Citations

Infantile neuroaxonal dystrophy (INAD) is caused by recessive variants in PLA2G6 and is a lethal pediatric neurodegenerative disorder. Loss of the Drosophila homolog of PLA2G6 , leads to ceramide accumulation, lysosome expansion, and mitochondrial defects. Here, we report that retromer function, ceramide metabolism, the endolysosomal pathway, and mitochondrial morphology are affected in INAD patient-derived neurons. We show that in INAD mouse models, the same features are affected in Purkinje cells, arguing that the neuropathological mechanisms are evolutionary conserved and that these features can be used as biomarkers. We tested 20 drugs that target these pathways and found that Ambroxol, Desipramine, Azoramide, and Genistein alleviate neurodegenerative phenotypes in INAD flies and INAD patient-derived neural progenitor cells. We also develop an AAV-based gene therapy approach that delays neurodegeneration and prolongs lifespan in an INAD mouse model.

OBJECTIVE The vertebral column is the most common site for skeletal metastasis, often leading to debilitating pain and weakness. Metastatic cancer has unique genetic drivers that potentiate tumorigenicity. There is an unmet need for novel targeted therapy in patients with spinal metastatic disease. METHODS The authors assessed the effect of verteporfin-induced yes-associated protein (YAP) inhibition on spine metastatic cell tumorigenicity and radiation sensitivity in vitro. Animal studies used a subcutaneous xenograft mouse model to assess the use of systemic intraperitoneal verteporfin (IP-VP) and intratumoral verteporfin microparticles (IT-VP) to inhibit the tumorigenicity of lung and breast spinal metastatic tumors from primary patient-derived tissue. RESULTS Verteporfin led to a dose-dependent decrease in migration, clonogenicity, and cell viability via inhibition of YAP and downstream effectors cyclin D1, CTGF, TOP2A, ANDRD1, MCL-1, FOSL2, KIF14, and KIF23. This was confirmed with knockdown of YAP. Verteporfin has an additive response when combined with radiation, and knockdown of YAP rendered cells more sensitive to radiation. The addition of verteporfin to YAP knockdown cells did not significantly alter migration, clonogenicity, or cell viability. IP-VP and IT-VP led to diminished tumor growth (p < 0.0001), especially when combined with radiation (p < 0.0001). Tissue analysis revealed diminished expression of YAP (p < 0.0001), MCL-1 (p < 0.0001), and Ki-67 (p < 0.0001) in tissue from verteporfin-treated tumors compared with vehicle-treated tumors. CONCLUSIONS This is the first study to demonstrate that verteporfin-mediated inhibition of YAP leads to diminished tumorigenicity in lung and breast spinal metastatic cancer cells. Targeting of YAP with verteporfin offers promising results that could be translated to human clinical trials.

Leukemia cells accumulate DNA damage but altered DNA repair mechanisms protect them from apoptosis. We showed here that formaldehyde generated by serine/one-carbon cycle metabolism contributed to accumulation of toxic DNA-protein crosslinks (DPCs) in leukemia cells, especially in driver clones harboring oncogenic tyrosine kinases [OTKs: FLT3(ITD), JAK2(V617F), BCR/ABL1]. To counteract this effect, OTKs enhanced the expression of DNA polymerase theta (POLq) by ERK1/2 serine/threonine kinase-dependent inhibition of c-CBL E3 ligase-mediated ubiquitination of POLq and its proteasomal degradation. Overexpression of POLq in OTK-positive cells resulted in efficient repair of DPC-containing DNA double-strand breaks (DSBs) by POLq-mediated end-joining (TMEJ). Transforming activity of OTKs and other leukemia-inducing oncogenes, especially of those causing inhibition of BRCA1/2 -mediated homologous recombination (HR) with and without concomitant inhibition of DNA-PK -dependent non-homologous end-joining (D-NHEJ), was abrogated in Polq-/- murine bone marrow cells. Genetic and pharmacological targeting of POLq polymerase and helicase activities revealed that both activities are promising targets in leukemia cells. Moreover, OTK inhibitor or DPC-inducing drug etoposide enhanced anti-leukemia effect of POLq inhibitor (POLqi) in vitro and in vivo. In conclusion, we demonstrated that POLq plays an essential role in protecting leukemia cells from metabolically induced toxic DNA lesions triggered by formaldehyde and that it can be targeted to achieve therapeutic effect.

AbstractBackgroundCyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo‐ovarian high‐grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large‐scale, histotype‐specific validation has been performed. The hypothesis was that high‐level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC.MethodsWithin the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated.ResultsHigh‐level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high‐level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08‐1.47, p = .034, and HR, 1.18; 95% CI, 1.05‐1.32, p = .015, respectively). This was also true for cases with combined high‐level amplification/overexpression (HR, 1.26; 95% CI, 1.09‐1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1‐SD increase; 95% CI, 0.94‐1.06; p = .58). CCNE1 high‐level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss.ConclusionThis study provides large‐scale validation that CCNE1 high‐level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC.

AbstractBackgroundResting state functional connectivity (rsfMRI) studies have shown functional network disruptions in logopenic progressive aphasia (LPA) (reduced connectivity within the left temporal language network) and posterior cortical atrophy (PCA) (reduced connectivity within ventral visual network). However, little is known about how these networks compare and how connectivity differs within and between networks in these atypical Alzheimer’s Disease (AD) phenotypes.MethodFifty‐nine amyloid‐positive PCA patients and eighty‐five amyloid‐positive LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore the default mode network (DMN), salience, sensorimotor, left language, visual and memory networks, which were generated by averaging multiple regions‐of‐interest (ROI) within each network. The data was analyzed at both the voxel‐level and network‐level (ROI‐ROI).ResultVoxel‐level differences in functional connectivity between PCA and LPA were observed in all networks (p<0.05; cluster size, FWE=0.05). PCA showed reduced connectivity in the occipital lobe in visual and memory networks, parietal lobe in language and DMN, and sensorimotor strip in sensorimotor network, particularly on the right, compared to LPA. Conversely, LPA showed reduced connectivity in the frontal cortex in salience, memory, visual and sensorimotor networks, inferior parietal lobe in visual network, and anterior temporal pole and sensorimotor cortex in the language network, compared to PCA. Between network differences (p<0.05; cluster size, FDR=0.05) were observed between groups, with PCA showing reduced connectivity between the visual and sensorimotor networks, while LPA showed reduced connectivity between the language and sensorimotor networks. The DMN was anti‐correlated to the language network in PCA, while it was positively correlated to the language network in LPA. In both phenotypes, the DMN was anticorrelated with the salience network. The visual network in PCA showed connections to all networks, while in LPA, it was only connected to the salience, language and DMN.ConclusionThe atypical AD phenotypes were associated with multiple disruptions in connectivity, both within and between brain networks. Phenotype specific differences in connectivity patterns were noted in visual network for PCA and language network for LPA.

AbstractBackgroundThe ability to detect malformed prion proteins using real‐time quaking‐induced conversion (RT‐QuIC) assays has transformed the diagnostic approach to sporadic Creutzfeldt‐Jakob disease (CJD), facilitating earlier recognition of affected patients. Recognizing this, we evaluated the frequency of clinical features and biomarker results at initial evaluation in patients with CJD and determined the relationship between these early features and CJD diagnosis and prognosis.MethodClinical data were extracted from the electronic medical records of patients with probable or definite (pathologically confirmed) CJD assessed at Mayo Clinic from 2014‐2021. Diagnoses of probable CJD were established by clinical consensus, referencing the Centers for Disease Control and Prevention diagnostic criteria. Clinical features and biomarkers results were evaluated at presentation, and associations with CJD diagnosis and symptomatic disease duration determined.ResultOne‐hundred fifteen patients were included (40, [35%] with definite CJD). Mean age‐at‐symptom onset was 64.8±9.4 years; 68 patients were female (59%). The sensitivity of clinical markers (myoclonus) and tests historically considered in patients with suspected CJD (e.g., stereotyped EEG abnormalities (16%), CSF 14‐3‐3 (60%)) was poor. Biomarkers with good diagnostic sensitivity included RT‐QuIC (93%), t‐tau levels >1149 pg/mL (88%), and characteristic signal abnormalities on MRI (77%). Multivariable linear regression confirmed shorter survival for patients with myoclonus (125.9 days, 95%CI 23.3‐15.5, p=0.026), visual/cerebellar signs (180.19 days, 95%CI 282.2‐78.2, p<0.001), positive 14‐3‐3 (193 days, 95%CI 304.9‐82.9; p<0.001), and elevated t‐tau levels (each 1000 pg/ml increase was associated with a nine‐day shorter time‐to‐death, 95%CI 1‐18; p=0.041).ConclusionCSF RT‐QuIC and elevated t‐tau levels, and stereotyped MRI abnormalities continue to be strongly associated with the diagnosis in the modern era, while other clinical findings (myoclonus) and biomarkers results traditionally ascribed to CJD (e.g., PSWC on EEG, 14‐3‐3) offered less value in the diagnostic evaluation. Detection of visual or cerebellar features, myoclonus, CSF 14‐3‐3, and t‐tau levels may predict disease duration, justifying inclusion in the evaluation of CJD‐suspected patients.

AbstractBackgroundReduced striatal uptake on ioflupane SPECT is considered an indicative biomarker for Dementia with Lewy Bodies (DLB), and has been proposed as an indicative biomarker for Mild Cognitive Impairment with Lewy Bodies (MCI‐LB). Little data exists on the profile of uptake on key striatal regions of interest (ROIs) in DLB or MCI‐LB.MethodParticipants with DLB or MCI‐LB at baseline based on the presence of ≥2 core clinical DLB features, and ≥2 ioflupane SPECT scans performed approximately annually, were identified from the Mayo ADRC database. Minimum brain laterality z‐score values for the putamen (whole, anterior and posterior), caudate and striatum were determined using DaTQUANT 2 software. Baseline clinical and scan data was used for all analyses, and the annualized minimum putamen z‐change was determined on serial scans. The scan was considered abnormal if the minimum putamen z‐score was <‐0.98 based on clinicopathologic data.Result27 participants were included (21 DLB and 6 MCI‐LB, 96% male). Compared to MCI‐LB, DLB had a lower mean MoCA score and a trend toward a higher UPDRS score compared to MCI‐LB; there were no statistically significant differences in age, sex, onset age or duration of cognitive decline, or presence of other core DLB features. The majority (89%) had an abnormal scan at baseline. All ROI z‐scores showed the expected general trend of DLB<MCI‐LB, and the annualized z‐change was more negative in DLB, but none of the findings reached statistical significance.ConclusionThese findings suggest in MCI‐LB and DLB that 1) an abnormal ioflupane SPECT scan is frequently present at diagnosis, 2) z‐scores become more negative over time, and 3) the annualized z‐change is similar. Similar findings in larger cohorts would support the use of ioflupane SPECT as a biomarker in both MCI‐LB and DLB clinical trials.

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

AbstractBackgroundMulti‐compartment modelling of white matter (WM) microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) can provide information on WM health through the neurite (axons and dendrites) density index and free water measures. We hypothesized that cerebrovascular disease (CVD), Alzheimer’s disease (AD), and an FDG pattern suggestive of TDP‐43 proteinopathy would be associated with distinct NODDI readouts of WM damage which would be informative for identifying the substrate for cognitive impairment.MethodWe identified two independent cohorts with multi‐shell diffusion MRI, amyloid and tau PET, and cognitive assessments. One, were participants from the Mayo Clinic Study of Aging, a population‐based cohort of 347 elderly randomly sampled from the Olmsted county, Minnesota. The second were 61 amyloid positive AD participants from the Mayo AD Research Center.ResultWe observed an increase in free water and decrease in neurite density in the genu of the corpus callosum associated with vascular risk factors, which we refer to as vascular WM component (Figure 1). Tau PET signal reflective of 3R/4R tau deposition was associated with worsening neurite density index in the temporal WM where we measured parahippocampal cingulum and inferior temporal WM bundles. Worsening temporal neurite density was associated with (antemortem confirmed) FDG TDP‐43 signature. Post‐mortem neuropathologic data on a small subset (n=9) of this sample lend support to our findings. In the population‐based cohort where vascular disease was more prevalent, the vascular WM component explained variability in global cognition (partial R2 of free water and neurite density = 8.3%) and MMSE performance (8.2%) which was comparable to amyloid PET (7.4% for global cognition and 6.6% for memory) (Table 1). In the AD dementia clinic‐based cohort, tau deposition was the greatest contributor to cognitive performance (9.6%), but there was also a non‐trivial contribution of the temporal WM component (8.5%) to cognitive performance. The differences observed between the two cohorts were reflective of their distinct clinical composition.ConclusionWhite matter microstructural damage assessed using advanced diffusion models may add significant value for distinguishing the underlying substrate (whether CVD versus neurodegenerative disease caused by tau deposition or TDP‐43 pathology) for cognitive impairment in older individuals.

AbstractBackgroundIn vivo tau‐positron emission tomography (PET) is an attractive biomarker for Alzheimer’s disease (AD) diagnosis and treatment. However, tau‐PET is less widely available than other modalities. In this study, we tested cross‐modality synthesis of tau‐PET brain images from fluorodeoxyglucose F‐18 (FDG)‐PET using a deep convolutional neural network (CNN).MethodParticipants (n=1,192) who had brain FDG‐PET with 18F‐FDG and tau‐PET with Flortaucipir (F‐18‐AV‐1451) were included for training and testing. This cohort spanned normal aging (ages 26‐98), pre‐clinical, and clinical AD and related disorders including the FTD and DLB spectrum. External validation was done using ADNI (n=288). The PET scans were co‐registered to the corresponding MRI and subsequently warped to Mayo Clinic Adult Lifespan Template (MCALT) space. Tau‐PET images were SUVR‐normalized to the cerebellar crus, and FDG to the pons. A 3D dense‐U‐net model was utilized as an architecture. Cross‐validation experiments were conducted using 5‐fold validations (60% training set, 20% validation set, and 20% test set) with mean squared error as the loss function.ResultOur dense‐U‐net model successfully synthesized tau‐PET from metabolic images with good correlation and low prediction error for regional SUVRs (Figure 1A‐C). The model showed a robust prediction ability, performing accurately in an independent, external ADNI cohort (Figure 1D‐F). The model‐imputed tau‐PET significantly improved performance in classifying tau positivity (mean AUROC(±SD)=0.78±0.04 and 0.85±0.03 for FDG‐PET and synthesized tau‐PET, respectively) and diagnostic groups (cognitively unimpaired with abnormal amyloid‐PET vs. cognitively impaired with abnormal amyloid‐PET) compared to the original input FDG data (mean AUROC(±SD)=0.89±0.04, 0.85±0.05 0.91±0.04 for actual tau‐PET, FDG‐PET and synthesized tau‐PET, respectively) (Figure 2), suggesting enhanced clinical utility for metabolic images. The ADNI cohort also showed similar results (for tau positivity: AUROC=0.66 and 0.78 for FDG‐PET and synthesized tau‐PET, respectively; for CU A+ vs. CI A+: AUROC=0.86, 0.62, and 0.73 for actual tau‐PET, FDG‐PET, and synthesized tau‐PET; Figure 3).ConclusionWe showed that using a CNN model to predict tau‐PET from FDG‐PET is feasible. The synthesized tau‐PET can augment the value of FDG‐PET, facilitating the multi‐modal diagnosis of AD.