Citations

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer. Standard of care surgery employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. Recent multi-omics analyses identified predominant neuronal and immune signatures within NE tumor. Here, we apply a graph network to integrate regionally heterogeneous data from a unique dataset of spatially-localized CE and NE biopsies from a large cohort of patients with HGG. METHODS RNA-sequencing of 1159 differentially expressed genes across 159 IDH-wt biopsies (74 patients) from NE/CE regions were represented as nodes in a Neo4j network. Candidate genes for network random walks occurred exclusively within NE communities detected by modularity optimization and localized by cell-type using single-cell RNA-sequencing (scRNAseq). CSF2RA and RBFOX1 were selected as sources (walk length = 5; 10,000 walks/source node). Frequently occurring genes (one-tailed test) in walks were ranked by correlation with CSF2RA/RBFOX1 expression in NE samples and analyzed by gene set enrichment analysis for biological processes (clusterProfiler). Random walk genes were visualized (DoHeatmap) against composite NE/CE scRNAseq profiles. RESULTS CSF2RA and RBFOX1 were upregulated in NE samples (logFC = 0.789, 1.85). Upregulation of CSF2RA, an immune receptor, in the Glycolytic/Plurimetabolic and RBFOX1, a neurodevelopmental regulator, in the Neuronal glioma subtypes were observed in NE samples (ANOVA p < 0.05). CSF2RA walk genes (n=46) enriched metabolic/proliferative signatures, while RBFOX1 walk genes (n=201) showed neurodevelopmental upregulation. scRNAseq localized CSF2RA to monocytes/macrophages while RBFOX1 was expressed in distinct astrocyte/neuroepithelial populations. CONCLUSION CSF2RA and RBFOX1’s gene networks modulate immune and neurodevelopmental processes influencing glioma progression in NE tumor. Understanding the region-specific roles of these networks may offer strategies for targeting cells in the invasive glioma rim.

Importance The prognosis for patients with glioblastoma is poor following standard therapy with surgical resection, radiation, temozolomide, and tumor-treating fields. Objectives To evaluate the combination of veliparib and temozolomide in glioblastoma based on preclinical data demonstrating significant chemosensitizing effects of the polyadenosine diphosphate-ribose polymerase 1/2 inhibitor veliparib when combined with temozolomide. Design, Setting, and Participants Patients with newly diagnosed glioblastoma with MGMT promoter hypermethylation who had completed concomitant radiation and temozolomide were enrolled between December 15, 2014, and December 15, 2018, in this Alliance for Clinical Trials in Oncology trial. The data for this analysis were locked on April 21, 2023. Interventions Patients were randomized and treated with standard adjuvant temozolomide (150-200 mg/m 2 orally, days 1-5) combined with either placebo or veliparib (40 mg orally, twice daily, days 1-7) for 6 cycles. Main Outcomes and Measures The primary end point for the phase 3 portion of the trial was overall survival (OS). Results There were 322 patients randomized during the phase 2 accrual period and an additional 125 patients randomized to complete the phase 3 accrual, for a total of 447 patients in the final phase 3 analysis. The median (range) age for patients was 60 (20-85) years and 190 patients (42.5%) were female. The median OS was 24.8 months (90% CI, 22.6-27.7) for the placebo arm and 28.1 months (90% CI, 24.3-33.3) for the veliparib arm ( P = .17). The difference in survival did not meet the prespecified efficacy end point. However, there was a separation of the survival curves that favored the veliparib arm over 24 to 48 months of follow-up. The experimental combination was well tolerated with an acceptable elevation in grade 3 or 4 hematologic toxic effects. Conclusions and Relevance This trial found that adding veliparib to adjuvant temozolomide did not significantly extend OS in patients with newly diagnosed, MGMT -hypermethylated glioblastoma. Trial Registration ClinicalTrials.gov Identifier: NCT02152982

AbstractObjectivePrion disease classically presents with rapidly progressive dementia, leading to death within months of diagnosis. Advances in diagnostic testing have improved recognition of patients with atypical presentations and protracted disease courses, raising key questions surrounding the relationship between patterns of neurodegeneration and survival. We assessed the contribution of fluorodeoxyglucose (FDG‐PET) imaging for this purpose.MethodsFDG‐PET were performed in 40 clinic patients with prion disease. FDG‐PET images were projected onto latent factors generated in an external dataset to yield patient‐specific eigenvalues. Eigenvalues were input into a clustering algorithm to generate data‐driven clusters, which were compared by survival time.ResultsMedian age at FDG‐PET was 65.3 years (range 23–85). Median time from FDG‐PET to death was 3.7 months (range 0.3–19.0). Four data‐driven clusters were generated, termed “Neocortical” (n = 7), “Transitional” (n = 12), “Temporo‐parietal” (n = 13), and “Deep nuclei” (n = 6). Deep nuclei and transitional clusters had a shorter survival time than the neocortical cluster. Subsequent analyses suggested that this difference was driven by greater hypometabolism of deep nuclei relative to neocortical areas. FDG‐PET‐patterns were not associated with demographic (age and sex) or clinical (CSF total‐tau, 14‐3‐3) variables.InterpretationGreater hypometabolism within deep nuclei relative to neocortical areas associated with more rapid decline in patients with prion disease and vice versa. FDG‐PET informs large‐scale network physiology and may inform the relationship between spreading pathology and survival in patients with prion disease. Future studies should consider whether FDG‐PET may enrich multimodal prion disease prognostication models.

Abstract BACKGROUND High-grade glioma (HGG) is the most common and aggressive form of primary brain cancer with poor prognosis. Standard of care surgery employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, typically leaving behind a non-enhancing (NE) invasive rim of tumor which invariably contributes to recurrence. Recent multi-omics analyses identified predominant neuronal and immune signatures within NE tumor, which could provide key insights for combatting recurrence. We present the novel application of graph network analyses to integrate multi-omic data from a unique dataset of multi-regional spatially-localized CE and NE biopsy samples from a large cohort of patients with HGG. MATERIAL AND METHODS 159 IDH-wildtype tumor biopsy samples from NE and CE regions of 74 glioma patients were profiled by RNA sequencing. Differentially expressed genes (DEGs) across NE and CE samples (n = 1159) were determined using the EdgeR package, and represented as nodes within a Neo4j graph network. Samples were hierarchically clustered for each gene (method = median), and edges were generated between gene nodes sharing ≥ 1 sample. Modularity optimization was used to identify communities of densely connected nodes. Genes occurring exclusively within NE communities were localized by cell type using external single-cell RNA sequencing datasets and identified as source node candidates for NE network random walks. These were performed with CSF2RA nodes as source (walk length = 5; 10,000 walks/source node). Random walks traversing samples with significantly elevated CSF2RA expression (one-tailed test) were kept for analysis. Genes occurring at high frequency (one-tailed test) within random walks were ranked by correlation with CSF2RA expression in NE samples and analyzed by gene set enrichment analysis (GSEA) for biological processes using clusterProfiler. RESULTS CSF2RA, an immune signaling receptor gene, was upregulated (logFC = 0.789) in NE samples and uniquely occurred within NE communities. We show its differential expression within the immune-enriched Glycolytic/Plurimetabolic (GPM) glioma subtype and confirm its enrichment in myeloid populations across single cell HGG datasets. NE network random walks identified 46 DEGs associated with CSF2RA. GSEA revealed upregulation of proliferative and metabolic signatures and downregulation of signaling pathways within CSF2RA’s network. CONCLUSION CSF2RA’s genetic network in NE tumor represents a potential mechanism for immunologic facilitation of glioma progression within this region. Given the expression of CSF2 ligand by glioma cells, which plays a crucial role in recruiting myeloid cells during peripheral cancer progression, modulating the expression of key genes in the CSF2RA network may be a promising approach for perturbing the crosstalk signals between immune and tumor cells in the NE region.

Abstract Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer’s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10–20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer’s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer’s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59–0.91) and Alzheimer’s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer’s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.

Abstract Background Few antiviral therapies have been studied in patients with coronavirus disease 2019 (COVID-19) and kidney impairment. Herein, the efficacy, safety, and pharmacokinetics of remdesivir, its metabolites, and sulfobutylether-β-cyclodextrin excipient were evaluated in hospitalized patients with COVID-19 and severe kidney impairment. Methods In REDPINE, a phase 3, randomized, double-blind, placebo-controlled study, participants aged ≥12 years hospitalized for COVID-19 pneumonia with acute kidney injury, chronic kidney disease, or kidney failure were randomized 2:1 to receive intravenous remdesivir (200 mg on day 1; 100 mg daily up to day 5) or placebo (enrollment from March 2021 to March 2022). The primary efficacy end point was the composite of the all-cause mortality rate or invasive mechanical ventilation rate through day 29. Safety was evaluated through day 60. Results Although enrollment concluded early, 243 participants were enrolled and treated (remdesivir, n = 163; placebo, n = 80). At baseline, 90 participants (37.0%) had acute kidney injury (remdesivir, n = 60; placebo, n = 30), 64 (26.3%) had chronic kidney disease (remdesivir, n = 44; placebo, n = 20), and 89 (36.6%) had kidney failure (remdesivir, n = 59; placebo, n = 30); and 31 (12.8%) were vaccinated against COVID-19. Composite all-cause mortality or invasive mechanical ventilation rates through day 29 were 29.4% and 32.5% in the remdesivir and placebo group, respectively (P = .61). Treatment-emergent adverse events were reported in 80.4% for remdesivir versus 77.5% for placebo, and serious adverse events in 50.3% versus 50.0%, respectively. Pharmacokinetic plasma exposure to remdesivir was not affected by kidney function. Conclusions Although the study was underpowered, no significant difference in efficacy was observed between treatment groups. REDPINE demonstrated that remdesivir is safe in patients with COVID-19 and severe kidney impairment. Clinical Trials Registration EudraCT 2020-005416-22; Clinical Trials.gov NCT04745351.

AbstractIntroductionSurgical techniques for sellar reconstruction include no reconstruction, use of synthetic materials, autologous grafts, and/or vascularized flaps. The aim of this study was to conduct a multi‐center study comparing the efficacy and postoperative morbidity associated with different sellar reconstruction techniques.MethodsA retrospective chart review of patients who underwent endoscopic transsphenoidal surgery for pituitary tumors from five participating sites between January 2021 and March 2023 was performed. The variables included demographics, tumor characteristics, reconstruction technique, postoperative cerebrospinal fluid leak (CSF) leak, and 22‐item Sino‐Nasal Outcome Test (SNOT‐22) scores. Comparisons of postoperative complications, SNOT‐22 scores, and duration of surgery by type of onlay reconstruction were evaluated using Fisher's exact test, analysis of variance, and Kruskal‒Wallis test.ResultsFive hundred and one patients were identified. The median tumor size was 2.1 cm, and 64% were non‐functioning. Intraoperative CSF leak was identified in 38% of patients. A total of 89% of patients underwent onlay reconstruction: 49% were reconstructed with mucosal grafts, 35% with nasoseptal flaps, and 5% with other onlay techniques. Nasoseptal flaps were utilized more frequently in the setting of giant pituitary adenomas (>3 cm), medial cavernous sinus wall resection, and high‐flow intraoperative CSF leaks. Cases who utilized mucosal grafts had an overall shorter operating time (median: 183 min vs. 240 min; p < 0.001). Five postoperative CSF leaks were identified, and therefore, statistical analysis could not be performed for this complication.ConclusionThe effectiveness and morbidity of different sellar reconstruction techniques are comparable. Vascularized flaps were utilized more frequently in the setting of larger tumors and high‐flow intraoperative CSF leaks.

Abstract Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.