Citations

Follicular helper T-cell lymphoma of the angioimmunoblastic type (AITL) is associated with dismal prognosis. We performed functional genomic approaches including whole-exome sequencing (WES; n=119), transcriptomic (n=78) and methylation (n=40) analysis. We identified recurrent mutations in known epigenetic drivers ( TET2, DNMT3A, IDH2 R172), and also identified novel ones (TET3, KMT2D). Somatic mutation of all three epigenetic drivers ( TET2, IDH2, and DNMT3A) was associated with poor prognosis (p<.001). Mutations in genes regulating T-cell receptor (TCR) signaling ( CD28 VAV1, FYN, PLCG1) or activation ( RHOA G17V), and regulators of the PI3K pathway (PIK(3)C members, PTEN,PHLPP-1/-2) were also found. Genome-wide DNA-methylation analysis integrated with mRNA expression profiling also revealed epigenetic alterations in genes regulating TCR-RHOA/B/C or PI3K-signaling. TET2 loss was noted in 85% AITLs and was significantly associated with RHOA G17V, CD28 and IDH2 R172mutations. AITLs lacking RHOA G17V tended to have mutations regulating the JAK-STAT pathway ( JAK2, JAK3, STAT1, STAT3, SOCS1). RNA-seq analysis identified fusion transcripts in genes regulating TCR activation (8%), revealed a restricted TCR repertoire in the majority of cases (a=87%, b=72%), and showed the presence of Epstein-Barr virus transcriptome (73%). GEP demonstrated association of B-cells in the tumor-milieu with better prognosis (p=.006), while dendritic cells were associated with worse prognosis (p=.001), which was further validated by immunohistochemistry using CD20, CD68, and CD163 antibodies. RNA-seq and corresponding WES analysis of 12 AITL patient-derived-xenografts (PDX) showed that bi-allelic TET2 mutations, DNMT3A mutations or sub-clonal mutations ( PLCG1 PHLPP2) werepropagated in sequential passages. Gene signatures related to T FH (follicular helper) and T CM (central memory) were also well-maintained in secondary passages in PDX models. Gene signatures of late PDX passages (3 rd-5 th) were enriched with genes related to proliferation and metabolic reprogramming, and in an independent cohort of AITLs, high expression of T3/T5 related signatures was associated with worse outcome (p=0.02/p=0.009). Low mRNA expression of PHLPP2 predicted poor prognosis (p=.03) and engineered PHLPP2 loss showed enhanced PI(3)K activation and FOXO1 inactivation in CD4+ T-cells in-vitro. Thus, we defined the genomic landscape for AITL, which is largely characterized by epigenetic alterations, TCR signaling and PI3K/AKT dysregulation, which may be amenable for therapeutic targeting.

SummaryMantle cell lymphoma (MCL) is clinically and biologically heterogeneous. While various prognostic features have been proposed, none currently impact therapy selection, particularly in older patients, for whom treatment is primarily dictated by age and comorbidities. Herein, we undertook a comprehensive comparison of clinicopathological features in a cohort of patients 60 years and older, uniformly treated with bendamustine and rituximab, with a median survival of >8 years. The strongest prognostic indicators in this cohort were a high‐risk call by a simplified MCL international prognostic index (s‐MIPI) (HR: 3.32, 95% CI: 1.65–6.68 compared to low risk), a high‐risk call by MCL35 (HR: 10.34, 95% CI: 2.37–45.20 compared to low risk) and blastoid cytology (HR: 4.21, 95% CR: 1.92–9.22 compared to classic). Patients called high risk by both the s‐MIPI and MCL35 had the most dismal prognosis (HR: 11.58, 95% CI: 4.10–32.72), while those with high risk by either had a moderate but clinically relevant prognosis (HR: 2.95, 95% CI: 1.49–5.82). A robust assay to assess proliferation, such as MCL35, along with stringent guidelines for cytological evaluation of MCL, in combination with MIPI, may be a strong path to risk‐stratify older MCL patients in future clinical trials.

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1 *04 subtypes best accounted for the association, strongest with HLA-DRB1 *04:04 and HLA-DRB1 *04:07, and intermediary with HLA-DRB1 *04:01 and HLA-DRB1 *04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1 *04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1 *04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

AbstractObjectiveThyroglobulin measurement is the cornerstone of modern management of differentiated thyroid cancer, with clinical decisions on treatment and follow-up based on the results of such measurements. However, numerous factors need to be considered regarding measurement with and interpretation of thyroglobulin assay results.DesignThe present document provides an integrated update to the 2013 and 2014 separate clinical position papers of our group on these issues.MethodsIssues concerning analytical and clinical aspects of highly-sensitive thyroglobulin measurement will be reviewed and discussed based on an extensive analysis of the available literature.ResultsThyroglobulin measurement remains a highly complex process with many pitfalls and major sources of interference, especially anti-thyroglobulin antibodies, need to be assessed, considered and, when necessary, dealt with appropriately.ConclusionsOur expert consensus group formulated 53 practical, graded recommendations for guidance on highly-sensitive thyroglobulin and TgAb in laboratory and clinical practice, especially valuable where current guidelines do not offer sufficient guidance.

Importance Electroencephalograms (EEGs) are a fundamental evaluation in neurology but require special expertise unavailable in many regions of the world. Artificial intelligence (AI) has a potential for addressing these unmet needs. Previous AI models address only limited aspects of EEG interpretation such as distinguishing abnormal from normal or identifying epileptiform activity. A comprehensive, fully automated interpretation of routine EEG based on AI suitable for clinical practice is needed. Objective To develop and validate an AI model (Standardized Computer-based Organized Reporting of EEG–Artificial Intelligence [SCORE-AI]) with the ability to distinguish abnormal from normal EEG recordings and to classify abnormal EEG recordings into categories relevant for clinical decision-making: epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse. Design, Setting, and Participants In this multicenter diagnostic accuracy study, a convolutional neural network model, SCORE-AI, was developed and validated using EEGs recorded between 2014 and 2020. Data were analyzed from January 17, 2022, until November 14, 2022. A total of 30 493 recordings of patients referred for EEG were included into the development data set annotated by 17 experts. Patients aged more than 3 months and not critically ill were eligible. The SCORE-AI was validated using 3 independent test data sets: a multicenter data set of 100 representative EEGs evaluated by 11 experts, a single-center data set of 9785 EEGs evaluated by 14 experts, and for benchmarking with previously published AI models, a data set of 60 EEGs with external reference standard. No patients who met eligibility criteria were excluded. Main Outcomes and Measures Diagnostic accuracy, sensitivity, and specificity compared with the experts and the external reference standard of patients’ habitual clinical episodes obtained during video-EEG recording. Results The characteristics of the EEG data sets include development data set (N = 30 493; 14 980 men; median age, 25.3 years [95% CI, 1.3-76.2 years]), multicenter test data set (N = 100; 61 men, median age, 25.8 years [95% CI, 4.1-85.5 years]), single-center test data set (N = 9785; 5168 men; median age, 35.4 years [95% CI, 0.6-87.4 years]), and test data set with external reference standard (N = 60; 27 men; median age, 36 years [95% CI, 3-75 years]). The SCORE-AI achieved high accuracy, with an area under the receiver operating characteristic curve between 0.89 and 0.96 for the different categories of EEG abnormalities, and performance similar to human experts. Benchmarking against 3 previously published AI models was limited to comparing detection of epileptiform abnormalities. The accuracy of SCORE-AI (88.3%; 95% CI, 79.2%-94.9%) was significantly higher than the 3 previously published models ( P < .001) and similar to human experts. Conclusions and Relevance In this study, SCORE-AI achieved human expert level performance in fully automated interpretation of routine EEGs. Application of SCORE-AI may improve diagnosis and patient care in underserved areas and improve efficiency and consistency in specialized epilepsy centers.

Abstract Background Donepezil is an approved therapy for the treatment of Alzheimer’s disease (AD). Results across clinical trials have been inconsistent, which may be explained by design-methodological issues, the pathophysiological heterogeneity of AD, and diversity of included study participants. We investigated whether response to Donepezil differs in mild cognitive impaired (MCI) individuals demonstrating different magnetic resonance imaging (MRI) subtypes. Methods From the Hippocampus Study double-blind, randomized clinical trial, we included 173 MCI individuals (donepezil = 83; placebo = 90) with structural MRI data, at baseline and at clinical follow-up assessments (6-12-month). Efficacy outcomes were the annualized percentage change (APC) in hippocampal, ventricular, and total grey matter volumes, as well as in the AD cortical thickness signature. Participants were classified into MRI subtypes as typical-AD, limbic-predominant, hippocampal-sparing, or minimal atrophy at baseline. We compared two subtyping approaches: the conventional categorical approach and an approach based on continuous dimensions. Results Donepezil treated MCI individuals showed slower atrophy rates compared to the placebo group, but only if they belonged to the minimal atrophy or hippocampal sparing subtypes. Importantly, only the continuous subtyping approach, but not the conventional categorical approach, captured this differential response. Conclusions Our data suggest that individuals with MCI, with hippocampal sparing or minimal atrophy subtype, may have improved benefit from donepezil, as compared with MCI individuals with typical or limbic predominant patterns of atrophy. The newly proposed continuous subtyping approach may have advantages compared to the conventional categorical approach. Future research is warranted to demonstrate the potential of subtype stratification for disease prognosis and response to treatment. Trial registration: Clinical Trial.gov Number: NCT00403520, Submission Date: November 21, 2006.

Cerebrospinal fluid (CSF) analysis is of utmost importance for diagnosis and differential diagnosis of patients with suspected multiple sclerosis (MS). Evidence of intrathecal immunoglobulin G (IgG) synthesis proves the inflammatory nature of the disease, increases diagnostic certainty and substitutes for dissemination in time according to current diagnostic criteria. The gold standard to determine intrathecal IgG synthesis is the detection of CSF-restricted oligoclonal bands (OCBs). However, advances in laboratory methods brought up κ-free light chains (FLCs) as a new biomarker, which are produced in excess over intact immunoglobulins and accumulate in CSF in the case of central nervous system-derived inflammation. Overwhelming evidence showed a high diagnostic accuracy of intrathecal κ-FLC synthesis in MS with sensitivity and specificity of approximately 90% similar to OCB. κ-FLCs have advantages as its detection is fast, easy, cost-effective, reliable, rater-independent and returning quantitative results which might also improve the value of predicting MS disease activity. An international panel of experts in MS and CSF diagnostics developed a consensus of all participants. Six recommendations are given for establishing standard CSF evaluation in patients suspected of having MS. The panel recommended to include intrathecal κ-FLC synthesis in the next revision of MS diagnostic criteria as an additional tool to measure intrathecal immunoglobulin synthesis.

Background: Intrathecal immunoglobulin-G synthesis is a hallmark of multiple sclerosis (MS), which can be detected by oligoclonal IgG bands (OCB) or by κ-free light chains (κ-FLC) in cerebrospinal fluid. Objective: To perform a systematic review and meta-analysis to evaluate whether κ-FLC index has similar diagnostic value to identify patients with clinically isolated syndrome (CIS) or MS compared to OCB, and to determine κ-FLC index cut-off. Methods: PubMed was searched for studies that assessed diagnostic sensitivity and specificity of κ-FLC index and OCB to discriminate CIS/MS patients from control subjects. Two reviewers following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines performed study eligibility assessment and data extraction. Findings from studies were analyzed with bivariate mixed models. Results: A total of 32 studies were included in the meta-analysis to evaluate diagnostic value of κ-FLC index. Sensitivity and specificity ranged from 52% to 100% (weighted average: 88%) and 69% to 100% (89%) for κ-FLC index and from 37% to 100% (85%) and 74% to 100% (92%) for OCB. Mean difference of sensitivity and specificity between κ-FLC index and OCB was 2 and −4 percentage points. Diagnostic accuracy determined by mixed models revealed no significant difference between κ-FLC index and OCB. A discriminatory cut-off for κ-FLC index was determined at 6.1. Conclusion: The findings indicate that κ-FLC index has similar diagnostic accuracy in MS as OCB.