Citations

Abstract Objective Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. Process A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. Conclusions Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Importance Radiologically isolated syndrome (RIS) represents the earliest detectable preclinical phase of multiple sclerosis (MS) punctuated by incidental magnetic resonance imaging (MRI) white matter anomalies within the central nervous system. Objective To determine the time to onset of symptoms consistent with MS. Design, Setting, and Participants From September 2017 to October 2022, this multicenter, double-blind, phase 3, randomized clinical trial investigated the efficacy of teriflunomide in delaying MS in individuals with RIS, with a 3-year follow-up. The setting included referral centers in France, Switzerland, and Turkey. Participants older than 18 years meeting 2009 RIS criteria were randomly assigned (1:1) to oral teriflunomide, 14 mg daily, or placebo up to week 96 or, optionally, to week 144. Interventions Clinical, MRI, and patient-reported outcomes (PROs) were collected at baseline and yearly until week 96, with an optional third year in the allocated arm if no symptoms have occurred. Main outcomes Primary analysis was performed in the intention-to-treat population, and safety was assessed accordingly. Secondary end points included MRI outcomes and PROs. Results Among 124 individuals assessed for eligibility, 35 were excluded for declining to participate, not meeting inclusion criteria, or loss of follow-up. Eighty-nine participants (mean [SD] age, 37.8 [12.1] years; 63 female [70.8%]) were enrolled (placebo, 45 [50.6%]; teriflunomide, 44 [49.4%]). Eighteen participants (placebo, 9 [50.0%]; teriflunomide, 9 [50.0%]) discontinued the study, resulting in a dropout rate of 20% for adverse events (3 [16.7%]), consent withdrawal (4 [22.2%]), loss to follow-up (5 [27.8%]), voluntary withdrawal (4 [22.2%]), pregnancy (1 [5.6%]), and study termination (1 [5.6%]). The time to the first clinical event was significantly extended in the teriflunomide arm compared with placebo, in both the unadjusted (hazard ratio [HR], 0.37; 95% CI, 0.16-0.84; P = .02) and adjusted (HR, 0.28; 95% CI, 0.11-0.71; P = .007) analysis. Secondary imaging end point outcomes including the comparison of the cumulative number of new or newly enlarging T2 lesions (rate ratio [RR], 0.57; 95% CI, 0.27-1.20; P = .14), new gadolinium-enhancing lesions (RR, 0.33; 95% CI, 0.09-1.17; P = .09), and the proportion of participants with new lesions (odds ratio, 0.72; 95% CI, 0.25-2.06; P = .54) were not significant. Conclusion and Relevance Treatment with teriflunomide resulted in an unadjusted risk reduction of 63% and an adjusted risk reduction of 72%, relative to placebo, in preventing a first clinical demyelinating event. These data suggest a benefit to early treatment in the MS disease spectrum. Trial Registration ClinicalTrials.gov Identifier: NCT03122652

Verteporfin and 5-ALA are used for visualizing malignant tissue components in different body tumors and as photodynamic therapy in treating isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM). Additionally, verteporfin interferes with Yes-associated protein 1 (YAP)/Transcriptional coactivator with PDZ-binding motif - TEA domain (TAZ-TEAD) pathway, thus inhibiting the downstream effect of these oncogenes and reducing the malignant properties of GBM. Animal studies have shown verteporfin to be successful in increasing survival rates, which have led to the conduction of phase 1 and 2 clinical trials to further investigate its efficacy in treating GBM. In this article, we aimed to review the novel mechanism of verteporfin’s action, the impact of its interaction with YAP/TAZ-TEAD, its effect on glioblastoma stem cells, and its role in inducing ferroptosis.

Aberrant activation of the Hedgehog (Hh) signaling pathway, through which the GLI family of transcription factors (TF) is stimulated, is commonly observed in cancer cells. One well-established mechanism of this increased activity is through the inactivation of Suppressor of Fused (SUFU), a negative regulator of the Hh pathway. Relief from negative regulation by SUFU facilitates GLI activity and induction of target gene expression. Here, we demonstrate a novel role for SUFU as a promoter of GLI activity in pancreatic ductal adenocarcinoma (PDAC). In non-ciliated PDAC cells unresponsive to Smoothened agonism, SUFU overexpression increases GLI transcriptional activity. Conversely, knockdown (KD) of SUFU reduces the activity of GLI in PDAC cells. Through array PCR analysis of GLI target genes, we identified B-cell lymphoma 2 (BCL2) among the top candidates down-regulated by SUFU KD. We demonstrate that SUFU KD results in reduced PDAC cell viability, and overexpression of BCL2 partially rescues the effect of reduced cell viability by SUFU KD. Further analysis using as a model GLI1, a major TF activator of the GLI family in PDAC cells, shows the interaction of SUFU and GLI1 in the nucleus through previously characterized domains. Chromatin immunoprecipitation (ChIP) assay shows the binding of both SUFU and GLI1 at the promoter of BCL2 in PDAC cells. Finally, we demonstrate that SUFU promotes GLI1 activity without affecting its protein stability. Through our findings, we propose a novel role of SUFU as a positive regulator of GLI1 in PDAC, adding a new mechanism of Hh/GLI signaling pathway regulation in cancer cells.

AbstractBackground and PurposeTo determine the incidence of acute neuroimaging (NI) findings and comorbidities in the coronavirus disease of 2019 (COVID‐19)‐infected subjects in seven U.S. and four European hospitals.MethodsThis is a retrospective study of COVID‐19‐positive subjects with the following inclusion criteria: age >18, lab‐confirmed COVID‐19 infection, and acute NI findings (NI+) attributed to COVID‐19 on CT or MRI brain. NI+ and comorbidities in total hospitalized COVID‐19‐positive (TN) subjects were assessed.ResultsA total of 37,950 COVID‐19‐positive subjects were reviewed and 4342 underwent NI. NI+ incidence in subjects with NI was 10.1% (442/4342) including 7.9% (294/3701) in the United States and 22.8% (148/647) in Europe. NI+ incidence in TN was 1.16% (442/37,950). In NI (4342), incidence of ischemic stroke was 6.4% followed by intracranial hemorrhage (ICH) (3.8%), encephalitis (0.5%), sinus venous thrombosis (0.2%), and acute disseminated encephalomyelitis (ADEM) (0.2%). White matter involvement was seen in 57% of NI+. Hypertension was the most common comorbidity (54%) before cardiac disease (28.8%) and diabetes mellitus (27.7%). Cardiac disease (p < .025), diabetes (p < .014), and chronic kidney disease (p < .012) were more common in the United States.ConclusionThis multicenter, multinational study investigated the incidence and spectrum of NI+ in 37,950 hospitalized adult COVID‐19 subjects including regional differences in incidences of NI+, associated comorbidities, and other demographics. NI+ incidence in TN was 1.16% including 0.95% in the United States and 2.09% in Europe. ICH, encephalitis, and ADEM were common in Europe, while ischemic strokes were more common in the United States. In this cohort, incidence and distribution of NI+ helped characterize the neurological complications of COVID‐19.

Background: Vaccination in patients with multiple sclerosis (MS) treated with immunosuppressive drugs is highly recommended. Regarding COVID-19 vaccination, no specific concern has been raised. Objectives: We aimed to evaluate if COVID-19 vaccination or infection increased the risk of disease activity, either radiological or clinical, with conversion to MS in a cohort of people with a radiologically isolated syndrome (RIS). Methods: This multicentric observational study analyzed patients in the RIS Consortium cohort during the pandemic between January 2020 and December 2022. We compared the occurrence of disease activity in patients according to their vaccination status. The same analysis was conducted by comparing patients’ history of COVID-19 infection. Results: No difference was found concerning clinical conversion to MS in the vaccinated versus unvaccinated group (6.7% vs 8.5%, p > 0.9). The rate of disease activity was not statistically different (13.6% and 7.4%, respectively, p = 0.54). The clinical conversion rate to MS was not significantly different in patients with a documented COVID-19 infection versus non-infected patients. Conclusion: Our study suggests that COVID-19 infection or immunization in RIS individuals does not increase the risk of disease activity. Our results support that COVID-19 vaccination can be safely proposed and repeated for these subjects.

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.

Abstract OBJECTIVES The prevalence and aetiology of acute aortic dissection type A (AADA) in patients ≤30 years is unknown. The aims of this clinical study were to determine the prevalence and potential aetiology of AADA in surgically treated patients ≤30 years and to evaluate the respective postoperative outcomes in this selective group of patients in a large multicentre study. METHODS Retrospective data collection was performed at 16 participating international aortic institutions. All patients ≤30 years at the time of dissection onset were included. The postoperative results were analysed with regard to connective tissue disease (CTD). RESULTS The overall prevalence of AADA ≤30 years was 1.8% (139 out of 7914 patients), including 51 (36.7%) patients who were retrospectively diagnosed with CTD. Cumulative postoperative mortality was 8.6%, 2.2% and 1.4%. Actuarial survival was 80% at 10 years postoperatively. Non-CTD patients (n = 88) had a significantly higher incidence of arterial hypertension (46.6% vs 9.8%; P &lt; 0.001) while AADA affected the aortic root (P &lt; 0.001) and arch (P = 0.029) significantly more often in the CTD group. A positive family history of aortic disease was present in 9.4% of the study cohort (n = 13). CONCLUSIONS The prevalence of AADA in surgically treated patients ≤30 years is &lt;2% with CTD and arterial hypertension as the 2 most prevalent triggers of AADA. Open surgery may be performed with good early results and excellent mid- to long-term outcomes.