Heimberger, Amy B.

Abstract Background Oligodendrogliomas were previously defined by histology, but since 2016, the WHO has integrated molecular signatures to create the entity of IDH-mutant 1p/19q-codeleted oligodendroglioma. These tumors harbor recurrent genetic alterations—including IDH mutations, CIC and FUBP1 loss-of-function mutations, and PI3K-activating mutations—that collectively define a genetic profile distinct from IDH-mutant astrocytoma and IDH wild-type glioblastoma. The paucity of faithful model systems has impeded progress in advancing mechanistic insights and therapeutic development for oligodendroglioma. Results To test whether recurrent mutations found in oligodendroglioma patients would drive tumor formation in mice, we utilized in utero electroporation to introduce combinations of gain- and loss-of-function mutations into the developing cortex. Initial tests that included expression of IDH-and PIK3CA-mutants with Cic and Fubp1 loss (termed Oligo) were non-tumorigenic but biased cells towards glial lineages. Introducing Cdkn2a-loss, which is normally suppressed by IDH-associated epigenetic changes in human tumors, to the Oligo combination (termed Oligo-Cdkn2a) resulted in fully penetrant tumor formation. Histopathological review showed the Oligo Cdkn2a-loss mouse model resembled grade II/III oligodendroglioma with rounded nuclei and open chromatin. If Trp53-loss is used (termed Oligo-Trp53) in place of Cdkn2a-loss, tumor histology shifts to a more astrocytic phenotype with angulated nuclei with denser chromatin, demonstrating a direct impact of Trp53-loss on histological features. Bulk RNA-sequencing verifies metabolic alterations driven by the presence of IDH-mutations in Oligo-Cdkn2a mouse models compared to an IDH wild-type GBM model. Single-cell transcriptional profiling shows that Oligo-Cdkn2a tumors contain an increased population of tumor cells that resemble more differentiated oligodendrocytes. Examination of the tumor immune microenvironment within Oligo-Cdkn2a and IDHWT GBM mouse models is being performed to determine changes in immune cell infiltration and transcriptional programs. Conclusion We have created, to our knowledge, the first murine model of IDH-mutant 1p/19q-codeleted oligodendroglioma that incorporates Cic loss. This immune-competent mouse model of IDH-mutant 1p/19q-codeleted oligodendroglioma offers a powerful new tool for interrogating tumor biology and evaluating preclinical therapeutic strategies.

Abstract Oligodendroglioma is a primary central nervous system tumor classified by the presence of isocitrate dehydrogenase (IDH) mutations and codeletion of 1p/19q. Here we describe the generation of an IDH-mutant 1p/19q-codeleted oligodendroglioma mouse model using in utero electroporation. We identified IDH1 R132H , PIK3CA E545K , Cic KO , Fubp1 KO and Cdkn2a KO as the optimal combination (termed Oligo Cdkn2a ) to drive fully penetrant tumors that histologically resemble human grade II/III IDH-mutant, 1p/19q-codeleted oligodendroglioma. Replacing Cdkn2a with Trp53 loss in this mouse model shifted tumor histology towards high grade astrocytoma. Oligo Cdkn2a tumors displayed metabolic and transcriptional changes associated with IDH and CIC mutations, and single cell sequencing identified a bias towards oligodendrocyte differentiation compared to an IDH wild-type glioblastoma mouse model. Oligo Cdkn2a tumors represent the first mouse model system to recapitulate the genetic, histological and transcriptional features of human IDH-mutant 1p/19q-codeleted oligodendrogliomas, offering a platform to further dissect tumor biology and test new therapeutic strategies.

Abstract Background: Glioblastoma recurrence is inevitable despite aggressive surgical resection of contrast enhancing (CE) tumor. Unlike the deep molecular characterization of CE glioblastoma, unresected infiltrating tumor, representing minimal residual disease (MRD) has not been well profiled. Increasingly, clinical trials are being conducted in the MRD setting for glioblastoma patients, necessitating understanding of unresected infiltrating tumor for targeted therapeutic development. Methods: Visium HD spatial transcriptomics was used to profile a cohort of newly diagnosed, treatment-naïve glioblastoma patients that underwent supramaximal resection, with both core and infiltrating in situ components. Spatial profiles generated expression signatures of infiltrative and CE tumor. For in silico prediction of therapeutics targeting the MRD, expression signatures of infiltrative and CE tumor were compared with drug perturbation signatures from the NIH L1000 database using the sRGES method to predict reversal of disease expression patterns. IC50 data from human cell lines were aggregated from the Genomics of Drug Sensitivity in Cancer or individual study data while blood-brain barrier (BBB) penetrance was predicted using the CNS-MPO approach. Findings: Infiltrative tumor expression profiles were enriched for genes corresponding to neural and oligodendrocyte progenitor like cells. In contrast CE glioblastoma displayed mesenchymal and astrocytic programs, suggesting spatial concentration of glioblastoma states within MRD and CE tumor. For CE glioblastoma, proteasome inhibitors and glucocorticoid receptor agonists were predicted to have the highest antitumor activity. In contrast, histone deacetylase inhibitors (HDACi) were highly ranked for MRD while exhibiting low predicted efficacy in CE tumor. Synergy analysis of drug candidates provided multiple dual treatment options for both the CE and MRD contexts. Conclusions: Cumulatively, these data indicate that the resected tumor does not reflect the MRD state, providing clarity on why targeted therapeutics for glioblastoma have not been particularly successful. Spatial profiling of the MRD demonstrates vulnerability to unique drug targets, which if utilized in the correct setting could confer greater survival benefit. Citation Format: Harrshavasan Congivaram, Shashwat Tripathi, Mateo Gomez, Katy McCortney, Ching Man Wai, Ruochen Du, Thomas K. Sears, Jianzhong Zhang, Daniel J. Brat, Craig M. Horbinski, Mark W. Youngblood, Jared T. Ahrendsen, Adam M. Sonabend, Stephen T. Magill, Matthew C. Tate, Maciej S. Lesniak, Sean Sachdev, Timothy Sita, Priya Kumthekar, Karan Dixit, Robin Buerki, Ditte Primdahl, Mustafa Khasraw, John de Groot, David A. Reardon, Rimas V. Lukas, Roger Stupp, Amy B. Heimberger. In silico screening of therapeutics candidates targeting minimal residual disease in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2496.

ABSTRACT Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

Abstract Glioblastoma (GBM) presents as a highly aggressive and malignant brain tumor with limited treatment options. Despite the availability of existing therapies, the invasive characteristics of GBM frequently result in tumor recurrence and poor prognosis. We previously developed a B-cell-based therapy (BVax) that has shown promising results in its ability to induce therapeutic responses in preclinical models of GBM. Our research revealed potency of BVax can be attributed to its robust antigen-presenting capability, cognate antigen specificity with T cells, and ability to generate tumor-reactive antibodies. The present study aims to characterize the unique antigenic reactivity of BVax-derived antibodies by evaluating their immunological effects and therapeutic potential in both murine models and GBM patient-specific samples. Leveraging CD45.1 and CD45.2 congenic mouse models, we first revealed that BVax preferentially migrated to glioma-bearing brains upon intravenous injection, resulting in a CD38+CD20-CD19+ plasmablast phenotype. To further characterize the reactivity of BVax-derived immunoglobulins (Ig), we utilized proteomic analysis and identified preferential targets of Gelsolin, Fibronectin, Versican, Fibrinogen, and collagen type IV alpha, commonly associated with cell motility and the extracellular matrix. Microdialysis data confirmed the increased presence of many of these BVax-derived Igs-recognized antigens in tumor regions compared to normal brain regions in GBM patients. Utilizing purified BVax-derived antibodies from patient specimen, in vitro cell motility assays demonstrated a significant prevention of the invasion and migration of cell lines from patient derived xenograft (PDX). In this study, we concluded that BVax elicits antitumor activity through targeted migration to tumor-bearing regions, differentiation into plasmablasts, and robust secretion of specific Igs that retain the ability to inhibit functionalities associated with cell invasion and migration. Here, we present the first in-depth study that characterizes GBM patients’ antibody reactivity, specifically a subset of inflammatory B cells (BVax) with therapeutic effects that offer an indication for a new direction of immunotherapies.

Adult-type diffusely infiltrating gliomas, of which glioblastoma is the most common and aggressive, almost always recur after treatment and are fatal. Improved understanding of therapy-driven tumor evolution and acquired therapy resistance in gliomas is essential for improving patient outcomes, yet the majority of the models currently used in preclinical research are of therapy-naïve tumors. Here, we describe the development of therapy-resistant IDH-wildtype glioblastoma patient-derived xenografts (PDX) through orthotopic engraftment of therapy naïve PDX in athymic nude mice, and repeated in vivo exposure to the therapeutic modalities most often used in treating glioblastoma patients: radiotherapy and temozolomide chemotherapy. Post-temozolomide PDX became enriched for C>T transition mutations, acquired inactivating mutations in DNA mismatch repair genes (especially MSH6), and developed hypermutation. Such post-temozolomide PDX were resistant to additional temozolomide (median survival decrease from 80 days in parental PDX to 42 days in a temozolomide-resistant derivative). However, temozolomide-resistant PDX were sensitive to lomustine (also known as CCNU), a nitrosourea which induces tumor cell apoptosis by a different mechanism than temozolomide. These PDX models mimic changes observed in recurrent GBM in patients, including critical features of therapy-driven tumor evolution. These models can therefore serve as valuable tools for improving our understanding and treatment of recurrent glioma.

Abstract Immunotherapy has revolutionized treatment for many hard-to-treat cancers but has yet to produce significant improvement in outcomes for patients with glioblastoma. This reflects the multiple and unique mechanisms of immune evasion and escape in this highly heterogeneous tumor. Glioblastoma engenders profound local and systemic immunosuppression and is remarkably effective at inducing T-cell dysfunction, posing a challenge to any immunotherapy-based approach. To overcome these mechanisms, multiple disparate modes of immune-oriented therapy will be required. However, designing trials that can evaluate these combinatorial approaches requires careful consideration. In this review, we explore the immunotherapy resistance mechanisms that have been encountered to date and how combinatorial approaches may address these. We also describe the unique aspects of trial design in both preclinical and clinical settings and consider endpoints and markers of response best suited for an intervention involving multiple agents.

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel–novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel–novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel–novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

Abstract Background This secondary image analysis of a randomized trial of proton radiotherapy (PT) versus photon intensity-modulated radiotherapy (IMRT) compares tumor progression based on clinical radiological assessment versus Response Assessment in Neuro-Oncology (RANO). Methods Eligible patients were enrolled in the randomized trial and had MR imaging at baseline and follow-up beyond 12 weeks from completion of radiotherapy. “Clinical progression” was based on a clinical radiology report of progression and/or change in treatment for progression. Results Of 90 enrolled patients, 66 were evaluable. Median clinical progression-free survival (PFS) was 10.8 (range: 9.4–14.7) months; 10.8 months IMRT versus 11.2 months PT (P = .14). Median RANO-PFS was 8.2 (range: 6.9, 12): 8.9 months IMRT versus 6.6 months PT (P = .24). RANO-PFS was significantly shorter than clinical PFS overall (P = .001) and for both the IMRT (P = .01) and PT (P = .04) groups. There were 31 (46.3%) discrepant cases of which 17 had RANO progression more than a month prior to clinical progression, and 14 had progression by RANO but not clinical criteria. Conclusions Based on this secondary analysis of a trial of PT versus IMRT for glioblastoma, while no difference in PFS was noted relative to treatment technique, RANO criteria identified progression more often and earlier than clinical assessment. This highlights the disconnect between measures of tumor response in clinical trials versus clinical practice. With growing efforts to utilize real-world data and personalized treatment with timely adaptation, there is a growing need to improve the consistency of determining tumor progression within clinical trials and clinical practice.

Abstract Background To determine if proton radiotherapy (PT), compared to intensity-modulated radiotherapy (IMRT), delayed time to cognitive failure in patients with newly diagnosed glioblastoma (GBM). Methods Eligible patients were randomized unblinded to PT vs IMRT. The primary endpoint was time to cognitive failure. Secondary endpoints included overall survival (OS), intracranial progression-free survival (PFS), toxicity, and patient-reported outcomes (PROs). Results A total of 90 patients were enrolled and 67 were evaluable with median follow-up of 48.7 months (range 7.1-66.7). There was no significant difference in time to cognitive failure between treatment arms (HR, 0.88; 95% CI, 0.45-1.75; P = .74). PT was associated with a lower rate of fatigue (24% vs 58%, P = .05), but otherwise, there were no significant differences in PROs at 6 months. There was no difference in PFS (HR, 0.74; 95% CI, 0.44-1.23; P = .24) or OS (HR, 0.86; 95% CI, 0.49-1.50; P = .60). However, PT significantly reduced the radiation dose for nearly all structures analyzed. The average number of grade 2 or higher toxicities was significantly higher in patients who received IMRT (mean 1.15, range 0-6) compared to PT (mean 0.35, range 0-3; P = .02). Conclusions In this signal-seeking phase II trial, PT was not associated with a delay in time to cognitive failure but did reduce toxicity and patient-reported fatigue. Larger randomized trials are needed to determine the potential of PT such as dose escalation for GBM and cognitive preservation in patients with lower-grade gliomas with a longer survival time.