Respiratory Research Institute

<bold>RATIONALE:</bold> Daily FeNO may reflect airway T2 inflammation better than singular clinic data. <bold>Objectives:</bold> To compare longitudinal mean FeNO and variability (CV) in relation to asthma control; to compare sensitivity to detect T2_FeNO inflammation at diagnostic (≥40 ppb in GINA 1) and on-treatment (≥25 ppb in GINA 2-5) cutoffs vs. single clinic values. <bold>Methods:</bold> Observational study with daily home-based FeNO (Vivatmo <italic>me</italic>) and symptom diary in asthmatics of different GINA steps over 3 months. Clinic FeNO, FEV₁, and ACQ were also collected at 2 visits (enrollment/study end). <bold>Results:</bold> We enrolled n=85 patients (n=23 Step 1, n=37 Step 2-3, and n=25 Step 4-5). Mean daily FeNO over 3 months was highest in uncontrolled Step 4-5 (p=0.031), and FeNO variability in Step 2-3 (p=0.045). Overall, subjects with (+) sustained control (ACQ<0.75 both visits) had comparable mean FeNO values, but fewer proportion of patients with variability above the median CV vs. (-)sustained control patients (39.1 vs. 54.1%; p=0.03). Mean FeNO was elevated on symptomatic asthma days, particularly in Step 1 (p=0.001), with similar trends during loss of control phases. Home FeNO increased sensitivity to detect T2_FeNO inflammation at diagnostic cutoff (Step 1) from 8.7% (clinic FeNO) to 47.8% of subjects. In GINA 2-3 and 4-5, sensitivity for on-treatment T2_FeNO inflammation by home vs. clinic FeNO increased from 58.3% to 83.3%, and 64% to 96%, respectively. <bold>Conclusions:</bold> Daily FeNO provides important information about airway inflammation, complimentary to symptom control. Further, detection of T2 inflammation in asthmatics is improved at all GINA steps at both diagnostic and predictive/prognostic cutoffs, with important implications for management and diagnosis.

<bold>AIM:</bold> The INTREPID trial demonstrated the clinical benefits of fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) versus non-ELLIPTA multiple-inhaler triple therapy (MITT) for the treatment of COPD in a usual clinical practice setting (Halpin, 2019 ERJ Open Res, 7[2]). This study assessed the cost-effectiveness of FF/UMEC/VI vs. non-ELLIPTA MITT for treating symptomatic COPD from a German healthcare perspective. <bold>Methods:</bold> Baseline characteristics, treatment effects (forced expiratory volume in 1 second and St. George’s Respiratory Questionnaire score [derived from exploratory COPD Assessment Test score mapping]) and discontinuation data from the INTREPID trial, along with German healthcare resource and drug costs (2023) were used to populate the GALAXY COPD disease progression model (Briggs, 2017 <italic>Med Decis Making</italic>,37[4]). <bold>Results:</bold> Over a life-time horizon, using deterministic analyses, FF/UMEC/VI provided an additional 0.174 life years (95% range: 0.065, 0.322) and 0.261 quality-adjusted life years (0.186, 0.346), with a cost saving of €2,850 per patient (−€3,517, −€2,220); patients on FF/UMEC/VI also had fewer exacerbations (−0.063). Hence, FF/UMEC/VI was the dominant treatment option (Table). <bold>Conclusions:</bold> FF/UMEC/VI may help to reduce the clinical and economic burden of COPD and may be considered by physicians as a preferred treatment option in Germany. <bold>Funding:</bold> GSK 206973 <fig><object-id>erj;64/suppl_68/PA2124/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>

<bold>Background:</bold> Inhalation errors are a common source of underdosing in asthma patients, leading to poor asthma control. Thus, regular inhalation training is recommended. The Kata^® app is a digital inhalation coach that provides step-by-step guidance on how to inhale correctly. <bold>Objective:</bold> Evaluate the effect of the Kata^® app on inhalation technique and clinical endpoints (e.g., FeNO; ACQ-5) in a clinical trial. <bold>Methods:</bold> This post hoc analysis of Kata^® app data collected during a phase II randomized controlled trial, included adult patients with moderate to severe, uncontrolled type-2 high asthma (N=78). Patients were followed for up to 12 weeks. A smartphone with the Kata^® app was provided at study start. Patients were asked to record each inhalation of their asthma maintenance treatment. Inhalation errors were scored according to predefined criteria. Rates for total and critical errors (significantly affect drug delivery) were calculated as the number of errors per inhalation. Analysis of clinical endpoints used data from the run-in phase only. <bold>Results:</bold> Univariate regression analysis showed a significant total error rate decrease of 1.4% per study day (p<.001; 95%CI: 0.8-1.9). Similarly, a significant decrease of the critical error rate of 2.0% per study day was found (p<.001; 95%CI: 1.0-2.6). There was a significant improvement of the ACQ-5 (p=.036; N=59). No significant improvement of FeNO was found (p=.170; N=60), but 26 of 60 patients (43%) showed a clinically relevant decrease (>20%) of FeNO. <bold>Conclusion:</bold> The Kata^® app improves inhalation technique in a clinical trial and is associated with improved asthma control. The Kata^® app is a useful tool for patients using inhalers to treat their asthma.

Triple therapy (long-acting muscarinic antagonist/long-acting β2-agonist/inhaled corticosteroid) is recommended for patients with chronic obstructive pulmonary disease (COPD) who experience recurrent exacerbations. Multiple-inhaler triple therapy (MITT) is associated with poor adherence and persistence. This study assessed comparative adherence and persistence to single-inhaler triple therapy (SITT) versus MITT among patients with COPD in a real-world setting in Germany. This retrospective analysis using the WIG2 benchmark database identified patients with COPD newly initiating triple therapy with MITT or SITT (fluticasone furoate/umeclidinium/vilanterol [FF/UMEC/VI] or formoterol/beclomethasone/glycopyrronium bromide [FOR/BDP/GLY]) November 2017-June 2019. Eligible patients were ≥35 years with 1 year's continual insurance prior to triple therapy initiation and no previous record of triple therapy. Inverse probability of treatment weighting was used to balance baseline characteristics. Adherence was measured using proportion of days covered (PDC) at 6, 12, and 18 months post-treatment initiation; persistence (time until treatment discontinuation) was measured at 6, 12, and 18 months, with a gap of >30 days used to define non-persistence. Of 5710 patients included in the analysis (mean age 66 years), 71.4% initiated MITT and 28.6% initiated SITT (FF/UMEC/VI: 41.4%; FOR/BDP/GLY: 58.6%). Mean PDC was higher among SITT versus MITT users at all time points; at each time point, mean PDC was highest among FF/UMEC/VI users. During the first 6 months following treatment initiation, higher adherence was exhibited by FF/UMEC/VI (29%) and FOR/BDP/GLY (19%) users versus MITT users. Over the entire observation period, FF/UMEC/VI users had the highest proportion of persistent patients; at 18 months, 16.5% of FF/UMEC/VI users were persistent versus 2.3% of MITT users. Patients initiating SITT in Germany had significantly higher adherence and persistence compared with patients initiating MITT over 6 to 18 months following treatment initiation. Among SITT, FF/UMEC/VI users had the highest proportion of adherence and persistence.

Simple SummaryFor the effective treatment of lung cancer patients, correct tumor subtyping is of utmost importance, but it is often challenging in clinical routine. Using artificial intelligence and combining information from digital microscopy and matrix-assisted laser desorption/ionization mass spectrometry imaging data have the potential to support the pathologist’s decision-making process. We present a classification algorithm to distinguish between adenocarcinoma and squamous cell carcinoma of the lung based on the automatic detection of tumor areas in whole tissue sections and the determination of the tumor subtype with high accuracy.Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Distant metastasis is the main cause of mortality in breast cancer patients. Using the breast cancer genomic data from The Cancer Genome Atlas (TCGA), we identified brain specific Cav2.2 as a critical regulator of metastasis. Cav2.2 expression is significantly upregulated in breast cancer and its higher expression is inversely correlated with survival suggesting a previously unappreciated role of Cav2.2 in breast cancer. Cav2.2 is required for breast cancer migration, invasion, and metastasis. Interestingly, Cav2.2 promotes invadopodia formation and extracellular matrix (ECM) degradation through the stabilization of invadopodia component cortactin in a proteosome-dependent manner. Moreover, deubiquitinating enzyme USP43 mediated the functions of Cav2.2 in cortactin stabilization, invadopodia formation, ECM degradation, and metastasis. Interestingly, Cav2.2 upregulates USP43 expression through NFAT2 dephosphorylation and nuclear localization. Our study uncovered a novel pathway that regulates cortactin expression and invadopodia formation in breast cancer metastasis.