Citations

Background/Objectives: The role of iron in Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) has been debated. However, the effects of iron on protein burden in these diseases remain poorly elucidated. We hypothesized that there is an association between iron deposition and tau and beta-amyloid deposition in deep grey matter (GM) regions in FTLD. Methods: Neuropathological samples were collected from two control subjects and 10 disease subjects. This included 12 subjects, with several pathological subgroups represented by only one or two cases. Of the 10 disease subjects, one had low-level AD neuropathology (L-AD), and one had high-level AD neuropathology (H-AD), whereas the remaining eight subjects had FTLD-tau pathology, including progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), globular glial tauopathy (GGT), and Pick’s disease (PiD). Neuropathological samples were stained for iron with Prussian blue (PB) and ferritin, tau, and beta-amyloid in the basal ganglia (BG). All 12 subjects had also undergone diffusion MRI, amyloid-PET (PiB-PET), and Tau-PET (flortaucipir) before death. Diffusion tensor imaging mean diffusivity (MD) from BG was examined in each subject. Spearman’s rank correlations between imaging and histology were calculated. Results: Increased BG iron was observed in PSP subjects. Prussian blue and ferritin measures were associated with higher tau- and β-amyloid pathological burden. Tau-PET uptake correlated with iron in the BG. Conclusions: The results of this exploratory study suggest associations between iron burden and tau- and β-amyloid-related measures within basal ganglia regions that may help to explain increased PET signals in AD and FTLD-Tau. Our findings should be considered to be hypothesis-generating and require validation. These preliminary findings require validation in larger cohorts.

Abstract Introduction Quantitative interpretation of pediatric brain [ 18 F]-FDG PET is limited by the lack of age-matched normative reference databases, despite substantial developmental variation in cerebral glucose metabolism. This study aimed to construct a large-scale normative pediatric [ 18 F]-FDG PET database and develop an age-specific z-score mapping framework for quantitative evaluation of pediatric epilepsy. Method Pediatric brain PET data were retrospectively derived from clinically acquired whole-body or dedicated [ 18 F]-FDG PET examinations in participants 0–19 years of age without neurologic disease or visible brain abnormalities. A total of 417 PET scans from 341 participants were stratified into five developmental age groups: infancy, early childhood, middle childhood/school age, adolescence, and late adolescence. SUVR maps were generated using whole-brain atlas and brainstem reference regions. Voxel-wise t-test analyses were performed to characterize age- and sex-related metabolic differences. Age-specific voxel-wise mean and standard deviation maps were generated from the normative cohort and used to calculate age-matched hypometabolic z-score maps in 14 pediatric patients with epilepsy. The resulting abnormalities were qualitatively evaluated for concordance with epileptogenic findings identified on clinical MRI and/or PET by expert image review. Results Voxel-wise analyses demonstrated prominent age-dependent SUVR differences, particularly between infancy and older age groups. With whole-brain atlas normalization, older children showed progressively higher relative SUVR in cortical and cerebellar gray matter regions, whereas infancy showed relatively higher SUVR in the occipital cortex, white matter, thalamus, and brainstem. Brainstem normalization produced distinct developmental patterns, emphasizing higher white matter SUVR in infancy and broader gray matter differences in older groups. Sex-related differences were limited and regionally specific. Whole-brain atlas normalization showed lower variability than brainstem normalization and was selected for z-score mapping. In representative epilepsy cases, age-matched z-score maps identified focal or regional hypometabolism corresponding to malformations of cortical development, focal cortical dysplasia, mesial temporal sclerosis, nonlesional MRI cases, remote insult, tumor-related abnormalities, and Sturge-Weber syndrome. Discussion A large-scale normative pediatric [18F]-FDG PET database can support developmentally appropriate quantitative interpretation of pediatric brain PET. Age-matched z-score mapping may complement visual assessment by highlighting abnormal cerebral glucose metabolism in pediatric epilepsy, although findings should be interpreted with MRI, EEG, seizure semiology, and other presurgical data.

Abstract Clinical syndromes of frontotemporal dementia present both distinct and overlapping neuroimaging abnormalities and, consequently, domains of impairment. The aim of this study was to determine how well covariance patterns from multiple neuroimaging modalities discriminate across clinical frontotemporal dementia syndromes. Four-hundred participants with a clinical frontotemporal dementia diagnosis, including behavioral variant of frontotemporal dementia, semantic variant of primary progressive aphasia, right temporal variant of frontotemporal dementia, primary progressive aphasia, apraxia of speech with agrammatic aphasia, primary progressive apraxia of speech, progressive supranuclear palsy and corticobasal syndrome, and 109 cognitively unimpaired participants underwent extensive neurological and neuropsychological assessments, structural MRI, flortaucipir-PET for tau and [18F] fluorodeoxyglucose-PET. Multimodal imaging covariance was investigated at the voxel-level using principal component analysis after adjusting for age and sex effects. Linear regression models were fit to investigate the relationship between principal components and clinical scores. Multinomial logistic regression models investigated the ability of imaging principal components to discriminate across syndromes. The first principal component, which describes the largest source of variability in the data, was a pattern of widespread cortical atrophy (21% variability explained) and of frontoparietal and temporal hypometabolism (21%): scores were lowest in behavioral variant of frontotemporal dementia and correlated with general cognitive impairment. On tau-PET, the first principal component (43% variability) was not significantly different among syndromes and captured a widespread cortical tau uptake, with a focus on the temporal lobe. In all three modalities, the second principal component captured temporal lobe imaging abnormalities, contrasting the motor and semantic clinical syndromes. Subsequent components described smaller variability. On cross-validation, the classifiers based on principal components from MRI (multiclass area under the receiver operating characteristic: 0.76) and [18F]fluorodeoxyglucose-PET (0.79) outperformed tau-PET (0.71), while combining all three modalities did not significantly improve the classifier performance. On cross-validation, the sensitivity of the image-based classifiers was heterogeneous across syndromes and modalities, with highest values for progressive supranuclear palsy and semantic variant of primary progressive aphasia and lowest for primary progressive aphasia and corticobasal syndrome. Our study found that the covariance components of structural and molecular neuroimaging underlying frontotemporal dementia clinical syndromes exist both as a continuum and with syndrome-specific patterns. More than one imaging model was able to correctly classify several frontotemporal dementia clinical syndromes with good specificity and sensitivity, highlighting the value of imaging in aiding patient diagnosis.

ABSTRACT Studies in humans and murine models have pointed towards a possible link between metabolic syndrome, which shows insulin resistance and metabolic dysregulation, and Alzheimer’s disease (AD) pathology marked by amyloid-beta (Aβ) accumulation at the BBB and hypometabolism in the brain. Yet, the underlying biological mechanisms by which metabolic syndrome affects these pathological changes in AD brain remain unknown. We hypothesized that insulin resistance is responsible for alterations in blood-brain barrier (BBB) transport of Aβ peptides and glucose. This hypothesis was tested by employing radiolabeled tracers ( 125 I-Aβ40, 125 I-Aβ42, and 18 F-FDG) in high-fat diet (HFD)-fed mouse models that manifest metabolic syndrome. Further, we assessed alterations in the expression of various molecular mediators within the brain microcapillaries harvested from both low-fat diet (LFD)-fed and HFD-fed mice. As expected, our studies show that HFD- fed mice developed peripheral insulin resistance and obesity. In addition, HFD-fed mice demonstrated an increase in the influx rate of Aβ peptides and a reduction in 18 F-FDG (a glucose surrogate) influx rate at the BBB compared to LFD-fed mice. These transport changes are associated with increase in the BBB endothelial expression of RAGE (receptor to traffic Aβ from plasma-to-brain) and a reduction of GLUT1 (glucose transporter) expression in HFD-fed mice compared to LFD-fed mice. Moreover, disruption in insulin signaling, as indicated by reduced pAKT and pERK expression, was observed in HFD-fed mice. Inhibiting AKT or ERK phosphorylation with specific inhibitors resulted in similar changes in Aβ and glucose uptake in polarized BBB endothelial cell monolayers in vitro. These results indicate that HFD induced metabolic syndrome may lead to BBB dysfunction, characterized by increased plasma-to-brain Aβ trafficking and diminished glucose transport at the BBB, thereby aggravating the expression of AD pathological hallmarks.

Abstract Triple-Negative Breast Cancer (TNBC) presents a significant clinical challenge due to its heterogeneity and lack of targeted treatment options, with chemotherapy and immunotherapy combinations currently serving as the main therapeutic strategy. Efforts to address TNBC heterogeneity have largely focused on classifying intrinsic cancer subtypes based on differential tumor mRNA expression, a strategy that has proven effective in hormone receptor-positive breast cancers but has yet to yield a clinically useful predictor of survival or treatment response in TNBC. We hypothesize that both the intrinsic characteristics of TNBC and the surrounding immune microenvironment influence treatment outcomes and that immune cell infiltration affects TNBC subtype classification and response variability. To explore this hypothesis, we compared the predictive and prognostic capabilities of cancer subtype-based (TNBC-type) gene signatures and immune cell deconvolution methods (CIBERSORT) within the same TNBC datasets. We found that immune cell abundance outperformed TNBC subtype-signatures and multicellular immune cell aggregates showed the highest performance of all. More specifically, aggregate immune cells associated with tertiary lymphoid structures and tumor associated macrophages/monocytes demonstrated statistically significant predictive value. These findings were confirmed in an independent cohort of 67 TNBC patients treated with neoadjuvant chemotherapy. Further, single-cell RNA sequencing analysis revealed that the predictive power of cancer-subtype could be partially explained by immune- and stromal features. Examination of single-cell resolution spatial transcriptomic data confirmed presence of TLS-like, TAM- and cancer-stromal niches within TNBC biopsy samples that were associated with treatment response. Overall, our results highlight that immune cell aggregates, which capture the spatial organization of the TME, outperform cell-type specific gene signatures in predicting TNBC outcomes. Our novel approach provides a robust framework for interpreting spatial relationships in bulk RNA-seq data, offering a pathway for reconciling past data with current advancements in spatial profiling technologies. This work paves the way for future studies to leverage the multi-cellular complexity of TNBC, enhancing diagnostic precision and facilitating the development of therapies that strategically modulate the tumor microenvironment for improved anti-cancer responses.

Abstract INTRODUCTION Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter‐software variability and diagnostic performance across five platforms using identical 1 8 F‐florbetapir datasets. METHODS Retrospectively, 192 patients undergoing 1 8 F‐florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) ‐cleared platforms and an in‐house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland–Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation. RESULTS Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961–0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949–0.975), with sensitivity 0.923–0.968 and specificity 0.667–0.806. DISCUSSION Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.

Long-established prostate cancer cell lines provide limited insight into how contemporary early prostate cancer evolves into drug-resistant and neuroendocrine-like refractory disease. Patient-derived three-dimensional (3D) ex vivo models may better preserve this transition. Here, we found that 8 of 10 magnetic resonance imaging-guided biopsy specimens from patients with early-stage prostate cancer generated sustained 3D tumor spheroid cultures. After 12 weeks of enzalutamide selection, only one patient-derived culture acquired a resistant phenotype with treatment-emergent neuroendocrine prostate cancer (t-NEPC)-like features, including increased chromogranin A (CgA) and synaptophysin (SYP); reduced androgen receptor (AR), prostate-specific antigen (PSA), and prostate-specific membrane antigen (PSMA); and conversion from compact spheroids into irregular resistant aggregates. During this transition, WD repeat and SOCS box-containing protein 1 (WSB1) increased, whereas PSMA progressively decreased. WSB1 silencing restored PSMA, AR, and PSA expression and reduced neuroendocrine-associated features. A similar WSB1 dependency was observed in enzalutamide-resistant LNCaP cells, the castration-resistant prostate cancer model 22Rv1, and the neuroendocrine/small-cell prostate cancer model NCI-H660. Mechanistically, WSB1 functioned as a SOCS box-dependent E3 ubiquitin ligase adaptor that promoted PSMA ubiquitination and degradation. SOCS box deletion or T380A mutation impaired this process, while Aurora kinase A (AURKA) inhibition reduced WSB1-dependent PSMA ubiquitination. WSB1 depletion, AURKA inhibition with alisertib, and combined AURKA inhibition with EZH2 suppression reduced resistant aggregate growth and increased apoptosis-associated markers in patient-derived enzalutamide-resistant neuroendocrine-like spheroids and related models. These findings nominate the AURKA–WSB1–PSMA axis as a therapeutic vulnerability in refractory prostate cancer.

Abstract Introduction Alzheimer’s disease (AD) is characterized by pathologies including amyloid, tau, neurodegeneration reflected in established blood biomarkers and track with clinical changes. However, the association between peripheral cell-specific signatures and AD-related phenotypes remain poorly characterized. Methods We analyzed bulk blood transcriptome data from the Mayo Clinic Study of Aging (MCSA) and Emory University Vascular study. We used BayesPrism, CIBERSORTx, and an in-house pipeline CNNreg to deconvolute these data and obtain peripheral cell proportions. Cell type specific transcripts were estimated with BayesPrism. We compared cell proportions between cases (AD/MCI) and controls. Association analysis was performed between cell type specific transcripts and AD-related phenotypes including diagnosis and cognition. Meta-analysis of transcripts associations and gene ontology analysis were conducted to assess the enriched pathways for significant genes. Results In MCSA, B and CD4+ T cells proportions are significantly lower while that of myeloid cells higher in cases. The Emory cohort had similar trends. We identified transcripts associated with AD-related phenotypes in CD4+ T and CD8+ T cells. In CD4+ T cells, transcripts downregulated in case are enriched in response to stimulus pathway, whereas those upregulated in negative regulation of immune response. Downregulation of CD4+ T genes enriched in extracellular matrix disassembly and epigenetic regulation and upregulation of those in protein localization associate with better cognition. In CD8+T cells, genes pertinent to vascular development were downregulated while those to metabolic processes were upregulated in cases. Downregulation of CD8+T genes involved in lipid transportation and upregulation of those in mitochondria associate with better cognition. Conclusion We identified peripheral cellular transcriptional changes associated with AD/MCI phenotypes and involved in important biological pathways, revealing potential disease mechanisms in AD. Funding Source RF1 AG051504 Topic Categories Neuroimmunology (NEUR)

Abstract Background Corticobasal syndrome (CBS) is a neurodegenerative syndrome that often results from a 4-repeat tauopathy. Abnormalities on tau PET, structural MRI, and diffusion MRI (dMRI) have been observed in CBS, although no prior work has systematically compared the relative sensitivity of these modalities. In addition, the relationships between tau uptake, volume loss, and white matter degeneration remain incompletely understood. Objective To compare the sensitivity of tau PET, structural volume, and dMRI metrics to differentiate CBS from controls, and to characterize relationships between regional tau uptake, volume loss, and white matter tract abnormalities. Methods Thirty-two participants meeting criteria for possible or probable CBS and 35 healthy controls underwent standardized 3T dMRI, 18 F-flortaucipir tau PET, and detailed neurologic assessment. Diffusion data were processed using complementary diffusion tensor, free-water, and Neurite Orientation Dispersion and Density Imaging (NODDI) pipelines, with tract-level metrics extracted using the Johns Hopkins University-Eve (JHU EVE) White Matter atlas. Regional gray matter volumes and flortaucipir standardized uptake value ratios (SUVRs) were calculated. Global tau PET uptake, represented by the first principal component (PC1), was removed in a secondary tau analysis. Modality-level discrimination was assessed using area under the curve (AUC) analyses. Cross-modality relationships were evaluated using regional correlations and covariate-adjusted models comparing volume- and tau-related predictors of white matter abnormalities. Results Relative to controls, CBS showed widespread higher mean diffusivity (MD) and lower intracellular volume fraction (ICVF), especially in sensorimotor and projection white matter pathways. Structural volume reductions were most prominent in precentral cortex and subcortical regions including putamen, thalamus, and pallidum, whereas tau PET abnormalities were weaker and less spatially extensive. MD showed the strongest overall discrimination between CBS and controls, followed by ICVF, structural volume, PC1-removed tau PET, and original tau PET. In targeted sensorimotor analyses, higher DTI-MD was most strongly associated with lower gray matter volume. PC1-removed sensorimotor tau uptake was also associated with higher sensorimotor DTI-MD, whereas original tau uptake and broader cortical or subcortical tau measures were not significant predictors. Conclusions White matter microstructural disruption is a dominant imaging signature of CBS and is more closely linked to gray matter volume loss than to measurable uptake on tau PET. dMRI, particularly MD and ICVF, may provide greatest sensitivity for detecting disease-related changes in CBS.

Abstract Background There is limited data on immune checkpoint inhibitor (ICI) use in patients with non-small cell lung cancer (NSCLC) and known pericardial effusions. Methods This international multi-center observational retrospective study included patients with advanced NSCLC with or without pericardial effusions receiving ICIs. Immune-related adverse events (irAEs) were graded per CTCAE v5.0. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. Cox proportional hazards multivariable models assessed the association between survival outcomes and clinicopathologic variables, and an inverse probability of treatment weighting (IPTW) model compared outcomes between the two groups. Results A total of 1248 patients with advanced NSCLC treated with ICIs across 13 institutions were included. Of these, 85 patients (6.8%) had pre-existing pericardial effusions. Compared to controls, patients with pericardial effusions were younger (median age 63 vs 67 years, P = .007), diagnosed more frequently with PD-L1 positive tumors (96% vs 76%, P < .001), and had a higher median number of baseline metastatic sites (3 vs 1, P < .001). IrAEs were similar between both groups (42% vs 38%), and no cardiac irAEs occurred in patients with pericardial effusions. PFS (median 5.6 vs 4.6 months, P = .3) and OS (median 10.2 vs 14.7 months, P = .50) were not statistically different between patients with pericardial effusions and the controls, respectively. In both multivariable and IPTW models, pericardial effusions were not associated with PFS or OS. Conclusions Among patients with NSCLC and pre-existing pericardial effusions, ICIs demonstrate meaningful clinical benefit similar to those without effusions, with no new cardiac or severe toxicities.