Lachance, Daniel Honore

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.

Abstract BACKGROUND The majority of pituitary neuroendocrine tumors (PitNETs) are effectively managed with medical therapy, surgery, and/or radiation therapy; however, a subset of pituitary adenomas exhibit aggressive behavior. Temozolomide is thought to be an effective treatment option for patients with PitNETs. METHODS We identified and retrospectively reviewed 11 patients with pituitary tumors treated with temozolomide. We then conducted a review of the literature, combining 114 previously published cases with our cohort. RESULTS 125 cases were identified with diagnoses of pituitary adenomas (n=45), aggressive pituitary tumors (n=61), and pituitary carcinoma (n=19) treated with temozolomide. 79 patients were male (68.1%) with a median age of 47.5 (range 9-72). A variety of tumor types were represented: 50 corticotroph adenomas (40.7%), 37 prolactinomas (30.1%), 22 hormone nonfunctioning adenomas (17.9%), 8 somatotroph adenomas (6.5%), 6 other functioning adenomas (4.9%), and 2 cases where the functional status was not mentioned. Median treatment duration was 9 cycles (n=91, 150-200 mg/m2 days 1-5, 28-day cycles; n=2, 75 mg/m2, days 1-21, 28-day cycles; n=1, 75 mg/m2 daily). 27 patients received concurrent therapy with a dopamine agonist/somatostatin analogue (n=10), other chemotherapy agent (n=9) or radiotherapy (n=8). Radiographic responses included stable disease (n=40), partial response (n=35), complete response (n=8), and progressive disease (n=30). Hormonal responses included complete response (n=13), partial response (n=32), and no response (n=16). 22 patients (47.8%; out of 46 patients where progression/recurrence was assessed) experienced progression/recurrence following temozolomide initiation with a median progression free survival (PFS) of 24 months. There was no significant association between MGMT methylation status and radiological or hormonal response. No unexpected toxicities were observed. CONCLUSION In this cohort, temozolomide was well-tolerated with a median PFS of 24 months. MGMT methylation status did not predict temozolomide response. Temozolomide is an option with a survival benefit for patients with PitNETs.

Abstract The WHO 2021 classification of central nervous system tumors incorporated molecular markers to better define diffuse glioma subgroups. We analyzed array-based copy number, targeted next-generation sequencing, germline genotyping, and Illumina EPIC array methylation data from 404 primary IDH-mutant astrocytomas to further understand the relationship between copy number and outcomes. Loss of chromosome arm 13q was associated with poor overall survival after adjusting for age, sex, tumor grade, and treatment status (hazard ratio [HR]=2.04 (95% confidence interval: 1.25-3.33), p=0.004). Chromosome 13q loss was also significantly associated with the total number of copy number alterations (p<0.001). Loss of arm 11p was associated with poor survival in the adjusted analysis (HR=2.29 (1.38-3.79), p=0.001). Unsupervised clustering of the raw copy number data identified 12 clusters, where each cluster was defined by distinct copy number alteration(s). Clusters that contained 13q loss exhibited worse overall survival compared to other clusters. The clusters were significantly associated with age at diagnosis (p=0.015), tumor grade (p<0.001) and copy number complexity (p<0.001). The clusters with a high proportion of grade 4 tumors and with the highest complexity had the poorest overall survival. These findings highlight the prognostic relevance of chromosome 13q and 11p losses in IDH-mutant astrocytomas and support the integration of additional copy number alterations into molecular classification frameworks of IDH-mutant astrocytomas to improve risk stratification and patient management.

Abstract Telomere length variation is a hallmark of genomic instability, however, its role in shaping the structural variant (SV) landscape of diffuse astrocytoma remains poorly understood. In this study, we employed Oxford Nanopore long-read sequencing to investigate the association between SVs and chromosome arm-specific telomere length in a clinical patient cohort consisting of n = 20 individuals diagnosed with an IDH mutant astrocytoma. Oxford Nanopore long-read sequencing was performed to a depth of ~20-40× coverage. SVs were identified using Sniffles, copy number alterations were assessed using DNAcopy and chromosome arm-specific telomere length was estimated with Telogator. Statistical analyses were conducted to assess associations between telomere dynamics, structural variation, and copy number changes. Astrocytoma genomes exhibited a broad spectrum of structural variants, including deletions and insertions, with significant enrichment in the short arms of acrocentric chromosomes. A subset of samples demonstrated markedly shorter telomeres relative to the overall cohort, accompanied by an increased burden of structural variants, particularly duplications and inversions, suggesting enhanced genomic instability. Notably, a prominent accumulation of deletion and insertion SVs was observed near chromosome ends and centromeres, highlighting the (sub)telomeric and peri(centromeric) regions as SV hotspots. Statistical modeling revealed a significant association between telomere length and arm-specific variation, including telomeric deletions, inversions, and copy number losses. In summary, our findings reveal a previously underappreciated link between SVs and telomere length in astrocytomas. Genomic rearrangements caused by telomere shortening may represent critical drivers of genome instability and could serve as novel biomarkers for disease progression.

AbstractAbstract Background Central nervous system (CNS) tumors are a significant cause of morbidity and mortality in Sub-Saharan Africa (SSA). This project aimed to assess and map out the current neuro-oncology capacity in SSA, brain tumor registries, and biobanks. Methods This cross-sectional study utilized an online survey to gather data from healthcare professionals involved in CNS tumor care across SSA through the Society for Neuro-Oncology SSA (SNOSSA). The survey captured information on the availability of neuro-oncology practitioners, neuropathology, molecular diagnostics, cancer registries, and biobanking. Results A total of 145 respondents representing 70 healthcare institutions across 22 countries participated. Of the 70 institutions, specialties included neurosurgery (89%, 62/70), anatomic pathology (74%, 52/70), and medical oncology (71%, 50/70). For radiologic evaluation, 64% (45/70) of responding institutions reported having both CT and MRI. Access to molecular testing through immunohistochemistry or next-generation sequencing was available in 46% (32/70) of responding institutions. Multidisciplinary tumor boards were available for CNS cases in 86% (60/70) of responding institutions. Brain tumor-specific registries were available in 29% (20/70) of responding institutions, and 10% (7/70) maintained brain tumor biorepositories. Conclusion This study demonstrates that SSA has substantial potential in neuro-oncology care practice capacity and facilities. The most significant gaps were in neuropathology and molecular testing, which makes widespread implementation of the WHO 2021 diagnosis challenging. Understanding the prevalence of CNS tumor subtypes in SSA is difficult due to the low number of brain tumor registries and biobanks.