Bendok, Bernard R.

Abstract Chordoid meningiomas, rare meningioma variants, are characterized by their histopathological features and clinical behavior resembling that of other chondroid/myxoid neoplasms. We present a case of pathology-proven chordoid meningioma imaged with both 68Ga-DOTATATE and 18F-FDG PET images during a complicated postoperative course with multiple episodes of local recurrence and, ultimately, extracranial metastasis. This case underscores the aggressive behavior of chordoid meningiomas while highlighting how molecular imaging plays an important role in clinical monitoring and guidance of management.

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.

Medical devices actuated by external magnetic fields can create opportunities for clinical adoption of precision telesurgery.

Abstract It has been widely acknowledged that limited access to the human brain, along with the importance of its structures, presents significant challenges in obtaining brain tumor tissue. This poses difficulties for clinical decision-making, as healthcare professionals often have to rely solely on imaging to address complex problems. There is currently no definitive method to make these decisions other than relying on informed yet subjective human judgment. That said, imaging abnormalities are a result of a presence of particular tumor-related biology. Because of this, and the heterogeneity frequently observed in brain tumors, we have developed an image-localized biopsy protocol that has been employed for research purposes at Mayo Clinic Arizona for over five years. This multidisciplinary approach involves the collaborative efforts from neurosurgeons, neuro-oncologists, scrub-techs, nurses, radiologists, as well as multiple research labs, all working within a well-established pipeline. The implementation of this protocol has allowed us to obtain image-localized biopsies during surgical procedures, enabling the correlation of local imaging findings with corresponding tissue features through the utilization of machine learning models. The primary objective of this endeavor is to enhance our understanding of the underlying biological mechanisms of brain tumors based on patients' imaging data. This protocol has been successful for our research purposes, and with minor adjustments, it can be readily adopted by any hospital in order to collect more data about these incredibly heterogeneous diseases. While the use of neuronavigation during surgery has become commonplace in many medical institutions, localization of tissue samples is not. Utilizing pre-existing infrastructure in the operating room is a small burden with big potential positive outcomes. Sample locations can be useful to clinicians during tumor board discussions, to better assess disease heterogeneity. These minor refinements in the surgical workflow have the capacity to provide clinicians with additional clinically-relevant data without disrupting patient care.

Abstract PURPOSE 18F-DOPA is sensitive/specific for identifying areas of biologically aggressive disease for glioblastoma. We report the impact of 18F-DOPA on radiation treatment planning from a phase II study. METHODS Gross target volume (GTV) was defined by the boolean of high-risk PET (tumor/normal brain SUV > 2.0) and MRI T1 contrast enhancement (MRI-CE) including surgical cavity. The clinical target volume (CTV) included the GTV with 1cm margin. Using proton beam therapy, GTV ≤ 65 cc was treated with 25 (CTV), 30 (MRI-CE), and 35 (PET) GyE over 5 fractions with a simultaneous integrated boost. GTV > 65 cc received 30 (CTV), 35 (MRI-CE), and 40 (PET) GyE over 10 fractions. Radiation field design was compared to traditional 1.5cm CTV margin on MRI-CE. RESULTS Between May 2019 and October 2021, 39 patients were treated. The median MRI-CE tumor diameter was 5.2cm. The median PET volume was 7.9cc (range 0–64.9cc). The median MRI-CE volume was 35.1cc (range 8.6–114cc). High-risk PET was contained within: MRI-CE 18/39 (46%), MRI-CE + T2W FLAIR 13/39 (33%), MRI-CE + T2W FLAIR + outside GTV 4/39 (10%), outside GTV 2/39 (5%) and no uptake 2/39 (5%). Utilizing this planning technique, there was a 26% volume reduction in CTV to traditional 1.5 cm margin on MRI-CE. PET imaging in treatment planning allowed inclusion of high-risk disease that would have been excluded on traditional CTV in 23% of cases (range 1.6-33.8cc). PET was more predictive [HR 1.03 (95% CI 1.01, 1.05) p = 0.002] than MRI [HR 1.01 (95% CI 1.00, 1.03) p = 0.02] for death with each cc increasing risk of death by 3.1%. CONCLUSIONS The incorporation of 18F-DOPA in treatment planning yields a more targeted approach with reductions in both normal brain treated and risk of geographic miss compared to traditional CTV margins.

Abstract Sequencing efforts have identified key oncogenic drivers and transcriptomic subtypes of high-grade gliomas (HGGs), but this knowledge has not resulted in individualized therapy or improved patient outcomes. Here, we used a functional genomics approach combining integrated genomic/transcriptomic analyses with patient-derived 3D micro-cancers (mCancers) to rapidly identify individualized therapeutic vulnerabilities and determine predictive response biomarkers. 30 primary/recurrent HGGs, including 20 glioblastomas, 5 astrocytomas, and 5 oligodendrogliomas, were included for drug testing. mCancers recapitulated the structural and molecular alterations, cellularity, and heterogeneity, of parental patient tumors. A total of 119 single-agent drugs and 183 drug combinations were tested, while a pre-selected drug panel comprising 25 glioma-relevant treatment strategies was tested across all cases. In general, mCancer efficacy corresponded well to historical responses to previously tested agents (EGFR, HDAC inhibitors, etc.). Combined, strong efficacy (i.e. >70% growth inhibition at Cmax) of single treatments targeting deregulated receptor tyrosine kinase (RTK), p53/apoptosis, cell cycle, DNA damage repair, IDH, or telomere maintainance pathways, was observed in 14 out of 30 cases. Surprisingly, transcriptional signatures, but not specific genetic alterations, were found to stratify drug responses. For example, mCancers enriched for the most aggressive mesenchymal (MES) and glycolytic/plurimetabolic (GPM) transcriptional subtypes exhibited stronger response to the BET inhibitor JQ1 (and other clinical BET inhibitors) and the DNA-PKcs/mTOR dual inhibitor CC-115. This therapeutic vulnerability was associated with enrichment of YAP/TAZ signaling, and the glioblastoma stem cell G-STEM signature. Notably, individual combination strategies were effective across tumors, with strong responses ranging from 38% to 77% of all tumors tested. Overall, this study underscores the value of functional genomics in identifying novel vulnerabilities of HGGs and associated response biomarkers. It also highlights the need for further clinical validation of several novel therapies and especially drug combination strategies with strong efficacy in the ex vivo setting.

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.