Canoll, Peter

PURPOSE Age ≥40 years is regarded as a high-risk feature and an indication for adjuvant chemoradiotherapy for patients with lower-grade glioma in clinical practice guidelines. It is unclear whether age remains a relevant prognostic factor for contemporary definitions of lower-grade gliomas in the molecular era. METHODS The Prospective Gliomas Research (PROGRES) database contains individual patient-level data from 11 prospective clinical trials or observational registries of histologically defined lower-grade 2-3 oligodendroglioma or astrocytoma. We determined the association of age (18-39 years v ≥40 years) with progression-free survival (PFS) stratified by isocitrate dehydrogenase 1 or 2 ( IDH1/2 ) status, using log-rank tests and Cox regression models. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] database). RESULTS We identified 1,619 and 1,292 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH -wildtype tumors were more common in patients 40 years and older (38% v 5%, odds ratio: 11.3 [95% CI, 6.5 to 19.7]). Age was associated with PFS in IDH -wildtype (5-year PFS for ≥40 v 18-39 years: 6% v 24%, hazard ratio [HR], 1.74 [95% CI, 1.21 to 2.50]) but not in IDH -mutant glioma (60% v 59%, HR, 0.89 [95% CI, 0.76 to 1.05], P interaction < .001). In IDH -wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% v 28%), EGFR amplification (41% v 15%), and chromosome +7/–10 alteration (57% v 25%). In a pooled analysis of four clinical trials, age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone for IDH -mutant glioma. CONCLUSION In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral of adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

Abstract PURPOSE Age over 40 years is commonly regarded as a “high risk” feature and potential indication for adjuvant chemoradiotherapy for patients with lower grade glioma in most clinical practice guidelines. However, these recommendations are based on retrospective observations and their subsequent incorporation into legacy clinical trials. It is unclear whether age remains a relevant prognostic factor in the molecular era. METHODS The Prospective Gliomas Research [PROGRES] Database contains individual patient-level data from 11 prospective clinical trials or observational registries, including seven international trials from the RTOG, EORTC, or Alliance cooperative groups. We determined the association of age with progression-free survival (PFS) in low grade or anaplastic glioma, stratified by isocitrate dehydrogenase (IDH1/2) status. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] Database). RESULTS We identified 1,619 and 1,271 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH-wildtype tumors were more common in patients &gt;40 years (38% vs, 5%, odds ratio: 11.3 [95%CI 6.5 – 19.7]). Age was associated with PFS in IDH-wildtype (5-year PFS: 6% vs, 24%, P =.003) but not in IDH-mutant glioma (60% vs, 59%, P = 0.22, P value of interaction &lt;.001). In IDH-wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% vs, 28%), EGFR amplification (41% vs, 15%), and chromosome +7/-10 alteration (57% vs, 25%). Age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone in IDH-mutant glioma. CONCLUSION Older age is a putative surrogate for IDH-wildtype status, but was not an independent predictor of progression in IDH-mutant lower grade glioma and did not predict benefit from chemoradiotherapy. In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

Abstract The intra- and inter-patient heterogeneity in high-grade glioma (HGG) continues to contribute to its poor prognosis. Clinical biopsies are often harvested from limited regions and typically are not image localized. Thus, they fail to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in tumor development. It is important to gain an understanding of these subpopulation ecologies, their spatial resolution, and interactions between them that may then be exploited for future therapeutic benefit. Further, these may differ by patient characteristics such as sex, age at diagnosis and treatment status. Using an ongoing image-localized biopsy collection protocol, we have so far evaluated the bulk transcriptomics of 202 multi-regional biopsies from 58 patients to characterize HGG heterogeneity. These samples were processed through Monocle, a reverse graph embedding algorithm that groups samples into states and orders them along developmental trajectories. Deconvolution methods were previously used to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample, which we have now overlaid on the Monocle graph. Monocle classified HGG into 4 main states along a three-pronged trajectory. These states reveal distinct population ecologies with associated enriched gene pathways. We also note significant immune pathway enrichments that differ between state and patient-reported sex. Together, these algorithms reveal a simple transcriptomic trajectory that helps us understand the development and evolution of HGG. Characterizing the in vivo diversity within and between high grade gliomas is important for understanding prognosis, stratifying future treatments and ultimately improving patient outcome.

Abstract Magnetic resonance imaging (MRI) is key to clinically managing brain tumor patients, however connecting the biology to imaging remains challenging. We previously developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Using this model, we previously identified the fatty acid metabolism (FAM) and oxidative phosphorylation (OxPhos) pathways as sex-distinct, with both pathways amplified for high edema in males and low edema in females. The purpose of this project was to further delineate sex-distinct biology associated with MRI-estimated brain tumor edema abundance. We analyzed 179 multiregional samples (Female: 75; Male: 104) from 55 high grade glioma patients (Female: 21; Male: 34) for bulk RNA-Seq. Patients’ pre-surgical multiparametric MRIs were preprocessed and segmented for abnormal regions and normal tissue. Utilizing the segmentations and preprocessed images we estimated the relative fractions of extracellular and intracellular space based on the edema mathematical model. Samples were characterized by their edema scores, which were analyzed using differential expression for high and low edema, gene set enrichment analysis (GSEA) using MSigDB hallmarks, and leading edge interpretation. Based on transcriptomic leading edge analyses of high and low edema samples from sex-separated cohorts, we identified common and sex-distinct enrichment of the FAM and OxPhos pathways underlying edema patterns. Of the OxPhos pathway leading edge genes, 45 were common, 36 were unique to females, and 66 were unique to males. Of the FAM pathway leading edge genes, 8 were common, 39 were unique to females, and 29 were unique to males. Notably, expression of both IDH1 and IDH2 were increased for males in regions of high edema in the OxPhos pathway. IDH3a was decreased for females in regions of low edema in the OxPhos pathway. These data suggest that there may be sex-distinct metabolism underlying MRI measurable edema formation.

Abstract Background IDH-wild type (-wt) status is a prerequisite for the diagnosis of glioblastoma (GBM); however, IDH-wt gliomas with low-grade or anaplastic morphology have historically been excluded from GBM trials and may represent a distinct prognostic entity. While alkylating agent chemotherapy improves overall survival (OS) and progression-free survival (PFS) for IDH-wt GBM and also IDH-mutant gliomas, irrespective of grade, the benefit for IDH-wt diffuse histologic lower-grade gliomas is unclear. Methods We performed a meta-analysis of randomized clinical trials for World Health Organization (WHO) grades 2–3 gliomas (2009 to present) to determine the effect of alkylating chemotherapy on IDH-wt and -mutant gliomas using a random-effects model with inverse-variance pooling. Results We identified 6 trials with 1204 patients (430 IDH-wt, 774 IDH-mutant) that evaluated alkylating chemoradiotherapy versus radiotherapy alone, allowing us to perform an analysis focused on the value of adding alkylating chemotherapy to radiotherapy. For patients with IDH-wt tumors, alkylating chemotherapy added to radiotherapy was associated with improved PFS (HR:0.77 [95% CI: 0.62–0.97], P = .03) but not OS (HR:0.87 [95% CI: 0.64–1.18], P = .17). For patients with IDH-mutant tumors, alkylating chemotherapy added to radiotherapy improved both OS (HR:0.52 [95% CI: 0.42–0.64], P &lt; .001) and PFS (HR = 0.47 [95% CI: 0.39–0.57], P &lt; .001) compared to radiotherapy alone. The magnitude of benefit was similar for IDH-mutant gliomas with or without 1p19q-codeletion. Conclusions Alkylating chemotherapy reduces mortality by 48% and progression by 53% for patients with IDH-mutant gliomas. Optimal management of IDH-wt diffuse histologic lower-grade gliomas remains to be determined, as there is little evidence supporting an OS benefit from alkylating chemotherapy.

Abstract Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of glioblastoma (GBM). This heterogeneity is further exacerbated during GBM recurrence, as treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse Magnetic Resonance Imaging (MRI) with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically-informed neural network model, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet offers valuable insights into the integration of multiple implicit and qualitative biological domain knowledge, which are challenging to describe in mathematical formulations. BioNet performs significantly better than a range of existing methods on cross-validation and blind test datasets. Voxel-level prediction maps of the gene modules by BioNet help reveal intratumoral heterogeneity, which can improve surgical targeting of confirmatory biopsies and evaluation of neuro-oncological treatment effectiveness. The non-invasive nature of the approach can potentially facilitate regular monitoring of the gene modules over time, and making timely therapeutic adjustment. These results also highlight the emerging role of ML in precision medicine.

Abstract Magnetic resonance imaging (MRI) is a centerpiece of clinical management of brain tumor patients, yet the biology underlying even commonly interpreted imaging changes is unclear. The purpose of this project was to understand what biology is associated with brain tumor edema abundance and if that association was sex distinct. We have developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Multiparametric MRIs were preprocessed with bias field correction and intensity normalization. The images were co-registered and segmented for abnormal regions and normal tissue. The segmentations and preprocessed images were utilized in the edema mathematical model to estimate the fraction of extracellular space (Fecs) and fraction of intracellular space (Fics). Through an ongoing image-localized biopsy collection protocol, we analyzed 179 samples (Female: 75; Male: 104) from 55 patients (Female: 21; Male: 34) for bulk RNA-Seq. Samples were characterized by their edema Fecs scores and underwent differential expression, gene set enrichment analysis using MSigDB hallmarks and cellular deconvolution using CIBERSORTx. Through differential expression and gene set enrichment analyses of these spatial biopsies, we have identified sex-distinct gene expression and pathways corresponding to regions of high vs low edema. Within highly edematous regions, male samples were amplified for respiratory pathways, while female samples were suppressed. Connecting changes on MRIs with molecular markers from image-localized biopsies provides an opportunity to identify the biological drivers of brain tumor associated imageable edema. Specifically, elucidating the sex-distinct patterns connecting changes on MRI with cellular populations and molecular pathways could be used to better interpret imaging of patients and interpreting treatment response. Citation Format: Pamela R. Jackson, Lee Curtin, Sara Ranjbar, Kyle W. Singleton, Maciej M. Mrugala, Richard S. Zimmerman, Bernard R. Bendok, Peter Canoll, Kristin R. Swanson. Sex-distinct patterns of molecular pathways associated with brain tumor edema [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6168.

Isocitrate Dehydrogenase-1 (IDH1) is commonly mutated in lower-grade diffuse gliomas. The IDH1R132H mutation is an important diagnostic tool for tumor diagnosis and prognosis; however, its role in glioma development, and its impact on response to therapy, is not fully understood. We developed a murine model of proneural IDH1R132H-mutated glioma that shows elevated production of 2-hydroxyglutarate (2-HG) and increased trimethylation of lysine residue K27 on histone H3 (H3K27me3) compared to IDH1 wild-type tumors. We found that using Tazemetostat to inhibit the methyltransferase for H3K27, Enhancer of Zeste 2 (EZH2), reduced H3K27me3 levels and increased acetylation on H3K27. We also found that, although the histone deacetylase inhibitor (HDACi) Panobinostat was less cytotoxic in IDH1R132H-mutated cells (either isolated from murine glioma or oligodendrocyte progenitor cells infected in vitro with a retrovirus expressing IDH1R132H) compared to IDH1-wild-type cells, combination treatment with Tazemetostat is synergistic in both mutant and wild-type models. These findings indicate a novel therapeutic strategy for IDH1-mutated gliomas that targets the specific epigenetic alteration in these tumors.

Abstract Glioblastoma (GBM) is the most aggressive and most common primary malignant brain tumor in adults, with a poor median survival time of 15 months. One of the key challenges in treating GBM is its highly heterogeneous nature, with multiple distinct subtypes that have been shown to occur on both inter- and intra-patient levels. Three main classifications, known as classical/proliferative, mesenchymal and proneural have become commonly demonstrated phenotypes. The cell cycle is a fundamental and highly conserved process that controls faithful division of cells; dysregulation of the cell cycle is known to be a key driver in many cancers. However, how the cell cycle is differently regulated between these subtypes has not been well classified in vivo. We investigate these three GBM subtypes using a recently published single nucleus RNAseq (snRNAseq) data set. We compare cell cycle regulation/dysregulation among these three subtypes using Tricycle, an R/Bioconductor package that utilises dimension reduction via principal component analysis and transfer learning to infer cell cycle position from any snRNAseq data set. We find that the classical GBM subtype has the highest proportion of actively dividing cells (cells in: S/G2/M phases), while the mesenchymal and proneural subtypes have a very low proportion of actively dividing cells. This supports the idea of a proliferation-migration dichotomy between GBM subtypes. We use this proportion of actively proliferating cells to calibrate a minimal spatiotemporal mathematical model for GBM tumor growth that accounts for the differences in cell cycle regulation between these three GBM subtypes.