Burns, Terry C.

Abstract Background Glioblastoma IDH-wildtype (GBM) may be diagnosed histologically (hGBM) or based on molecular features when microvascular proliferation and histologic necrosis are absent (mGBM). Clinical and radiographic distinction between hGBM and mGBM remains unclear. Methods 101 hGBM cases were matched to 109 mGBM cases by age, sex, year of diagnosis, and TERT promoter mutation (TERTp) status. Clinical outcome, radiographic, and molecular features were compared across mGBM and hGBM. Results Median time from first MRI to diagnosis was longer for mGBM versus hGBM (34 vs 7 days, p < .001). mGBM more often presented with seizures (51% vs 26%, p < .001), gliomatosis pattern (12% vs 2%, p = .005), basal ganglia involvement (17% vs 0%, p < .001). Contrast enhancement appeared on MRI for 98% (96/98) of hGBM and 50% (54/107) of mGBM (p < .001). mGBM with non-enhancing MRI were rarely multifocal (2% vs 30%, p < .001). mGBM harbored fewer total copy number alterations (median 20 vs 16.5, p = .029), less often had EGFRamp (34% vs 48%, p = .051), isolated chromosome 10 loss (84% vs 96%, p = .013), and CDKN2A/B homozygous deletion (56% vs 77%, p = .005). Median overall survival was longer for mGBM (2.16 vs 1.44 years, p < .001), and for mGBM without MRI contrast enhancement versus hGBM with enhancement (3.11 vs 1.62 years, adjusted p = .003). mGBM without +7/−10 or without CDKN2A/B homozygous deletion had longer survival and time-to-progression versus hGBM with these alterations. Conclusion mGBM with MRI enhancement may represent undersampled or early/evolving hGBM. mGBM that are non-enhancing, present with seizures, prolonged time to diagnosis, absent CDKN2A/B alteration or +7/−10 may represent other IDH-wildtype entities.

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.

Abstract Polyamines are small molecules that play a critical role in tumor progression and in the regulation of immune responses within the tumor microenvironment. Elevated levels of polyamines in cerebrospinal fluid and tumor tissue have been reported in multiple studies and are shown to correlate with glioma grade. These findings suggest that the polyamine metabolic pathway may represent a therapeutic target in gliomas. Difluoromethylornithine (DFMO) is an irreversible inhibitor of ornithine decarboxylase (ODC), the rate-limiting enzyme in polyamine biosynthesis, expected to exert antitumor effects through polyamine depletion. Although the impact of DFMO against glioblastoma as a monotherapy appears limited, opportunities for synergy with other therapies remain understudied. In this study, we evaluated the therapeutic efficacy of DFMO using both in vitro and in vivo experiments with a murine glioma model (CT-2A) and patient-derived xenograft (PDX) cell lines. Among the models tested, CT-2A cells exhibited the highest expression of ODC1 and demonstrated strong sensitivity to DFMO in vitro. We compared two CT-2A variants: a commercially available line responsive to immunotherapy, and a widely used subline resistant to PD-1 blockade. While DFMO did not improve survival in mice implanted with the immunotherapy-sensitive commercial CT-2A line, it extended median survival by 8 days in mice bearing tumors derived from the immunotherapy-resistant subline. Transcriptomic analysis revealed that the DFMO-responsive, immunotherapy-resistant tumors exhibited lower baseline expression of immune-related genes, including those involved in interferon-β, interferon-γ, and IL-12 signaling pathways, compared to the immunotherapy-sensitive tumors. The latter showed markedly higher expression of STAT1 and interferon-γ and were enriched for immune-related Gene Ontology terms. These findings suggest that polyamine-targeted therapy may be appropriate for gliomas with an immunosuppressive, “cold” phenotype. Ongoing RNA sequencing analyses aim to further elucidate the immunomodulatory mechanisms of DFMO, and other agents comprising an adaptive toolkit to optimize and sustain anti-tumoral immunity.

Abstract BACKGROUND Many cancers demonstrate upregulated amino acid transport. In glioblastoma (GBM), evidence for upregulated amino acid flux includes high signal on amino acid PET imaging, increased expression of LAT1 (L-type amino acid transporter 1), and elevated extracellular amino acid levels via microdialysis. LAT1-targeted therapies have shown antitumor effects in certain cancer models by restricting essential amino acid supply and inhibiting the mTOR pathway, but their utility for GBM remains unknown. In this study, we evaluated impacts of a competitive LAT1 inhibitor, nanvuranlat, and a non-competitive inhibitor, JPH034 against glioblastoma. METHODS Murine (CT-2A, GL261) and human (GBM6, GBM39) GBM cell lines were tested under varying concentrations of nanvuranlat or JPH034. The competitive impacts of added essential amino acids, serum, and albumin were evaluated in vitro. Survival impacts were evaluated in an intracranial CT-2A glioma model. RESULTS Supplementing essential amino acids in vitro significantly decreased tumor cell sensitivity to nanvuranlat but did not change the IC50 of JPH034. Conversely, supplementing serum or albumin significantly reduced tumor cell sensitivity to JPH034 but did not change the IC50 of nanvuranlat. These findings were reproducible in all human and murine cell lines tested. In the intracranial mouse model, JPH034 treatment prolonged median overall survival from 16 days in the control group to 18.5 days, representing a 2.5-day extension (p = 0.060). CONCLUSION The antitumor effects of nanvuranlat and JPH034 on glioma cells in vitro, are influenced by concentrations of essential amino acids and albumin. Specifically, amino acids decreased impact of the competitive inhibitor (nanvuranlat) while albumin decreased impact of the non-competitive inhibitor (JPH034). These results suggest that the composition of the tumor microenvironment, which is impacted by BBB-disruption within GBM may differentially attenuate the therapeutic efficacy of certain drugs including LAT1 inhibitors.

Abstract Glioblastoma (GBM) diagnosis integrates morphology and tumor molecular alterations. Patients with classic morphology are designated as histologic GBM (hGBM). Patients without classic morphology harboring TERT promoter mutation (TERTp), or epidermal growth factor receptor amplification (EGFRamp), or combined gain of chromosome 7 and loss of 10 (+7/-10) designate molecular GBM (mGBM). We compared radiographic features, genomic alterations, and clinical outcome between hGBM and mGBM. 109 mGBM Mayo Clinic cases diagnosed between 1993-2021 were matched to 101 cGBM by age, sex, year of diagnosis, and TERT promoter (TERTp) status. Clinical and radiographic data abstraction was blinded. Progression-free and overall survival were compared across groups in the subset of patients who received standard of care. Contrast enhancement was present on index MRI (MRI at diagnosis) for 98% (96/98) hGBM and 50% (54/107) mGBM (p<0.0001) patients. mGBM tumors harbored fewer total copy number alterations (median of 16.5 vs 20, p=0.029) and were less likely to have CDKN2A/B homozygous deletion (56% vs 77%, p=0.005). Among the 87 mGBM and 82 hGBM receiving standard of care, median overall survival was longer for mGBM versus hGBM (2.24 vs 1.62 years, p=0.016). mGBM patients with +7/-10 (HR=2.60, p=0.035) or CDKN2A/B (HR=3.61, p=0.002) had poorer overall survival compared to mGBM patients without the corresponding alteration. On index MRI mGBM patients without contrast enhancement had better overall survival compared to hGBM (HR=0.50; p=0.003) whereas there was no survival difference between mGBM with enhancement and hGBM. mGBM with enhancement on index MRI likely represent undersampled, or early / evolving hGBM. mGBM patients with non-enhancing MRI, younger than 60, with long symptom profile including seizures, and some combination of non-anaplastic morphology, TERTp only, no homozygous deletion of CDKN2A/B, and low copy number complexity may represent other IDHwt glioma entities. Caution is advised in counseling, management, discussing prognosis, and inclusion in clinical trials.

Abstract BACKGROUND CNS ependymal tumors rarely demonstrate mixed subependymal and ependymal pathology. The clinical course of these tumors is difficult to predict, but prior studies have identified TERT mutations and loss of chromosome 6 as poor prognostic factors in these tumors when located in the posterior fossa. METHODS In this retrospective single-institution study, patients with a diagnosis of mixed subependymoma/ependymoma were identified and chart reviewed to describe demographics, pathological findings, treatment courses and clinical outcomes. RESULTS Eleven patients (median age 57, 2 female, 9 male) were identified. Ten tumors were in the posterior fossa and 1 in the cervical spine. At diagnosis, 2 underwent gross total resection, 8 underwent subtotal resection, and 1 was only biopsied. Radiotherapy was administered to 5 patients at initial diagnosis (54Gy in 30 fractions). Systemic therapy (temozolomide and bevacizumab) was administered at recurrence to 2 patients. Loss of chromosome 6 was present in 5 /11 patients and TERT mutations were present in 4/11 patients, with both present in 3/11 patients. Median progression-free survival (mPFS) was 90.1 months (95% CI: 90.1–NA), with 3 progression events observed at 22, 90, and 107 months. Median follow-up duration was 27.9 months. Receiving radiotherapy had a longer mPFS (98 vs 22 months, p=0.15), although non-significant. At the time of data cut-off none of the patients were deceased. CONCLUSIONS Mixed subependymoma/ependymoma is a rare diagnosis with varying clinical outcomes. Our case series demonstrated that the majority of patients did not exhibit the previously identified poor prognostic factors. Although non-significant due to limited case numbers, radiotherapy was suggestive of longer mPFS.

Abstract The 2021 WHO classification redefined glioblastoma IDH-wildtype (GBM), integrating histopathological and tumor molecular features. Diffuse astrocytic glioma lacking histologic features of necrosis and microvascular proliferation are classified as (molecular) GBM if the tumor is IDH- and H3-wildtype and has one or more additional tumor molecular alterations: TERT promoter mutation, EGFR amplification, or +7/-10. Due to the absence of histopathological features of classic GBM and presentation without enhancement on MRI in approximately 50% of cases, molecular GBM frequently resemble low-grade IDH-mutant gliomas. We hypothesized that MRI-based deep learning models could distinguish IDH-mutant glioma from molecular GBM. We retrospectively identified 97 individuals with molecular GBM, who were matched to 97 individuals with IDH-mutant glioma by age, sex, year of diagnosis, and enhancement on MRI. Of the 97 IDH-mutant gliomas, 51% were 1p/19q codeleted, 42% grade 2, 47% grade 3, and 10% grade 4. Preoperative postcontrast T1-weighted (T1C) and T2-weighted (T2) MRI sequences were used to train a 3D-DenseNet121 model. The model was validated on an independent prospective set of 150 IDH-mutant gliomas and 71 molecular GBM. The MRI-based deep learning model achieved an area under the receiver operator curve (AUC) of 0.75 (95% CI: 0.68-0.82) in the prospective validation set. Sex-stratified AUCs were 0.72 (95% CI: 0.59-0.85) for females and 0.76 (95% CI: 0.68-0.85) for males. Age stratified AUCs were 0.71 (95% CI: 0.60-0.83) for patients under 55 years of age and 0.64 (95% CI: 0.50-0.79) for patients older than 55. These results suggest that discriminative MRI features of IDH-mutant glioma and molecular GBM exist and can be identified from deep learning models. However, radiographic ambiguity exists and underscores the need for further optimization of the deep learning architecture or by integrating additional non-invasive modalities. For example, future work entails integrating germline variants into the model, including rs55705857, which is a causal variant for IDH-mutant glioma. With further validation and integration of multi-modal data, a noninvasive tool may support more personalized decision-making.

Abstract BACKGROUND High grade astrocytoma with piloid features (HGAP) is a newly recognized tumor with a distinct DNA methylation profile. Its clinical course, treatment response, and outcomes remain poorly understood. METHODS We conducted a retrospective cohort study across three Mayo Clinic sites including adults with a HGAP diagnosis confirmed by DNA methylation profiling. RESULTS 17 patients were included (median age 38 years, Q1, Q3: 29–59; 9 male). DNA methylation-based tumor classification using the NCI/Bethesda classifier (v2) indicated a match to HGAP with high confidence (calibrated score >0.9) in 14 cases and was suggestive in 3 cases. All had MAPK pathway alterations (NF-1 mutation in 11, FGFR1 mutation in 2, KIAA1549::BRAF fusion in 2, FGFR1 transcript variant suggestive of internal tandem duplication in 1, KANK2::NTRK2 fusion in 1). All tumors also had CDKN2A/B homozygous deletion and 9 lost ATRX expression. Tumors were infratentorial (n=8), supratentorial (n=7), and spinal (n=2) at initial presentation. First line treatment included surgery followed by chemoradiation with temozolomide (n=12), surgery followed by radiation (n=4), radiation alone (n=1), and surgery alone (n=1). Gross total resection was achieved in 3/17, 8/17 had subtotal resection and 5/17 had biopsy. 14/17 received a median RT dose of 5700 cGy (range, 4005–6000) in 30 fractions. The median follow-up duration was 34.9 months. 8 patients experienced disease progression. The median progression-free survival (mPFS) was 30.3 months (95% CI: 15.4–NR). 6/8 underwent surgery; 3/8 underwent RT. Systemic therapies were administered in 5/8 (lomustine, bevacizumab, pembrolizumab, temozolomide, trametinib, tovorafenib). 3/17 patients died due to progressive disease with an OS of 54.2, 94.6 and 17.4 months, respectively. The mOS for the whole cohort was 54.2 months (95% CI: 54.2-NR). CONCLUSION While the optimal management of this newly recognized entity has yet to be established, our cohort demonstrates a clinical behavior consistent with CNS WHO grade 3, aligning with current WHO interpretations.

Abstract IDH-wildtype diffuse astrocytic gliomas with lower-grade histological features alongside specific genomic alterations (e.g., TERT promoter mutation, EGFR amplification, gain Chr7/loss Chr10) are presently diagnosed as glioblastoma, IDH-wildtype (GBM) under WHO 2021 criteria. Previous analyses categorized these into “early/evolving” or “surgically undersampled” GBMs. However, it remains unclear whether these tumors share identical clinical and prognostic features with histologic GBM. Data from IDH-wt molecular GBMs across six institutions were aggregated. Astrocytic IDH-wt tumors of any grade that lacked necrosis or microvascular proliferation on initial histopathology yet exhibited molecular alterations consistent with WHO 2021 GBM diagnosis, were included. Recursive partitioning analysis (RPA) was used to categorize patients into risk groups based on overall survival (OS). Among the 264 patients included, the median age was 60 years (IQR: 51-67), with 162 (61%) being male. Additionally, 210 (80%) were diagnosed since 2016, and 216 (82%) received chemoradiation plus adjuvant temozolomide (GBM-SOC). The median follow-up period was 3.9 years (95% CI: 3.4, 4.6). Identified univariate risk factors for worse survival included older age, biopsy-only status, EGFR amplification, absence of initial adjuvant treatment, low post-operative KPS, and tumor histologic grade of 3. RPA divided patients into four distinct survival groups (p<0.0001). The “best” risk group comprised lower grade (<=2) tumors (n=58, median OS (mOS): 40 months (95% CI: 33, NA)). The “second best” group included Grade 3 patients receiving GBM-SOC without steroids during initial RT (n=139, mOS: 25 months (95% CI: 22, 28)). The “second worst” group encompassed Grade 3 patients who were on steroids during initial RT while receiving GBM-SOC (n=38, mOS: 15 months (95% CI: 13, 20)). The “worst” group contained Grade 3 tumors that had not undergone SOC (n=29, mOS: 9 months (95% CI: 4, 26)). These data show that histologic features and treatment are strong indicators of patient outcomes.