Citations

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 Background Neurolymphomatosis (NL) is a rare and frequently underrecognized manifestation of lymphoma, characterized by infiltration of peripheral or cranial nerves by malignant lymphocytes. Limited data exist to guide optimal management. We present the largest single-enterprise (included the 3 Mayo Clinic sites) cohort to date, evaluating disease characteristics, treatment patterns, and outcomes in patients (pts) with NL. Methods We conducted a retrospective analysis of pts ≥18 years (yrs) diagnosed with clinical, radiographic, or biopsy-confirmed NL at Mayo Clinic between January 2002 and December 2024. Baseline characteristics were compared using independent t-tests for continuous variables and Fisher's exact or Pearson's chi-square tests for categorical variables. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Objective response rate (ORR) was a secondary endpoint. Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression were used to assess survival outcomes and associated factors. Results We included 149 pts. Median age at diagnosis was 64 yrs (range 22–83), and 23% were female. Most presented with stage IV disease (89%), and NL was part of the initial lymphoma presentation in 42% (primary NL). Diagnostic confirmation was based on histopathology and imaging in 55%, imaging alone in 26%, histopathology alone in 16%, and clinical features in 4%. Diffuse large B-cell lymphoma was the most common histology (61%), followed by marginal zone lymphoma (7%), and T-cell lymphoma (7%). Clinical presentation included painful neuropathy (65%), concurrent CNS involvement (37%), systemic disease (43%), elevated LDH (46.9%), and ECOG ≥2 (38%). Pattern of involvement included cranial nerves (29%), peripheral nerves (28%), and spinal nerve roots (27%). Median follow-up was 92 months (95% CI, 63–119). Among 137 treatment-evaluable pts, first-line regimens included methotrexate-based (MTX) (62%), bendamustine-based (B) (11%), cytarabine-based (Ara-C) (4%), and rituximab-based (R) (excluding MTX, B, or Ara-C) (15%) therapies. Radiation was used in 18%, and 21% underwent autologous stem cell transplant (ASCT). Among 103 response-evaluable pts, the ORR was 67% (52% complete response [CR], 16% partial response [PR]). ORRs by regimen were 78% (MTX), 92% (B), and 86% (R). On univariate analysis, primary NL (OR 4.3) and frontline ASCT (OR 8.47) were associated with higher odds of response (p<0.05), while ECOG ≥2 (OR 0.24) and localized disease (OR 0.33) were associated with lower odds. On multivariable analysis (MVA), primary NL (OR 5.0 [1.5–12.1], p=0.02) and ECOG >1 (OR 0.2 [0.06–0.79], p=0.018) remained significant predictors of response. Median PFS was 13 months (95% CI, 9–36), with a 2-yr PFS of 43% (95% CI, 35–53). On MVA, concurrent CNS involvement (HR 2.4, p=0.05), systemic disease (HR 2.7, p=0.02), and ECOG >1 (HR 2.4, p=0.02) were associated with inferior PFS, while primary NL was associated with improved PFS (HR 0.3, p=0.002). Patients undergoing frontline ASCT had a median PFS of 108 months (95% CI, 58–not reached) with a 7-yr PFS of 62% (95% CI, 46–84). Among those receiving second line and beyond (2+) therapy, 24 received MTX-based regimens, 10 received CAR-T, and 8 received tyrosine kinase inhibitors. Post-ASCT median PFS was 30 months (95% CI, 15–NR). Among CAR-T recipients, evaluable responses (n=8) included 4 CR, 1 PR, and 3 PD. Median OS was 56 months (95% CI, 29–133), with a 5-yr OS of 50% (95% CI, 41–60). On MVA, ECOG ≥2 (HR 2.5, p=0.02) predicted worse OS, while receipt of ASCT (HR 0.4, p=0.04) and primary NL (HR 0.5, p=0.04) were associated with improved OS. Conclusion This study represents the largest single-enterprise cohort of pts with NL, providing comprehensive insight into its clinical presentation, treatment patterns, and outcomes. Primary NL and preserved PS were independently associated with improved response rates and survival, while CNS and systemic involvement at diagnosis portended poorer PFS. MTX and bendamustine-based regimens achieved high ORR, and frontline ASCT was associated with significantly prolonged remission duration and OS. These findings highlight the prognostic importance of baseline disease burden and functional status, support the early incorporation of high-intensity regimens in eligible patients, and underscore the urgent need for prospective studies to guide evidence-based management in this rare and understudied population.

Abstract Background DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed. Methods We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade. Results We first demonstrated that all risk groups benefit from gross total resection. All “high-risk, RT sensitive” tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (P = .003); after subtotal resection (STR), from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (P = .004). “Low-risk, RT sensitive cases” (n = 1207, 61.8%) also benefited from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, P = .03), suggesting that RT be considered in these patients. Neither “low-risk RT resistant” (n = 84, 4.3%) nor “high-risk RT resistant” (n = 356, 18.2%) cases benefited from RT, and the latter group was associated with universally poor outcomes. Conclusions We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.

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 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 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.