Citations

Abstract Methylation profiling has revealed unexpected relationships between traditional histopathology-based versus biologically-based classifications of brain tumors. Methylation class pleomorphic xanthoastrocytoma (mcPXA) comprises tumors with the same methylation signature as classical PXA but with additional morphologic patterns, including overlap with glioblastoma (GBM). To characterize mcPXA, we examined epidemiologic, histologic, and molecular features as well as clinical behavior of a cohort (n=313) of tumors matching to mcPXA by the DKFZ classifier (v12 score >= 0.85). Patient median age was 19 (range 1-73) with a female predominance (178 female/135 male). CDKN2A/B homozygous deletion was observed in 273 (87%) cases. In the subset of cases tested for BRAF p.V600E (n=120), 111 (93%) harbored the mutation. Concomitant chr7+/chr10- was observed in 66 (21%) cases. In the subset of cases tested for TERT promoter mutations (n=50), 11 (22%) harbored the mutation, always with co-occurrence of BRAF p.V600E (11/11) and infrequently with chr7+/chr10- (3/11). MGMT promoter methylation was observed in 40 (13%) cases. Overall and progression-free survival (OS, PFS) in mcPXA were similar to grade 2-3 IDH-mutant astrocytoma (A-IDH). Prognostic factors in mcPXA included GBM-like histopathology (defined as cases diagnosed as GBM prior to methylation profiling), concomitant chr7+/chr10-, and MGMT promoter methylation. GBM-like histopathology was associated with shorter OS (p=0.02) and PFS (p< 0.01) compared to cases without GBM-like histopathology. Concomitant chr7+/chr10- was associated with shorter PFS (p=0.02), and MGMT promoter methylation was associated with shorter OS (p=0.03). Our findings demonstrate that mcPXA tumors affect adolescents and young adults, share molecular features of classical PXA, and manifest clinical behavior similar to classical PXA, but subsets of tumors with atypical features (chr7+/chr10-, MGMT promoter methylation, and/or GBM-like histopathology) are more aggressive. The use of a molecular definition of PXA reveals that this tumor type is more common than previously appreciated and warrants in-depth study for therapeutic options.

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.

Abstract Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treat-ment effects and surgical cavities are not present. Thus, existing automatic segmenta-tion models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmenta-tion on glioma images with different phenotypic and treatment appearance. We as-sessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained techni-cians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Abstract Image-based mathematical modeling is emerging as an advantageous tool to integrate into the care routine for patients with brain tumors. These tools can be used for a variety of tasks including predicting tumor growth, indicating spatial genomic alterations, and guiding radiation therapy. Often, the methods to produce these maps are developed in silos, using cohorts of retrospective patients and switching to a per patient map calculation can be an afterthought. Automatic pipelines are needed to enable creating mathematical maps in a clinically-relevant workflow. To this end, we developed a pipeline to create an image-based mathematical map of edema abundance based on T2-weighted (T2W) magnetic resonance imaging that includes preprocessing steps, abnormality and tissue type segmentation, brain extraction, map generation, and conversion between image file formats. The python-based pipeline was designed to take in T1-weighted (T1W), T1W with gadolinium contrast (T1Gd), T2W, and fluid attenuated inversion recovery (FLAIR) DICOM images for a single patient as input and convert them to NIfTIs for downstream steps. The pipeline preprocessed images by correcting bias field fluctuations using the N4 algorithm and normalizing intensities. The T1W, T1Gd, and FLAIR are then registered to the T2W. T1Gd and FLAIR are used in a deep learning algorithm to delineate the brain and abnormality including enhancing tumor, necrosis, and peripheral edema. Normal tissue segmentations are performed using the statistical parametric mapping (SPM) segmentation routine. The segmentations and preprocessed images are all utilized in the edema mathematical model. The resulting edema map is then reformatted as a DICOM and output for further use by clinicians and clinical imaging systems. Our future goals include integrating our pipeline in a clinical workstation and sending final maps to our hospital’s picture archiving and communication system (PACS) to allow for deployment of these models for patient care in a clinically efficient timeline.

Abstract Rationale Follow-up of patients with emphysema treated with endobronchial valves is limited to 3–12 months after treatment in prior reports. To date, no comparative data exist between treatment and control subjects with a longer follow-up. Objectives To assess the durability of the Spiration Valve System (SVS) in patients with severe heterogeneous emphysema over a 24-month period. Methods EMPROVE, a multicenter randomized controlled trial, presents a rigorous comparison between treatment and control groups for up to 24 months. Lung function, respiratory symptoms, and quality-of-life (QOL) measures were assessed. Results A significant improvement in forced expiratory volume in 1 second was maintained at 24 months in the SVS treatment group versus the control group. Similarly, significant improvements were maintained in several QOL measures, including the St. George’s Respiratory Questionnaire and the COPD Assessment Test. Patients in the SVS treatment group experienced significantly less dyspnea than those in the control group, as indicated by the modified Medical Research Council dyspnea scale score. Adverse events at 24 months did not significantly differ between the SVS treatment and control groups. Acute chronic obstructive pulmonary disease exacerbation rates in the SVS treatment and control groups were 13.7% (14 of 102) and 15.6% (7 of 45), respectively. Pneumothorax rates in the SVS treatment and control groups were 1.0% (1 of 102) and 0.0% (0 of 45), respectively. Conclusions SVS treatment resulted in statistically significant and clinically meaningful durable improvements in lung function, respiratory symptoms, and QOL, as well as a statistically significant reduction in dyspnea, for at least 24 months while maintaining an acceptable safety profile. Clinical trial registered with www.clinicaltrials.gov (NCT01812447).

Abstract INTRODUCTION Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early‐onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. METHODS We analyzed MRI scans to define the sporadic EOAD‐signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early‐onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. RESULTS The EOAD‐signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD‐signature atrophy was associated with the severity of cognitive impairment. DISCUSSION The EOAD‐signature atrophy is a reliable and clinically valid biomarker of AD‐related neurodegeneration that could be used in clinical trials for EOAD. Highlights We developed an early‐onset Alzheimer's disease (EOAD)–signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD‐signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.

ImportanceIncreased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors.ObjectiveTo estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes.Design, Setting, and ParticipantsThis cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers—the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer’s Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019).Main Outcome and MeasuresThe main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH.ResultsData from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = −3.1 [P = .002]; ADNI: t = −5.6 [P < .001]; HABS: t = −2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001).Conclusions and RelevanceThe findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.

Significance Statement Accurate diagnosis of a patient's underlying cause of CKD can influence management and ultimately overall health. The single-arm, interventional, prospective Renasight Clinical Application, Review, and Evaluation study assessed the utility of genetic testing with a 385 gene kidney disease panel on the diagnosis and management of 1623 patients with CKD. Among 20.8% of patients who had positive genetic findings, half resulted in a new or reclassified diagnosis. In addition, a change in management because of genetic testing was reported for 90.7% of patients with positive findings, including treatment changes in 32.9%. These findings demonstrate that genetic testing has a significant effect on both CKD diagnosis and management. Background Genetic testing in CKD has recently been shown to have diagnostic utility with many predicted implications for clinical management, but its effect on management has not been prospectively evaluated. Methods Renasight Clinical Application, Review, and Evaluation RenaCARE (ClinicalTrials.gov NCT05846113) is a single-arm, interventional, prospective, multicenter study that evaluated the utility of genetic testing with a broad, 385 gene panel (the Renasight TM test) on the diagnosis and management of adult patients with CKD recruited from 31 US-based community and academic medical centers. Patient medical history and clinical CKD diagnosis were collected at enrollment. Physician responses to questionnaires regarding patient disease categorization and management were collected before genetic testing and 1 month after the return of test results. Changes in CKD diagnosis and management after genetic testing were assessed. Results Of 1623 patients with CKD in 13 predefined clinical disease categories (ages, 18–96; median, 55 years), 20.8% ( n =338) had positive genetic findings spanning 54 genes. Positive genetic findings provided a new diagnosis or reclassified a prior diagnosis in 48.8% of those patients. Physicians reported that genetic results altered the management of 90.7% of patients with a positive genetic finding, including changes in treatment plan, which were reported in 32.9% of these patients. Conclusions Genetic testing with a CKD-focused 385 gene panel substantially refined clinical diagnoses and had widespread implications for clinical management, including appropriate treatment strategies. These data support the utility of broader integration of panels of genetic tests into the clinical care paradigm for patients with CKD. Clinical Trial registry name and registration number ClinicalTrials.gov, NCT05846113.