Citations

Importance Airway mucus plugs are common in patients with chronic obstructive pulmonary disease (COPD); however, the association of airway mucus plugging and mortality in patients with COPD is unknown. Objective To determine whether airway mucus plugs identified on chest computed tomography (CT) were associated with increased all-cause mortality. Design, Setting, and Participants Observational retrospective analysis of prospectively collected data of patients with a diagnosis of COPD in the Genetic Epidemiology of COPD cohort. Participants were non-Hispanic Black or White individuals, aged 45 to 80 years, who smoked at least 10 pack-years. Participants were enrolled at 21 centers across the US between November 2007 and April 2011 and were followed up through August 31, 2022. Exposures Mucus plugs that completely occluded airways on chest CT scans, identified in medium- to large-sized airways (ie, approximately 2- to 10-mm lumen diameter) and categorized as affecting 0, 1 to 2, or 3 or more lung segments. Main Outcomes and Measures The primary outcome was all-cause mortality, assessed with proportional hazard regression analysis. Models were adjusted for age, sex, race and ethnicity, body mass index, pack-years smoked, current smoking status, forced expiratory volume in the first second of expiration, and CT measures of emphysema and airway disease. Results Among the 4483 participants with COPD, 4363 were included in the primary analysis (median age, 63 years [IQR, 57-70 years]; 44% were women). A total of 2585 (59.3%), 953 (21.8%), and 825 (18.9%) participants had mucus plugs in 0, 1 to 2, and 3 or more lung segments, respectively. During a median 9.5-year follow-up, 1769 participants (40.6%) died. The mortality rates were 34.0% (95% CI, 32.2%-35.8%), 46.7% (95% CI, 43.5%-49.9%), and 54.1% (95% CI, 50.7%-57.4%) in participants who had mucus plugs in 0, 1 to 2, and 3 or more lung segments, respectively. The presence of mucus plugs in 1 to 2 vs 0 and 3 or more vs 0 lung segments was associated with an adjusted hazard ratio of death of 1.15 (95% CI, 1.02-1.29) and 1.24 (95% CI, 1.10-1.41), respectively. Conclusions and Relevance In participants with COPD, the presence of mucus plugs that obstructed medium- to large-sized airways was associated with higher all-cause mortality compared with patients without mucus plugging on chest CT scans.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

Background and Aims: The presence of at-risk NASH is associated with an increased risk of cirrhosis and complications. Therefore, noninvasive identification of at-risk NASH with an accurate biomarker is a critical need for pharmacologic therapy. We aim to explore the performance of several magnetic resonance (MR)-based imaging parameters in diagnosing at-risk NASH. Approach and Results: This prospective clinical trial (NCT02565446) includes 104 paired MR examinations and liver biopsies performed in patients with suspected or diagnosed NAFLD. Magnetic resonance elastography-assessed liver stiffness (LS), 6-point Dixon-derived proton density fat fraction (PDFF), and single-point saturation-recovery acquisition-calculated T1 relaxation time were explored. Among all predictors, LS showed the significantly highest accuracy in diagnosing at-risk NASH [AUCLS: 0.89 (0.82, 0.95), AUCPDFF: 0.70 (0.58, 0.81), AUCT1: 0.72 (0.61, 0.82), z-score test z >1.96 for LS vs any of others]. The optimal cutoff value of LS to identify at-risk NASH patients was 3.3 kPa (sensitivity: 79%, specificity: 82%, negative predictive value: 91%), whereas the optimal cutoff value of T1 was 850 ms (sensitivity: 75%, specificity: 63%, and negative predictive value: 87%). PDFF had the highest performance in diagnosing NASH with any fibrosis stage [AUCPDFF: 0.82 (0.72, 0.91), AUCLS: 0.73 (0.63, 0.84), AUCT1: 0.72 (0.61, 0.83), |z| <1.96 for all]. Conclusion: Magnetic resonance elastography-assessed LS alone outperformed PDFF, and T1 in identifying patients with at-risk NASH for therapeutic trials.

Abstract Glioblastoma (GBM), a lethal astrocytic brain tumor, has limited available treatment options. A major hindrance to the progress of new therapies is the selectivity of the blood brain barrier (BBB) which limits drug access to the tumor. Further, there are currently no minimally-invasive tests that can quickly and accurately determine whether novel therapeutics have crossed the BBB during clinical trials. Therefore, development of a non-invasive test that can identify drug delivery and target engagement in GBM represents an opportunity to enhance knowledge during early phase trials of a new compound’s pharmacological suitability and potential efficacy. This study proposes that cargo contained in GBM-derived extracellular vesicles (EVs), which are small membrane-bound particles released from cells, may serve as one such pharmacodynamic reporter. We analyzed RNA-seq data for EVs isolated from two GBM patient-derived xenograft models grown as short-term in vitro cultures, GBM10 and GBM120, following treatment with either temozolomide (TMZ) or pevonedistat (MLN). We assessed whether EV RNA payloads vary with different drug concentrations (EC50 vs EC80) and at different time points (4hr vs 24hr) post treatment. In the samples treated with MLN, a few long non-coding RNAs are commonly upregulated across the different time points and two models. Under different TMZ concentrations, differentially expressed genes are overrepresented in biological processes such as cytoplasmic translation and cellular macromolecule biosynthesis. We also found a negative correlation between the TMZ concentration and Y RNA expression level, which are functionally involved with DNA replication. Additionally, using CIRI2, we predicted the amount of circular RNAs in the EV cargo; we found that the ratio of circular RNAs to their linear counterparts (CLR) varies under different treatments. In GBM10, the percentage of circRNA with CLR &gt; 1 decreases with increasing drug effect. Thus, our preliminary results indicate that EV cargo post-treatment may be a novel pharmacodynamic reporter in GBM. To apply such an assessment to patients, we have simultaneously pursued the development of an immunoprecipitation (IP) approach to enrich GBM-derived EVs that can then undergo cargo profiling. Potential biomarkers were screened across isolated GBM EVs from in vitro supernatant by western blot and ExoView analysis. B7H3 has emerged as a lead candidate for GBM-EV enrichment. To validate the ability of EV cargo to reflect drug distribution across the BBB in patients, current efforts are focused on applying IP enrichment of GBM-EVs in longitudinally-acquired cerebrospinal fluid samples from patients during their disease course. Overall, we anticipate that the results of this study will lead to development of a clinical test that reflects BBB penetration and tumor response, which will likely aid in novel drug development efforts. Citation Format: Valerie DeLuca, Nanyun Tang, Yue Hao, Anjali Raju, Charles Shaffer, Cecile Riviere-Cazaux, Terry C. Burns, Jann Sarkaria, Ian Parney, Patrick Pirrotte, Kendall Van Keuren-Jensen, Michael E. Berens. Extracellular vesicles as a pharmacodynamic reporter in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2788.