PURPOSE:GBM AGILE (ClinicalTrials.gov identifier: NCT03970447) is a phase II/III Bayesian adaptive platform registration trial testing multiple arms against a common control; the primary end point is overall survival (OS). Regorafenib, a multikinase inhibitor, showed OS benefit in recurrent (RD) glioblastoma in the phase II REGOMA trial and entered GBM AGILE as the first investigational arm. METHODS:Patient subtypes included in the regorafenib arm of GBM AGILE were newly diagnosed unmethylated (NDU) and RD glioblastoma. Prospective defined sets of subtypes, or arm signatures, were NDU, RD, and all (NDU + RD). As the first investigational arm in GBM AGILE, regorafenib was equally randomized to the control arm. Treatment in the control arm is temozolomide + radiotherapy (in newly diagnosed) or lomustine (in RD). Efficacy was assessed by OS hazard ratio (HR), arm/control, and demonstrated when the Bayesian probability of benefit (HR <1.00) was ≥98%. Analysis was performed monthly for limited efficacy, which occurs when the Bayesian predictive power is <25% for all signatures, and determines stopping enrollment. Follow-up continued for 12 months after accrual stopped. RESULTS:When the predictive power was <25% in all predefined signatures for regorafenib, accrual stopped for limited efficacy. The final analysis did not demonstrate OS improvement in the regorafenib arm in RD nor NDU glioblastoma. Median HRs were 1.05 (NDU), 1.07 (RD), and 1.07 (all) with final probabilities of benefit (HR <1.00) of 0.421 (NDU), 0.312 (RD), and 0.296 (all). Regorafenib was associated with increased toxicity relative to control. CONCLUSION:GBM AGILE did not show superiority of regorafenib over control in RD (lomustine) or NDU (temozolomide + radiotherapy) glioblastoma, yet caused increased toxicities. Regorafenib has been removed from National Comprehensive Cancer Network guidelines as a treatment option for RD.

e14031 Background: Bevacizumab (Bev), a monoclonal antibody that inhibits vascular endothelial growth factor, was fully FDA-approved in 2017 for use in recurrent glioblastoma and is often considered and utilized to treat all recurrent gliomas. Currently, there are limited studies on Bev use for recurrent isocitrate dehydrogenase 1/2 mutant ( IDH MUT ) gliomas, which can have distinct characteristics, disease outcome, and treatments compared to IDH wild-type ( IDH WT ) glioblastoma. We retrospectively examined Bev usage and efficacy at recurrence in a large single institution IDH MUT glioma cohort. Methods: We identified 138 consecutive IDH MUT glioma patients treated with Bev at recurrence at UCLA between 2005 and 2024. We stratified patients by 2016 WHO tumor diagnosis criteria. We examined how the timing of Bev initiation and other clinical variables affect Bev PFS and Bev OS. Bev PFS as determined by the treating clinician will be compared with our application prototype, Automated Imaging Response Evaluation System (AIRES), which is adapted to utilize RANO criteria. In addition, AIRES will be used to determine treatment response to be correlated with Bev PFS and Bev OS. Results: Time to Bev, which was defined as the time from initial surgery to Bev initiation, was shortest for AA and Grade 4 astrocytoma (G4 Astro). The median Bev PFS and Bev OS were 3.9 and 10.5 months, respectively. Bev PFS were 5.2, 4.6, and 3.3 months for the 1 st , 2 nd , and ≥3 rd recurrence groups, and Bev OS were 15.7, 13.5, and 8.3 months. Median Bev PFS, as determined by the treating clinician, for LO, LA, AO, AA, and G4 Astro were 2.8, 3.7, 6.2, 3.9, and 3.6 months, respectively. The median Bev OS were 7.0, 7.8, 9.5, 12.6, and 10.5 months. Among the 138 patients, the vast majority of patients initiated Bev treatment due to have contrast-enhancing tumor. 98 (71%) were first treated with Bev as a combined therapy, while 40 (29%) were treated with Bev monotherapy. 11 patients were concurrently treated with IDH inhibitors and Bev. The average daily corticosteroid (dexamethasone) dose reduction after 2 months of Bev treatment was 2.6 mg. Current results and Bev PFS are based on clinical criteria, while AIRES determined Bev PFS and response analysis are underway. Conclusions: Unlike our prior study on predominantly IDH WT GBM, later initiation of Bev was associated with diminished efficacy. Amongst the various diagnoses at Bev initiation, we found similar Bev PFS and Bev OS, except unexpectedly, we found AO to have improved Bev PFS and Bev OS compared to LO. Comparison with AIRES to determine Bev PFS and response according to RANO criteria is ongoing to finetune and validate results. Corticosteroid dose reduction was common but not associated with Bev PFS and Bev OS. With the recent increased use of IDH inhibitor treatment, this cohort, as a mostly IDH inhibitor naïve group, may prove valuable as a comparator arm to determine whether IDH inhibitor treatment affects Bev efficacy.

While immune checkpoint blockade (ICB) has revolutionized cancer therapy, its effectiveness in glioblastoma remains limited. Our phase I trial (NCT04606316) enrolled relapsed, IDH wildtype glioblastoma patients undergoing surgical resection. Participants received either dual ICB (ipilimumab + nivolumab, n=25), single-agent PD-1 blockade (nivolumab, n=25), or a placebo (n=10) before surgery. Post-surgery, Arms 1 and 3 continued dual ICB, while Arm 2 remained on nivolumab until progression or toxicity. Dual ICB showed improved survival over single-agent nivolumab, but variability within the cohort highlights the need to develop a rapid blood-based biomarker that can better monitor immune responses and identify patients most likely to benefit from ICB. We conducted bulk RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) from 58 trial participants across two neoadjuvant and two adjuvant time points. Concurrently, we analyzed complete blood count (CBC) with differential values from the same patients at identical time points. We integrated RNAseq and CBC data to characterize systemic immune responses across different ICB regimens. Dual ICB significantly induced white blood cell count in CBC and interferon gene signatures in PBMCs, confirming a systemic pharmacodynamic effect. Multivariate analysis highlighted a strong interferon gene signature at the pre-surgery (post-neoadjuvant) time point that correlated with improved survival. However, concomitant myeloid CBC (monocytes/neutrophils) elevation and related gene signatures following dual ICB treatment were negatively associated with survival at post-adjuvant timepoints, suggesting that chronic systemic inflammation may impair anti-tumor immune responses. Integrated analysis of RNAseq and CBC suggests that dual ICB induces an immediate, productive immune response, but chronic, myeloid-driven inflammation may hinder durable therapeutic efficacy. CBC profiling offers a rapid biomarker for monitoring systemic immune responses to ICB. Implementation of these biomarkers could predict patients most likely to benefit from ICB and inform strategies to counteract chronic inflammation that may attenuate treatment effectiveness.

BACKGROUND AND PURPOSE:Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. MATERIALS AND METHODS:Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. RESULTS:Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. CONCLUSIONS:Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Glioblastoma is the most common type of malignant primary brain tumor and a major cause of morbidity and mortality. In 2021, the World Health Organization updated the classification of Central Nervous System (CNS) tumors to restrict glioblastomas to isocitrate dehydrogenase-wildtype (IDHwt) tumors, improving understanding of the prognosis and optimal therapy for these tumors. This revision also enables more homogeneous populations of patients to be enrolled in clinical trials, facilitating the evaluation of novel therapies. In this updated consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of patients with glioblastoma is discussed. In addition, novel therapies such as immunotherapies, viral therapies, targeted molecular therapies, theranostics, and antibody-drug conjugates will be reviewed, as well as the current challenges and future directions for research.

Sodium neuroimaging provides unique insights into the cellular and metabolic properties of brain tumors. However, at 3T, sodium neuroimaging MRI’s low signal-to-noise ratio (SNR) and resolution discourages routine clinical use. We evaluated the recently developed Anatomically constrained GAN using physics-based synthetic MRI artifacts” (ATHENA) for high-resolution sodium neuroimaging of brain tumors at 3T. We hypothesized the model would improve the image quality while preserving the inherent sodium information. 4,573 proton MRI scans from 1,390 suspected brain tumor patients were used for training. Sodium and proton MRI datasets from Twenty glioma patients were collected for validation. Twenty-four image-guided biopsies from seven patients were available for sodium-proton exchanger (NHE1) expression evaluation on immunohistochemistry. High-resolution synthetic sodium images were generated using the ATHENA model, then compared to native sodium MRI and NHE1 protein expression from image-guided biopsy samples. The ATHENA produced synthetic-sodium MR with significantly improved SNR (native SNR 18.20 ± 7.04; synthetic SNR 23.83 ± 9.33, P = 0.0079). The synthetic-sodium values were consistent with the native measurements (P = 0.2058), with a strong linear correlation within contrast-enhancing areas of the tumor (R2 = 0.7565, P = 0.0005), T2-hyperintense (R2 = 0.7325, P < 0.0001), and necrotic areas (R2 = 0.7678, P < 0.0001). The synthetic-sodium MR and the relative NHE1 expression from image-guided biopsies were better correlated for the synthetic (ρ = 0.3269, P < 0.0001) than the native (ρ = 0.1732, P = 0.0276) with higher sodium signal in samples expressing elevated NHE1 (P < 0.0001). ATHENA generates high-resolution synthetic-sodium MRI at 3T, enabling clinically attainable multinuclear imaging for brain tumors that retain the inherent information from the native sodium. The resulting synthetic sodium significantly correlates with tissue expression, potentially supporting its utility as a non-invasive marker of underlying sodium homeostasis in brain tumors.

Genomic profiling often fails to predict therapeutic outcomes in cancer. This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses. It is unclear whether integrating genomic and functional features of solid tumours can provide unique insight into therapeutic vulnerabilities. We perform combined molecular and functional characterization, via BH3 profiling of the intrinsic apoptotic machinery, in glioma patient samples and derivative models. We identify that standard-of-care therapy rapidly rewires apoptotic signaling in a genotype-specific manner, revealing targetable apoptotic vulnerabilities in gliomas containing specific molecular features (e.g., TP53 WT). However, integration of BH3 profiling reveals high mitochondrial priming is also required to induce glioma apoptosis. Accordingly, a machine-learning approach identifies a composite molecular and functional signature that best predicts responses of diverse intracranial glioma models to standard-of-care therapies combined with ABBV-155, a clinical drug targeting intrinsic apoptosis. This work demonstrates how complementary functional and molecular data can robustly predict therapy-induced cell death. Citation Format: Elizabeth G Fernandez, Wilson X Mai, Kai Song, Nicholas A Bayley, Andrew J. Souers, Jingyi Jessica Li, Thomas G. Graeber, Timothy F Cloughesy, David A. Nathanson. Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in glioma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A007.

Gliomas are lethal malignancies composed of heterogeneous and dynamic subpopulations of cellular states that resemble both normal neurodevelopmental cell types and adaptive responses to stress within the tumor microenvironment (TME). While cellular plasticity enables gliomas to survive environmental pressures, the mechanisms driving glioma state dynamics remain unclear. To explore how glioma state heterogeneity functionally and metabolically interacts with the brain TME, we conducted a multi-omic analysis across 392 glioma specimens, including patient tumors, orthotopic xenografts and gliomasphere cultures using bulk RNA/whole-exome sequencing and lipidomic profiling. Single-cell transcriptome sequencing of a diverse panel of glioma tumors (n = 21) integrated with bulk tumor transcriptome profiling revealed a spectrum of cellular identities associated with distinct lipidomic profiles. Comparison of matched patient and derived models showed that the non-native environments, such as gliomasphere culture, restricted glioma state diversity and enriched for states characterized by elevated de novo fatty acid synthesis. Across environmental contexts, glioma state plasticity was coupled to lipid metabolic plasticity, enabling tumors to concordantly remodel their state composition and lipid profiles in response to environmental constraints. Of note, certain tumors exhibited limited state and metabolic plasticity – specifically those enriched for states within oligodendroglial and neuronal lineages. These gliomas demonstrated reduced de novo lipid synthesis capacity, and a concordant increased reliance on exogenous lipid scavenging within the brain microenvironment for survival. Together, these results link glioma heterogeneity and plasticity to lipid metabolic reprogramming, and highlight how distinct cellular state profiles result in environment-dependent metabolic liabilities.

GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a multi-arm, international, seamless Phase 2/3 response adaptive randomization (RAR) platform trial designed to efficiently identify investigational therapies that improve overall survival and confirm efficacious therapies and biomarker signatures to support registration. GBM AGILE is a collaboration among academic investigators, patient organizations, and industry to support new drug applications for newly diagnosed and recurrent glioblastoma. The primary objective of GBM AGILE is to identify therapies that improve overall survival in patients with newly diagnosed or recurrent glioblastoma. Operating under a Master Protocol, GBM AGILE allows multiple drugs from different companies to be evaluated simultaneously and/or over time against a common control. Investigational therapies are added as information about promising drugs is identified, while other therapies are removed as they complete evaluation. RAR is used within subtypes of the disease to assign participants to investigational arms based on their performance. GBM AGILE has screened over 2300 patients and enrollment continues to be robust. In addition to the efficient evaluation of investigational arms, a goal of GBM AGILE is to expand knowledge of glioblastoma to support advancements in treatment using the data collected within the trial (learning environment). Over 7 million data points are currently available for inclusion in the development of a longitudinal model. Such a model may be able to inform randomization by providing earlier and continuous information regarding patient and arm performance. In addition, serial magnetic resonance imaging scans and biospecimens from baseline through patient progression are being collected for further analysis. An initial 500 baseline tissue samples are being characterized by genome sequencing and transcriptome analysis. NCT number: NCT03970447.

BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high acceleration that is notoriously susceptible to artifacts. MATERIALS AND METHODS: A total of 4,573 anatomical MR sequences from 1,392 patients undergoing clinically indicated MRI of the brain were used to create a synthetic data set using physics-based, simulated artifacts commonly found in EPI. By using multiple MRI contrasts, we hypothesized the GAN would learn to correct common artifacts while preserving the inherent contrast information, even for contrasts the network has not been trained on. A modified Pix2PixGAN architecture with an Attention-R2UNet generator was used for the model. Three training strategies were employed: (1) An ?all-in-one? model trained on all the artifacts at once; (2) a set of ?single models?, one for each artifact; and a (3) ?stacked transfer learning? approach where a model is first trained on one artifact set, then this learning is transferred to a new model and the process is repeated for the next artifact set. Lastly, the ?Stacked Transfer Learning? model was tested on ADC maps from single-shot diffusion MRI data in N = 49 patients diagnosed with recurrent glioblastoma to compare visual quality and lesion measurements between the natively acquired images and AI-corrected images. RESULTS: The ?stacked transfer learning? approach had superior artifact reduction performance compared to the other approaches as measured by Mean Squared Error (MSE = 0.0016), Structural Similarity Index (SSIM = 0.92), multiscale SSIM (MS-SSIM = 0.92), peak signal-to-noise ratio (PSNR = 28.10), and Hausdorff distance (HAUS = 4.08mm), suggesting that leveraging pre-trained knowledge and sequentially training on each artifact is the best approach this application. In recurrent glioblastoma, significantly higher visual quality was observed in model predicted images compared to native images, while quantitative measurements within the tumor regions remained consistent with non-corrected images. CONCLUSIONS: The current study demonstrates the feasibility of using a physics-based method for synthesizing a large data set of images with realistic artifacts and the effectiveness of utilizing this synthetic data set in a ?stacked transfer learning? approach to training a GAN for reduction of EPI-based artifacts.

Background To demonstrate the potential value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) as a rapid, non-invasive metabolic imaging surrogate for pharmacological modulation of EGFR signaling in EGFR-driven GBM, we synchronously conducted a preclinical imaging study using patient-derived orthotopic xenograft (PDOX) models and validated it in a phase II molecular imaging study in recurrent GBM (rGBM) patients using osimertinib.Methods A GBM PDOX mouse model study was performed concurrently with an open-label, single-arm, single-center, phase II study of osimertinib (NCT03732352) that enrolled 12 patients with rGBM with EGFR alterations. Patients received osimertinib daily and 3 18F-FDG PET scans: two 24 h apart prior to dosing, and one 48 h after dosing.Results GBM PDOX models suggest osimertinib has limited impact on both 18F-FDG uptake (+ 9.8%-+25.9%) and survival (+ 15.5%; P = .01), which may be explained by insufficient exposure in the brain (Kpuu: 0.30) required to robustly inhibit the EGFR alterations found in GBM. Treatment with osimertinib had subtle, but measurable decreases in the linear rate of change of 18F-FDG nSUV growth rate averaging -4.5% per day (P = .01) and change in 18F-FDG uptake was correlated with change in tumor growth rate (R2 = 0.4719, P = .0195). No metabolic (PERCIST) or radiographic (RANO) responses were seen, and no improvements in PFS or OS were observed.Conclusions This study demonstrated the feasibility of using FDG PET as a clinically reliable imaging biomarker for assessing EGFR inhibition in GBM, while revealing osimertinib's limited impact on both metabolic activity and tumor growth in GBM, findings that were concordant between preclinical and clinical observations.

High-grade glial tumors represent the most morbid form of brain cancer [...]

Elevated hexosamine biosynthesis fuels tumor growth by facilitating protein and lipid glycosylation. But which enzyme in this pathway is better to serve as an antitumor target remains unclear. Here, we revealed that targeting GFAT1, the rate-limiting enzyme in hexosamine synthesis, exhibits limited inhibitory effects on glioblastoma (GBM), the most lethal brain tumor. This outcome is due to the compensation of NAGK-mediated hexosamine salvage pathway. Unexpectedly, inhibiting PGM3, which controls the flux of both de novo hexosamine synthesis and salvage pathways, down-regulates the expression of other enzymes in this pathway and suppresses SREBP-1, a critical lipogenic transcription factor, effectively inhibiting GBM growth. Unexpectedly, SREBP-1 transcriptionally up-regulates the expression of hexosamine synthesis enzymes, while inhibition of these enzymes in turn down-regulates SREBP-1 activation via reducing N-glycosylation of its transporter, SCAP. Our study identified PGM3 as a promising target for treating GBM. Its inhibition disrupts the SREBP-1 activation-hexosamine synthesis positive feedback regulation to effectively eliminate GBM cells.

In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity. A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin–eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations. Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R2 = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm2), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R2 = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations. MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

BACKGROUND:This study explored MRI characteristics at the time of tumor progression to study pathologically confirmed MT in IDHm 1p/19q-intact astrocytomas (IDHm-A) and IDHm 1p/19q-co-deleted oligodendrogliomas (IDHm-O). METHODS:N = 64 patients with initial pathological grade 2 IDH-mutant glioma diagnosis who underwent repeated tissue sampling and were classified as pathologically confirmed MT (n = 35) or non-MT (n = 29) with available presurgical anatomical (n = 64), diffusion-weighted (n = 61), and dynamic susceptibility contrast perfusion MRI (n = 53) were retrospectively studied. Measurable contrast enhancement (> 1000 mm3), tumor volume, tumor growth rate, sphericity, median apparent diffusion coefficient (ADC), and normalized relative cerebral blood volume (nrCBV) were compared between MT vs non-MT IDHm-A and IDHm-O. RESULTS:81% of contrast-enhancing IDHm-A and 100% of contrast-enhancing IDHm-O demonstrated MT, while 41% of IDHm-A and 62% IDHm-O exhibited both nonenhancing tumor progression and MT. Tumor volumes were significantly larger in patients with MT compared to non-MT groups for IDHm-A (P = .02) and IDHm-O (P = .04). T2/FLAIR tumor volume growth rate was significantly higher (P = .003), nrCBV was significantly higher (P = .002), and ADC trended lower (P = .06) in MT vs non-MT IDHm-A. There were no significant differences in growth rate, ADC, nrCBV, or sphericity when comparing MT vs non-MT IDHm-O (P > .05). CONCLUSIONS:Many MT IDHm gliomas remain nonenhancing. Growth rate, diffusion, and perfusion MRI show differences between MT and non-MT in IDHm-A but not IDHm-O, which may reflect the different tumor biology of these IDHm molecular subtypes and their need for separate imaging biomarkers. Tumor volumes can help determine MT for both IDHm-A and IDHm-O.

Recurrent glioblastomas showing a survival benefit from anti-VEGF agents are known to exhibit a distinct diffusion MRI phenotype. We aim to characterize advanced imaging features of this glioblastoma subset. MRI scans from 87 patients with IDH-wildtype glioblastoma were analyzed. All patients had completed standard chemoradiation and were anti-VEGF-naïve. Contrast-enhancing tumor segmentations were used to extract: the lowest peak of the double gaussian distribution of apparent diffusion coefficient values (ADCL) calculated from diffusion MRI, relative cerebral blood flow (rCBV) values from perfusion MRI, MTRasym @ 3ppm from pH-weighted amine CEST MRI, quantitative T2 and T2* relaxation times (qT2 and qT2*), T1w subtraction map values, and contrast-enhancing tumor volume. Lesions were categorized as high- or low-ADCL using a cutoff of 1240 µm2/s, according to previous studies. High-ADCL lesions showed significantly lower rCBV (1.02 vs. 1.28, p = 0.0057), higher MTRasym @ 3ppm (2.36

Pamiparib, a small-molecule poly (ADP-ribose) polymerase (PARP) 1/2 inhibitor, demonstrates strong PARP-DNA complex trapping, antitumor activity, and blood–brain barrier penetration. This phase Ib/II dose-escalation study (NCT03150862) investigated pamiparib’s tolerability/safety and efficacy when combined with radiotherapy and/or low-dose temozolomide (TMZ) in patients with treatment-naïve (Arms A and B) and recurrent/refractory (Arm C) glioblastoma. The recommended phase II dose for Arm A was pamiparib 60 mg twice daily (BID) for 6 weeks with 6–7 weeks radiotherapy; the recommended dose for Arm C was pamiparib 60 mg BID plus 60 mg TMZ (days 1–7; 28-day cycle). The Arm B escalation cohort completed enrollment; the expansion cohort was not opened. Grade ≥3 treatment-emergent adverse events (TEAEs)/serious TEAEs were observed in 55.0%/36.7% (Arm A), 44.4%/22.2% (Arm B), and 66.0%/38.3% (Arm C) of patients. Disease control and objective response rates were 67.9% and 11.3%, respectively, for treatment-naïve patients in the dose-escalation and -expansion studies, and 40.9% and 13.6%, respectively, for recurrent/refractory patients. Median overall survival for treatment-naïve MGMT unmethylated patients was 12.8 months and 7.3 months for recurrent/refractory MGMT methylated and unmethylated patients. Pamiparib with radiotherapy and/or low-dose TMZ was tolerable for treatment-naïve or recurrent/refractory glioblastoma. Treatment-emergent cytopenia was manageable and reversible with dose reductions/interruptions. Combination regimens demonstrated antitumor activity.

Phenotypic plasticity plays a pivotal role in cancer, enabling tumor cells to adapt to environmental pressures and evade therapeutic interventions by transitioning between distinct cellular states. However, the contribution of phenotypic plasticity to adaptive drug resistance in glioblastoma (GBM), one of the most lethal of all cancers, remains poorly understood. In this study, we identify that GBM tumor-initiating cells resembling normal radial glia (RG), which occupy the apex of normal neurodevelopment, are driven by aberrant epidermal growth factor receptor (EGFR) signaling. Using a suite of patient-derived GBM models, we demonstrate through global proteomics and single-cell RNA sequencing that pharmacological inhibition of EGFR triggers a lineage transition toward neuronal and oligodendrocyte progenitor (OPC)-like states. This shift is accompanied by activation of oncogenic RAS-MAPK signaling – despite robust and durable inhibition of EGFR activation – and is further modulated by brain microenvironmental cues, including synaptic and calcium-mediated signaling programs. Dual inhibition of EGFR and RAS-MAPK with novel, tumor-selective small molecules blocks these phenotypic transitions and enhances GBM cell death in EGFR-mutated GBM models. To determine the subpopulation dynamics of RAS-MAPK signaling within GBM neurodevelopmental lineages, we develop DENALI (Dual-Expression Nuclear reporter of ERK Activity and Lineage Identity) – a novel, high-complexity barcoded lentiviral vector and integrative fluorescence reporter system. Using DENALI, we investigate the clonal mechanisms driving lineage plasticity in GBM following oncogenic EGFR inhibition and couple adaptive RAS signaling programs to the emergence of neuronal and OPC-like states under EGFRi therapy. Together, our findings establish neurodevelopmental lineage plasticity as a key driver of adaptive resistance in GBM and support dual-inhibition strategies to improve therapeutic outcomes in patients with GBM tumors.