MacLean Pancoast Nasrallah

BACKGROUND: Neurosurgeons are in the forefront of developing clinical trials that harness the immune system to combat glioblastoma. Autologous tumor lysate–loaded dendritic cell vaccine (DCVax-L) is a novel immunotherapy for glioblastoma. An unmet need is to develop a sensitive, specific, feasible, clinically relevant, neuroimaging biomarker that accurately reflects response to immunotherapy. OBSERVATIONS: This illustrative case involves a 63-year-old female who survived for 64 months following near-total resection of a glioblastoma and enrollment in a clinical trial (NCT00045968) using DCVax-L. A multivariate logistic regression model was used to assess treatment response longitudinally at 11 time points measuring predictive probability (PP) of tumor progression based on diffusion tensor imaging– and perfusion-weighted imaging–derived parameters within the contrast-enhancing region. Lesions were considered true progression (TP) if the PP was ≥ 50% and pseudoprogression (PsP; predominant treatment effects) if the PP was < 50 %. These findings were subsequently validated by histopathological analysis during resection for tumor recurrence. LESSONS: These findings support the use of multiparametric MRI to monitor the effects of immunotherapy in patients with glioblastoma. Such multiparametric prediction models using noninvasive, advanced neuroimaging may be helpful to differentiate TP from PsP in longitudinal assessment of treatment response. https://thejns.org/doi/10.3171/CASE26655

Background: High-grade astrocytoma with piloid features (HGAP) is a recently defined tumor type that is not well understood. Prognostic factors of clinical outcomes are not well established. Methods: Methylation profiling was performed on tumor samples, many at the National Cancer Institute (NCI) Laboratory of Pathology, and others from publicly available sources. Methylation classifier scores of ≥0.90 to the HGAP class on the NCI-Bethesda classifier version 3 were included. Clinical features were collected from the medical record. Survival analyses were performed using the Kaplan-Meier and Cox-proportional hazards methods. Results: The cohort comprised 421 patients. There were high rates of ATRX alteration (62%), CDKN2A/B homozygous loss (78%) and MGMT promoter methylation (53%). MAPK alterations were identified in 74% of evaluable samples. The median age was 46 years, and posterior fossa location was predominant (52%). The median overall survival was 88 months. Older age (P = .01) and the presence of an ATRX alteration (P = .04) were found to be negative prognostic factors. The presence of cystic features on magnetic resonance imaging (MRI) was found to be favorably prognostic (P = .01). Factors that were not significantly associated with prognosis included histologic high-grade features, CDKN2A/B homozygous deletion, MGMT promoter methylation, extent of resection, and presence of NF1 syndrome. Conclusions: This large cohort establishes relative frequencies of several important markers. Additionally, older age, the presence of an ATRX alteration, and cystic features on MRI were found to be prognostic. Our work may aid in optimizing treatment regimens for patients with this tumor type.

Background: In glioblastoma, tumor-related epilepsy (TRE) is paradoxically associated with longer survival, but the mechanism is unclear. We hypothesized that structural connectivity is associated with seizure susceptibility and survival, and tested whether these associations are independent or mediated. Methods: We retrospectively studied glioblastoma patients with preoperative diffusion-weighted MRI. Fractional anisotropy-weighted connectivity matrices were constructed using lesion-aware tractography. Mean edge weight and local efficiency were computed per Yeo 7-network and with global efficiency at hemisphere and whole-brain levels. Associations with TRE, overall survival (OS), and progression-free survival (PFS) were assessed using logistic regression and Cox proportional hazards models with false discovery rate correction. Causal mediation analysis tested whether connectivity and TRE mediate each other’s relationship with overall survival. Results: We included 420 glioblastoma patients. Of 406 classifiable for TRE, 217 (53%) had TRE. TRE was associated with higher local efficiency across all seven contralateral networks ( d= 0.42-0.49, q< 0.010) and five of seven ipsilateral networks ( d =0.44-0.49, q< 0.050). In adjusted models, higher contralateral global efficiency was associated with longer OS (HR 0.84, q= 0.021), with no metric associated with PFS. TRE did not mediate the connectivity-OS relationship. Contralateral local efficiency partially mediated the TRE-OS association when postoperative KPS was excluded from the model (ACME 16.6 days, 7.8% mediated, p =0.029), though this was attenuated when KPS was included ( p =0.074). Conclusions: Structural connectivity may partially mediate the seizure-survival association, suggesting preserved network architecture may contribute to longer survival in glioblastoma patients with TRE, though most of the effect operates through other pathways. Key Points: Tumor-related epilepsy in glioblastoma was associated with relatively greater structural connectivity. Higher contralateral global efficiency related to longer overall survival. Contralateral local efficiency may partly mediate the seizure-survival link. Importance of the Study: Seizures in glioblastoma are paradoxically associated with longer survival, yet the biological basis of this relationship has remained unknown. Using lesion-aware diffusion tractography in 420 patients, we found that the same structural network measures relate to both seizure susceptibility and survival. Patients with tumor-related epilepsy had relatively greater white matter connectivity, and higher contralateral connectivity was independently associated with longer overall survival. Causal mediation analysis showed that connectivity accounted for a small fraction of the seizure-survival association, suggesting that seizures mark, rather than confer, the survival advantage, and that both may reflect a shared upstream driver not captured here. These findings identify structural connectivity as a shared correlate and candidate imaging biomarker for seizure risk and prognosis in glioblastoma.

Central nervous system germ cell tumors are uncommon and include germinomas, teratomas, and other nongerminomatous germ cell tumors. Mature teratomas are usually cured by complete resection, but rare patients later develop a histologically distinct malignant germ cell tumor. We report a man who presented at age 20 years with a cystic pineal lesion. Gross‐total resection at age 21 years showed a mature teratoma with bronchogenic differentiation and no overt immature or malignant component on H&E. Serial postoperative MRI for 3 years showed no recurrence. Eleven years after surgery, he developed headache, gait disturbance, and confusion due to a 4.6‐cm pineal mass with obstructive hydrocephalus. Serum alpha‐fetoprotein was normal, whereas beta‐human chorionic gonadotropin (β‐hCG) was elevated. The resected tumor showed sheets of large round cells with clear cytoplasm and dense lymphocytes. Immunohistochemistry demonstrated strong nuclear OCT4 and SALL4 and membranous c‐KIT, with focal β‐hCG‐positive syncytiotrophoblastic giant cells. No nongerminomatous germ cell tumor component was identified, supporting a diagnosis of germinoma with focal syncytiotrophoblastic giant cells. After this diagnosis, retrospective evaluation of the initial tumor showed multifocal, patchy OCT4 immunoreactivity in scattered cells along the ciliated respiratory‐type epithelial lining. SALL4, c‐KIT, and PLAP were focally positive in corresponding regions. These findings raised the possibility of an occult germ cell marker‐positive population, although no discrete expansile germinoma component was identified. This case illustrates that metachronous germinoma can develop after a long disease‐free interval following resection of a mature teratoma and highlights the value of retrospective pathologic evaluation and long‐term surveillance.

A working group of cIMPACT-NOW evaluated the literature on IDH-mutant gliomas for opportunities to improve pathology-based risk stratification and grading, especially at the interface of CNS WHO grade 2 and 3, since therapeutic decisions depend on risk assessment for clinical management. The group also evaluated newly described IDH-mutant glioma subgroups and considered multidisciplinary aspects of therapeutic decision-making. PDGFRA amplification was associated with highly aggressive clinical behavior of IDH-mutant astrocytomas, consistent with CNS WHO grade 4. Other genetic alterations associated with intermediate risk and most consistent with CNS WHO grade 3 included PIK3 mutations, EGFR amplification, MYCN amplification and specific RB pathway alterations, including CDK4, CDK6 and CCND2 amplification, RB1 homozygous deletion or mutation, and mutation of CDKN2A. DNA methylation signatures of G-CIMP-low or A_IDH_HG were consistent with CNS WHO grade 3 or 4, depending upon other grading criteria. We suggest a mitotic count of ≥ 3 per 2.4 mm2 could be considered as a CNS WHO grade 3 criterion. Our review did not uncover features to improve grading of oligodendroglioma, IDH-mutant and 1p/19q-codeleted, although CNS WHO grade 3 tumors with elevated mitotic rates, yet lacking necrosis, microvascular proliferation, CDKN2A/B homozygous deletion and MRI contrast enhancement, may be associated with extended survival. Implementing evidence-based criteria for risk stratification and grading will improve guidance for clinical decision-making.

Glioblastoma is a highly aggressive primary brain tumor with near-universal recurrence despite maximal safe resection followed by standard chemoradiation. We conducted a prospective pilot study (ClinicalTrials.gov identifier: NCT03477513) with predefined endpoints and structured dose-escalation criteria to evaluate the feasibility and safety of personalized precision radiation therapy (PPRT) guided by machine learning (ML)-based maps of tumor infiltration. Twenty patients with newly diagnosed IDH-wildtype glioblastoma who underwent gross total resection received PPRT with concomitant and adjuvant temozolomide. The primary outcomes were median progression-free survival (PFS) and safety. Secondary outcomes included patterns of recurrence, rate of clinically significant toxicity, and median overall survival (OS). PPRT was feasible and well tolerated, with no grade ≥3 acute adverse events; 47% experienced grade 1 and 53% grade 2 events. Following therapy, radiation necrosis occurred in 47% of PPRT-temozolomide patients versus 12% in standard-of-care patients (p < 0.001). Median PFS was 24.4 months versus 11.6 months in 68 propensity score matched (PSM) historical control group (HR 0.28, 95% CI 0.13-0.61; p = 0.001). Median OS was 35.4 versus 17.7 months (HR 0.34, 95% CI 0.17-0.69; p = 0.003). Exploratory post hoc comparison suggests improved survival, but these findings are preliminary and require validation in randomized trials.

Background: Approximately 12 000 adolescents and young adults (ages 15-39, AYAs) are diagnosed with a primary central nervous system (CNS) tumor each year. DNA methylation profiling has transformed CNS tumor classification by refining diagnostic accuracy and identifying biologically distinct subtypes, but its application has not been systematically evaluated in the AYA population. In this study, we examined the spectrum of CNS tumor types across age groups, with a focus on the AYA population. Methods: We assembled a large dataset of CNS tumor samples with age annotations and methylation profiles matching with high confidence to a CNS tumor type using the NCI/Bethesda classifier. Prevalence of tumor type, methylation class and DNA copy number aberrations were compared across age strata (pediatric, AYA, and adult). Results: The cohort of 21 712 CNS tumors included 5351 tumors from AYAs (25%), which showed a mixed pattern, with tumors typical of childhood (eg, medulloblastoma) as well as those common in older adults (eg, glioblastoma). Several tumor types were specifically enriched in the AYA, including IDH-mutant astrocytoma, pleomorphic xanthoastrocytoma, posterior fossa group B ependymoma, and diffuse hemispheric glioma, H3 G34-mutant, among others. Distinct methylation subclasses of multiple tumor types were observed in the AYA, and patterns of genomic aberrations showed age-specific distributions. Conclusion: Large-scale methylation profiling revealed unique classification patterns of CNS tumors in AYAs, with specific tumor types, subclasses, and genomic alterations enriched in this population. These data may serve as a valuable reference resource for better understanding the spectrum of CNS tumors affecting AYA patients.

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. Whereas molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. In this study, we developed Path2Omics, a deep learning framework that independently predicts gene expression and methylation from histopathology across 30 The Cancer Genome Atlas cancer types. Path2Omics comprised two components: a “formalin-fixed, paraffin-embedded (FFPE) model” trained on FFPE slides and a “fresh-frozen (FF) model” trained on FF slides. When evaluated on seven external datasets, the “FF model” outperformed the “FFPE model,” even though six of the datasets consisted exclusively of FFPE slides. The “integrated model” combined predictions from both, achieving a 30% improvement over the FFPE model alone and robustly predicting approximately 4,400 genes (of 18,000). Importantly, the inferred gene expression closely matched actual values in predicting patient survival and treatment response. Overall, this study demonstrated the potential of Path2Omics to advance precision oncology using routine histopathology slides.Significance:Path2Omics is an AI framework that predicts gene expression and methylation from histopathology slides, providing inferred values that are nearly as effective as actual values in predicting patient survival and treatment response.This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.