation, identified by the presence of biomarkers, including history of allergy, high blood eosinophil (EOS) count, and high fractional exhaled nitric oxide levels. Objective: To assess disease burden in relation to type-2 inflammatory biomarker status (history of allergy, blood EOS count, and fractional exhaled nitric oxide level) in patients with uncontrolled and controlled severe asthma in the NOVEL observational longiTudinal studY (NOVELTY) (NCT02760329). Methods: Asthma diagnosis and severity were physician-reported. Control was defined using Asthma Control Test score (uncontrolled <20, controlled ≥20) and/or 1 or more severe physician-reported exacerbation in the previous year. Biomarker distribution (history of allergy, blood EOS count, and fractional exhaled nitric oxide level), symptom burden (Asthma Control Test score, modified Medical Research Council dyspnea scale), health status (St George's Respiratory Questionnaire score), exacerbations, and health care resource utilization were assessed. Results: Of 647 patients with severe asthma, 446 had uncontrolled and 123 had controlled asthma. Among those with uncontrolled asthma, 196 (44%) had 2 or more positive biomarkers, 187 (42%) had 1 positive biomarker, 325 (73%) had low blood EOS, and 63 (14%) were triple-negative. Disease burden was similarly high across uncontrolled subgroups, irrespective of biomarker status, with poor symptom control (Asthma Control Test score 14.9-16.6), impaired health status (St George's Respiratory Questionnaire total score 46.7-49.4), clinically important breathlessness (modified Medical Research Council grade ≥2 in 47.3%-57.1%), and 1 or more severe exacerbation (70.6%-76.2%). Conclusions: Type-2 inflammatory biomarkers did not differentiate disease burden in patients with severe asthma. Patients with low type-2 inflammatory biomarker levels have few biologic therapy options; their needs should be addressed

A randomized open-label phase III trial evaluating the addition of denosumab to standard first-line treatment in advanced NSCLC : the European Thoracic Oncology Platform (ETOP) and European Organisation for Research and Treatment of Cancer (EORTC) SPLENDOUR trial

Introduction Receptor activator of NF-kB ligand stimulates NF-kB–dependent cell signaling and acts as the primary signal for bone resorption. Retrospective analysis of a large trial comparing denosumab versus zoledronic acid in bone metastatic solid tumors suggested significant overall survival (OS) advantage for patients with lung cancer with denosumab (p = 0.01). The randomized open-label phase III SPLENDOUR trial was designed to evaluate whether the addition of denosumab to standard first-line platinum-based doublet chemotherapy improved OS in advanced NSCLC. Methods Patients with stage IV NSCLC were randomized in a 1:1 ratio to either chemotherapy with or without denosumab (120 mg every 3–4 wks), stratified by the presence of bone metastases (at diagnosis), Eastern Cooperative Oncology Group performance status, histology, and region. To detect an OS increase from 9 to 11.25 months (hazard ratio [HR] = 0.80), 847 OS events were required. The trial closed prematurely owing to decreasing accrual rate. Results A total of 514 patients were randomized, with 509 receiving one or more doses of the assigned treatment (chemotherapy: 252, chemotherapy-denosumab: 257). The median age was 66.1 years, 71% were men, and 59% were former smokers. Bone metastases were identified in 275 patients (53%). Median OS (95% confidence interval [CI]) was 8.7 (7.6–11.0) months in the control arm versus 8.2 (7.5–10.4) months in the chemotherapy-denosumab arm (HR = 0.96; 95% CI: 0.78–1.19; one-sided p = 0.36). For patients with bone metastasis, HR was 1.02 (95% CI: 0.77–1.35), whereas for those without, HR was 0.90 (95% CI: 0.66–1.23). Adverse events grade 3 or greater were observed in 40.9%, 5.2%, 8.7% versus 45.5%, 10.9%, 10.5% of patients. Conditional power for OS benefit was less than or equal to 10%. Conclusions Denosumab was well-tolerated without unexpected safety concerns. There was no OS improvement for denosumab when added to chemotherapy in the intention-to-treat population and the subgroups with and without bone metastases. Our data do not provide evidence of a clinical benefit for denosumab in patients with NSCLC without bone metastases

Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum

Background: Asthma and chronic obstructive pulmonary disease (COPD) are complex diseases, the definitions of which overlap. Objective: To investigate clustering of clinical/physiological features and readily available biomarkers in patients with physician-assigned diagnoses of asthma and/or COPD in the NOVEL observational longiTudinal studY (NOVELTY; NCT02760329). Methods: Two approaches were taken to variable selection using baseline data: approach A was data-driven, hypothesis-free and used the Pearson dissimilarity matrix; approach B used an unsupervised Random Forest guided by clinical input. Cluster analyses were conducted across 100 random resamples using partitioning around medoids, followed by consensus clustering. Results: Approach A included 3796 individuals (mean age, 59.5 years; 54% female); approach B included 2934 patients (mean age, 60.7 years; 53% female). Each identified 6 mathematically stable clusters, which had overlapping characteristics. Overall, 67% to 75% of patients with asthma were in 3 clusters, and approximately 90% of patients with COPD were in 3 clusters. Although traditional features such as allergies and current/ex-smoking (respectively) were higher in these clusters, there were differences between clusters and approaches in features such as sex, ethnicity, breathlessness, frequent productive cough, and blood cell counts. The strongest predictors of the approach A cluster membership were age, weight, childhood onset, prebronchodilator FEV1, duration of dust/fume exposure, and number of daily medications. Conclusions: Cluster analyses in patients from NOVELTY with asthma and/or COPD yielded identifiable clusters, with several discriminatory features that differed from conventional diagnostic characteristics. The overlap between clusters suggests that they do not reflect discrete underlying mechanisms and points to the need for identification of molecular endotypes and potential treatment targets across asthma and/or COPD