Citations

Abstract BACKGROUND Diffuse hemispheric glioma (DHG) is an uncommon infiltrating glioma, defined in the World Health Organization 2021 guidelines by a mutation in the H3F3A gene at codon 34 (H3.G34). DHG primarily affects children and young adults. Due to its rarity, there is paucity of knowledge regarding factors affecting overall survival (OS). We retrospectively assessed our institutional experience and present a review of the literature. METHODS We searched our institution’s pathology records to identify six cases of DHG. Demographic, clinical, diagnostic, treatment, and outcome data were recorded. We next searched for published articles regarding DHG cases via PubMed through May 2023. Four-hundred-fifty-four published individual cases of H3.G34 glioma were identified, 204 of which were hemispheric/supratentorial tumors with OS data reported, providing a combined 210 cases for analysis. 132 of these cases included MGMT promoter methylation status. 111 included extent of surgical resection. Kaplan-Meier and Cox regression survival analysis was performed. RESULTS Age range for DHG patients was 7-66 years (median 17). Median OS was 18.5 months (95% CI, 17-25). Greater extent of surgical resection and presence of MGMT promoter methylation were associated with better prognosis. Median OS for biopsy alone was 12 months (95% CI, 10-NA), subtotal resection was 16 months (95% CI, 14-23), and gross total resection was 27 months (95% CI 23-NA) – log-rank p< 0.001. OS for MGMT promoter unmethylated was 14 months (95% CI 12-22), MGMT promoter methylated was 24 months (95% CI, 17-30) – log-rank p=0.028. Contrary to previous reports, patient age was not found to impact prognosis (using 18, 21, and 40 years as cutoffs or as a continuous variable with Cox regression). CONCLUSION Extent of surgical resection and MGMT promoter methylation status are important prognostic factors for DHG. Unlike many primary brain tumors, survival does not appear to be significantly impacted by age.

Abstract Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Misdiagnosis of brain lesions may expose patients to unnecessary anxiety, surgery, or radiotherapy. Noninvasive methods to help with accurate diagnosis of tumor and non-tumor etiologies are needed. tMS subjects were identified by the Mayo Clinic Center for Multiple Sclerosis and Autoimmune Neurology (CMSAN). GBM subjects were matched to tMS by age at diagnosis, index MRI date, and sex. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. To identify potential experimental design bias, MRI technology and clinical characteristics were compared across groups. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 600 epochs, batch size 16, learning rate 10-3, cross entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential epoch cross entropy loss < 0.02. Five-fold cross validation (CV) was utilized to obtain an unbiased estimate of predictive performance (overfitting) and sensitivity of the 3D-DenseNet121 algorithm was evaluated. 224 subjects (114 GBM, 110 tMS) were analyzed: 40% male, median age 46. After one round of CV, the average unbiased estimate of sensitivity and specificity for predicting GBM from the final classification model was 0.86 and 0.73, respectively. To assess the reproducibility of the deep learning algorithm (3D-DenseNet121), the exact same CV folds were rerun resulting in average sensitivity and specificity of 0.82 and 0.75, respectively. Obtaining unbiased estimates of model performance using internal validation is critical before evaluating a final model on an external dataset. Understanding sensitivity of a deep learning algorithm is important for model validity. Differentiation of GBM and tMS appears reasonable with MRI; external validation is in progress.

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 BACKGROUND The diagnosis and management of patients with brain tumors currently uses WHO defined morphologic and molecular features. However, neuro-oncology practice in low resource settings relies on histomorphology. This study aims to reclassify glioma cases diagnosed in the Department of Anatomic and Molecular Pathology Lagos University Teaching Hospital using the 2021 WHO classification. METHODS Fifty-six cases of glioma diagnosed between 2013 to 2021 were evaluated. Morphologic diagnosis was reassessed. Five-micron sections were obtained for immunohistochemistry (IHC), and genetic testing (DNA and RNA); after manual macrodissection for tumor enrichment using 10μm sections. IHC for IDH1-R132H, ATRX, BRAFV600E, P53,Ki67, and H3K27M and OncoScan chromosomal microarray analysis were performed. NGS mutation/fusion analysis and EPIC methylation array profiling are in progress. RESULTS Of 56 cases evaluated, median age was 22 years (range=1-60 years); 71% were males. The initial morphologic diagnosis include 21 pilocytic astrocytoma, 11 glioblastoma, 9 ependymoma, 7 diffuse astrocytoma, 3 anaplastic astrocytoma, 2 high-grade gliomas, 1 oligodendroglioma, 1 diffuse midline glioma, and 1 pleomorphic xanthoastrocytoma (PXA). Histomorphologic re-evaluation revealed discordant diagnosis in 29% (16/56) of cases. Of the 56 cases, 73% (41/56) had usable DNA yield and 38% (21/56) had DNA yield of ≥ 750ng. Chromosomal microarray analysis was completed for 50% (28/56) cases. The revised 2021 WHO diagnosis were: 5 glioblastoma, IDH-wildtype, 5 pilocytic astrocytoma, 4 glioma NEC, 3 posterior fossa ependymoma, group B, 3 PXAs, 3 astrocytoma, IDH-mutant, 2 pediatric low-grade glioma, 2 supratentorial ependymoma, ZFTA fusion-positive, 1 posterior fossa ependymoma, group A. In 71% (20/28) of cases, diagnosis was revised using the 2021 WHO criteria. CONCLUSION Most cases had usable DNA yield. Overall, hsitologic re-evaluation and molecular testing based on the 2021 WHO classification guidelines refined the diagnosis in 48% (27/56) of cases.

Abstract INTRODUCTION Brain tumors constitute significant morbidity and mortality in sub-Saharan Africa (SSA). Improvements in neuro-oncology practice and research have seen increased diagnosis of brain tumors. However, to our knowledge, there have been no previous projects on brain tumor genetics in SSA. To understand the feasibility of developing brain tumor biobanking and registries, this project aims to start by mapping out the current neuro-oncology capacity in terms of workforce and available services. METHODOLOGY We will conduct a descriptive questionnaire-based online survey among a cross section of neuro-oncology practitioners spread across SSA countries. The dissemination of the survey questionnaire will be facilitated by the Society for Neuro-oncology in SSA (SNOSSA). The Google designed questionnaire will capture information on the distribution, and number of available neuro-oncology care practitioners, capacity for brain tumor diagnosis and management, cancer registration, tumor biobanking, and research. CLINICAL IMPACT AND CONCLUSION This study will generate robust data from SSA, which can be used to improve future research and promote advocacy towards brain tumor diagnosis and management. This work will also serve as a pilot study for subsequent research that will focus on the development of brain tumor-specific registries and biorepositories in SSA.

Abstract BACKGROUND The diagnosis and management of patients with brain tumors currently uses WHO defined morphologic and molecular features. However, neuro-oncology practice in low resource settings relies solely on histomorphology. This study aims to reclassify glioma cases diagnosed in the Department of Anatomic and Molecular Pathology Lagos University Teaching Hospital using the WHO 2021 classification. METHODS Fifty-six cases of glioma diagnosed between 2013 to 2021 were evaluated. Morphologic diagnosis was reassessed. Five-micron sections were obtained for immunohistochemistry (IHC), and genetic testing (DNA and RNA) after manual macrodissection for tumor enrichment used 10-µm sections. IHC for IDH1-R132H, ATRX, BRAF-V600E, p53, Ki67 and H3-K27M and Oncoscan chromosomal microarray analysis were performed. NGS mutation/fusion analysis and EPIC methylation array profiling are in progress. RESULTS Of the 56 cases evaluated, median age was 22 years (range, 1-60); 71% were male. The initial morphologic diagnoses included 21 pilocytic astrocytoma, 11 glioblastoma, 9 ependymoma, 7 diffuse astrocytoma, 3 anaplastic astrocytoma, 2 high-grade gliomas, 1 oligodendroglioma, 1 diffuse midline glioma, and 1 pleomorphic xanthoastrocytoma (PXA). Histomorphologic re-evaluation revealed discordant diagnosis in 29% (16/56) of cases. Of the 56 cases, 73% (41/56) had usable DNA yield and 38% (21/56) had DNA yield of ≥750ng. Chromosomal microarray analysis was completed for 50% (28/56) cases. The revised WHO 2021 diagnosis was: 5 glioblastoma IDH-wildtype, 5 pilocytic astrocytoma, 4 glioma NEC, 3 posterior fossa ependymoma, group B, 3 PXA, 3 astrocytoma IDH-mutant, 2 pediatric low-grade gliomas, 2 supratentorial ependymoma, ZFTA fusion-positive, 1 posterior fossa ependymoma, group A. In 71% (20/28) of cases with molecular testing, diagnosis was revised using WHO 2021 criteria. CONCLUSION Most cases had usable DNA. Overall, histologic re-evaluation and molecular based on the WHO 2021 criteria refined the diagnosis in 48% (27/56) of cases.

e24089 Background: Little is known about risk and protective factors for dementia in cancer survivors. With increasing longevity due to improved cancer therapy, it’s prudent to study factors contributing to cognitive impairment in this patient population. Methods: We performed multivariable cox regression analyses to assess established risk factors for all-cause dementia (age, education, APOE4-status, social and physical activity engagement) in breast and prostate cancer survivors. Patients 50+ years old with pathology-confirmed cancer diagnosis at Mayo Clinic were identified from a longitudinal, population-based cohort – Mayo Clinic Study of Aging. Analyses were controlled for age, APOE4-status, education, and cancer treatment. Results: We included a total of 209 patients with breast cancer (mean age 75.4 years, 24% APOE4-positive, median follow-up 6.8 years) and 352 with prostate cancer (mean age 77 years, 26% APOE4-positive, median follow-up 6.4 years). 31 patients with breast cancer (15%) and 66 patients with prostate cancer (20%) were diagnosed with dementia at last follow-up. Older age was associated with increased risk for dementia (5-year increase – breast: hazard ratio (HR) 1.9 [95%CI 1.3-2.7, p = 0.001]; prostate: HR 2.4 [95%CI 1.8-3.2, p < 0.0001]). APOE4-positivity was associated with increased dementia risk in breast (HR 3.6 [95%CI 1.6-7.8, p < 0.002]), but not prostate cancer (HR 1.2 [95%CI 0.7-2.0, p = 0.5]). More education was associated with lower dementia risk in prostate cancer (education year: HR 0.91 [95%CI 0.84-0.98, p < 0.02]; education years > 12: HR 0.26 [95%CI 0.1-0.6, p < 0.001]). Physical activity engagement in mid-life was associated with lower dementia risk (prostate: HR 0.56 [95%CI 0.4-0.8, p < 0.005]; breast: HR 0.48 [95%CI 0.3-0.9, p < 0.02]). Conclusions: In this longitudinal, population-based study we found that some risk and protective factors for dementia in cancer survivors vary by cancer type. While advanced age is an expected dementia risk factor, there were mixed results for APOE4-positivity (increased risk only in breast cancer) and education (only protective in prostate cancer). Our results support further study into factors influencing dementia risk by cancer type in hopes of identifying preventative strategies.