Decker, Paul A.

Abstract INTRODUCTION Prior to 2021, (classic) glioblastoma (GBM) required absence of IDH mutation and histologic features of hypercellularity, nuclear atypia, microvascular proliferation (MVP), and necrosis. Absence of histologic features can be a result of undersampling, with molecular, radiologic, and clinical features consistent with GBM. Accordingly, WHO 2021 defined molecular glioblastoma as diffuse glioma IDHwt, H3wt, with one or more of MVP, necrosis, TERT promoter mutation, EGFR gene amplification (EGFRamp), and/or +7/−10. We hypothesized that there is clinical and radiographic heterogeneity among tumors defined as molecular GBM. METHODS We identified 121 patients that met the criteria for molecular GBM. 121 Classic GBM, defined as IDHwt with hypercellularity, MVP or necrosis, were matched to molecular GBM cases by age, sex, and diagnosis date. All cases were reviewed for relevant histologic features and molecular features. Examined clinical variables included: first symptom, time from first symptom to date of pathological diagnosis, date of first MRI, tumor location, radiographic features of enhancement, diffusion or mass effect, time to progression and overall survival. Clinical, radiologic, and molecular features were compared between classic and molecular GBM. RESULTS Currently,160 cases have been reviewed. Preliminary results show that there is a longer time from first symptom to diagnosis for molecular GBM. Molecular GBM is significantly more likely to be discovered coincidentally, and without enhancement or radiographic necrosis. Classic GBM is significantly more likely to present with focal neurologic deficit as first symptom, regional mass effect on index imaging and have monosomy 10. CONCLUSION While pathologic undersampling accounts for a significant proportion of molecular GBM, our data suggest that there are significant clinical, radiographic, and outcome differences between classical and molecular GBM. Final analysis of the entire cohort will help better define this clinical spectrum.

Abstract There have been several attempts to predict the MGMT promoter methylation status of gliomas using deep learning (DL), with mixed results. These studies did not account for IDH mutation status and did not utilize a uniform methylation assay, introducing potential confounders in the experimental design. We assessed the performance of DL applied to brain MRI to predict the MGMT promoter methylation status of IDH-wildtype gliomas tested with a standardized assay. We included 539 IDH-wildtype gliomas tested with real-time methylation-specific PCR from 2017-2022; 205 (38%) were MGMT promoter methylated. The assay targets 8 downstream MGMT CpG sites using ACTB as control; methylation status is determined based on the difference of PCR crossing-point values: methylated if difference < 5, unmethylated if difference > 7, indeterminate otherwise. We used post-contrast T1 and T2 MRIs as inputs for our models trained using stratified 5-fold cross-validation, 400 epochs, batch size of 16, a learning rate scheduler starting at 10-3, weighted binary cross-entropy loss, and AdamW optimizer. We repeated the experiments using two 3D DL architectures (DenseNet121 and EfficientNetB0) and two image preprocessing pipelines: the BraTS protocol and one that uses padding instead of registration to an MRI atlas, keeping constant other experimental variables. For Densenet121, the best cross-validation AUROCC and F1-score (median, median absolute deviation) were 0.67 (0.06), 0.59 (0.02) using the BraTS protocol, and 0.67 (0.06), 0.58 (0.01) using padding. For EfficientNetB0, were 0.67 (0.02), 0.66 (0.01) using the BraTS protocol and 0.67 (0.01), 0.68 (0.01) using padding. Our findings suggest that it is unlikely that DL models trained on brain MRI can predict the MGMT promoter methylation of IDH-wildtype gliomas. Importantly, our study is the first to focus on an IDH-wildtype adult diffuse glioma cohort tested with a single, standardized MGMT methylation assay, eliminating the variability introduced by different assays and IDH status.

Abstract BACKGROUND D-2-hydroxyglutarate (D-2-HG) is a well-established oncometabolite of isocitrate dehydrogenase (IDH) mutant gliomas. While prior studies have demonstrated that D-2-HG is elevated in the cerebrospinal fluid (CSF) of patients with IDH-mutant gliomas, no study has evaluated the ability of CSF D-2-HG to quantitatively monitor tumor burden and therapeutic response. Toward this goal, we are routinely obtaining CSF samples from patients prior to, during, and after treatment via intra-operative collection and CSF-access devices. MATERIAL AND METHODS Patients were enrolled into our Ommaya reservoir study (NCT04692337), which enables longitudinal access to CSF. The catheter for the implanted reservoir was placed within the surgical resection cavity following a standard-of-care resection. Intra-operatively collected CSF samples were also biobanked through Mayo Clinic’s Neuro-Oncology biorepository. CSF samples were also acquired via clinically indicated ventriculoperitoneal shunts (VP shunts) or external ventricular drains via our ongoing CSF biomarkers study (NCT04692324). D-and-L-2-HG were assayed from each CSF sample via gas chromatography-mass spectrometry (GC-MS) at Mayo Clinic’s Biochemical Genetics Laboratory. RESULTS CSF was collected prior to and after resection in patients via Ommaya reservoirs or VP shunts, all of whom underwent near gross total resections (>95% of tumor volume). D-2-HG and the D/L ratio fell on average by 57.9% (range: 15.1%-98.6%) and 68.8% (range: 49.3%-98.9%), respectively, following resection. In one patient with a largely intraventricular grade 4 IDH-mutant astrocytoma, D-and-L-2-HG fell by 99.1% and 99.3% by post-operative day 2, consistent with the impact of ventricular contact on baseline CSF D-2-HG levels. In patients for whom CSF was collected throughout chemoradiation, with or without immunotherapy, D-2-HG and the D/L ratio continued to decrease over time, despite concern for pseudoprogression versus progression on imaging. CONCLUSION Our early experience with this strategy demonstrates the potential for intracranial CSF D-2-HG as a monitoring biomarker for IDH-mutant gliomas. Work is ongoing to evaluate the reproducibility of these findings in a larger cohort of patients with low or high-grade gliomas, in addition to the impact of tumor recurrence on 2-HG. Future studies will evaluate the impact of IDH inhibitors and other experimental therapies on CSF 2-HG levels for pharmacodynamic monitoring of treatment response.

AbstractObjectiveTo describe demographic, clinical, and radiographic features of tumefactive demyelination (TD) and identify factors associated with severe attacks and poor outcomes.MethodsRetrospective review of TD cases seen at Mayo Clinic, 1990–2021.ResultsOf 257 patients with TD, 183/257 (71%) fulfilled the 2017 multiple sclerosis (MS) McDonald criteria at the last follow‐up, 12/257 (5%) had myelin oligodendrocyte glycoprotein antibody‐associated disease (MOGAD), 0 had aquaporin‐4‐IgG seropositive neuromyelitis optic spectrum disorders (AQP4+ NMOSD), and 62/257 (24%) were cryptogenic. Onset before age 18 was present in 18/257 (7%). Female to male ratio was 1.3:1. Cerebrospinal fluid oligoclonal (CSF) bands were present in 95/153 (62%). TD was the first demyelinating attack in 176/257 (69%). At presentation, 59/126 (47%) fulfilled Barkhof criteria for dissemination in space, 59/100 (59%) had apparent diffusion coefficient (ADC) restriction, and 57/126 (45%) had mass effect. Despite aggressive clinical presentation at onset, 181/257 (70%) of patients remained fully ambulatory (Expanded Disability Status Scale [EDSS] ≤4) after a 3.0‐year median follow‐up duration. Severe initial attack‐related disability (EDSS ≥4) was more common in patients with motor symptoms (81/143 vs. 35/106, p < 0.0001), encephalopathy (20/143 vs. 2/106, p < 0.0001) and ADC restriction on initial MRI (42/63 vs. 15/33, p = 0.04). Poor long‐term outcome (EDSS ≥4) was more common in patients with older onset age (41.9 ± 15 vs. 36.8 ± 15.6, p = 0.02) and motor symptoms at onset (49/76 vs. 66/171, p < 0.0001).InterpretationMost TD patients should be considered part of the MS spectrum after excluding MOGAD and NMOSD. Motor symptoms and older age at presentation portend a poor outcome.

Abstract Background Although the epidermal growth factor receptor (EGFR) is a frequent oncogenic driver in glioblastoma (GBM), efforts to therapeutically target this protein have been largely unsuccessful. The present preclinical study evaluated the novel EGFR inhibitor WSD-0922. Methods We employed flank and orthotopic patient-derived xenograft models to characterize WSD-0922 and compare its efficacy to erlotinib, a potent EGFR inhibitor that failed to provide benefit for GBM patients. We performed long-term survival studies and collected short-term tumor, plasma, and whole-brain samples from mice treated with each drug. We utilized mass spectrometry to measure drug concentrations and spatial distribution and to assess the impact of each drug on receptor activity and cellular signaling networks. Results WSD-0922 inhibited EGFR signaling as effectively as erlotinib in in vitro and in vivo models. While WSD-0922 was more CNS penetrant than erlotinib in terms of total concentration, comparable concentrations of both drugs were measured at the tumor site in orthotopic models, and the concentration of free WSD-0922 in the brain was significantly less than the concentration of free erlotinib. WSD-0922 treatment provided a clear survival advantage compared to erlotinib in the GBM39 model, with marked suppression of tumor growth and most mice surviving until the end of the study. WSD-0922 treatment preferentially inhibited phosphorylation of several proteins, including those associated with EGFR inhibitor resistance and cell metabolism. Conclusions WSD-0922 is a highly potent inhibitor of EGFR in GBM, and warrants further evaluation in clinical studies.

Background Larger within‐patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record–based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD‐free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06–1.37]). Results were similar for low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol. Conclusions In a large electronic health record–based population cohort, high variability in total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Abstract Context Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (&lt;7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P &lt; .0001). Conclusion Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Abstract Missing MRI sequences are common in radiology due to differences in protocols, time constraints, and image artifacts. Missing sequences are a nuisance for subsequent algorithms, such as automated segmentation algorithms requiring multiple MRI sequences (e.g., HD-GLIO, which requires T2, T1, post-contrast T1 (T1c), and FLAIR). Accurate tumor segmentation is a fundamental step in extracting radiomics analysis to use, for example, to predict the IDH status of glioma or their overall survival. We validated a conditional (pix2pix) and super-resolution GAN (SOUP-GAN) for imputing MRI sequences on 285 adult glioma with complete MRI sequences available. We simulated missing T1 sequences and subsequently imputed them from T1c using pix2pix and from sagittal T1 using SOUP-GAN. Using HD-GLIO, we obtained the true segmentations from the complete MRI sequences. HD-GLIO was also used after replacing the original T1 sequence with the one imputed with pix2pix and SOUP-GAN. Thus, there were three sets of segmentations for each subject. We compared T2 and T1c radiomics features extracted with Pyradiomics using segmentations obtained with complete data with features extracted from segmentation obtained with the imputed T1 sequences using intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Median (interquartile range) ICC for shape features extracted from T2 and T1c were 0.82(0.76-0.92) using pix2pix and 0.89(0.86-0.94) using SOUP-GAN. Median ICC for first-order-statistics features were 0.90(0.83-0.93) for T2 and 0.90(0.88-0.92) for T1c using pix2pix, and 0.61(0.22-0.82) for T2 and 0.42(0.34-0.57) for T1c using SOUP-GAN. Median ICC for texture features were 0.86(0.82-0.87) for T2 and 0.87(0.79-0.90) for T1c using pix2pix, and 0.69(0.47-0.81) for T2 and 0.76(0.65-0.83) for T1c using SOUP-GAN. We show that T2 and T1c shape features (e.g., volumetric analysis) can be adequately extracted from segmentations obtained with an algorithm requiring multiple MRI sequences using T1 sequences imputed with GANs; however, the effect of imputation differed for first-order-statistics and texture features.