tbuckley/cabot-search huggingface dataset:
NIMG-01. RADIOMIC DATA ANALYSIS WITH ENSEMBLE MACHINE LEARNING TECHNIQUES PREDICT GRADE, HISTOPATHOLOGIC SUBTYPE, AND 1P/19Q CO-DELETION STATUS IN PATIENTS WITH LOW-GRADE GLIOMAS
Authors: Suhrud Panchawagh
Journal: Neuro-Oncology (2024)

Abstract Low-grade gliomas (LGGs) are a diverse group of primary brain tumors characterized by their variable prognosis and treatment response. Traditionally, the management of LGGs has relied on histopathological analysis, which sometimes offers limited insight into tumor behavior and treatment outcomes. Recent advances in radiomics have enabled the extraction of detailed quantitative features from routine imaging, providing a non-invasive method to assess tumor characteristics. These radiomic features can capture underlying tumor heterogeneity more comprehensively than conventional methods. Integrating radiomic data with ensemble machine learning techniques offers a promising approach to enhance the prediction of tumor grade, histopathologic subtype, and genetic alterations such as 1p/19q co-deletion status in LGGs, potentially leading to more tailored therapeutic strategies. We combined radiomic-genomic data from 159 magnetic resonance imaging (MRI) scans from the updated LGG-1p19qDeletion dataset (Erickson 2020) and 114 MRI scans from the ReMIND dataset (Juvekar 2024). Expert radiologists performed segmentations. A total of 2446 extracted radiomic features were initially extracted. Feature selection was performed using wrapper- and filter-based techniques with cross-validation. Prediction models were built using machine learning techniques, including random forest and extreme gradient boosting. Using Random Forest, we achieved high accuracy in predicting histopathologic subtype (88.14%, AUC 96.68%) with precision of 87.86% and Dice score of 87.88%. For grade prediction (accuracy 76.19%, AUC 91.84%), precision was 73.91%, and the Dice score was 77.27%. For 1p/19q co-deletion status (accuracy 75.61%, AUC 82.74%), precision was 77.78%, and the Dice score was 73.68%. Our study establishes a robust radiomic workflow to predict tumor grade, histopathologic subtype, and 1p/19q co-deletion status in low-grade gliomas, enhancing diagnostic accuracy and treatment personalization. This methodology can be applied clinically to improve the stratification of treatment plans for low-grade gliomas, potentially leading to effective, personalized therapy options. Using multicentric data potentially improved external validity, something which past research lacked. Future research should focus on validating and deploying this model using observational multicentric study designs.
EPID-32. ELEVATED SERUM IGE IS LINKED TO IMPROVED GLIOBLASTOMA SURVIVAL, PARTICULARLY AMONG FEMALES
Authors: Stephen Francis, Geno Guerra, Taishi Nakase, Linda Kachuri, Lucie McCoy, Helen Hansen, Joseph Wiemels, John Wiencke, Annette Molinaro, Margaret Wrensch
Journal: Neuro-Oncology (2024)

Abstract Prior epidemiological research shows a correlation between serum immunoglobulin E (IgE) levels and decreased risk of glioma. However, the relationship between IgE and the prognosis of glioma remains poorly understood. This study seeks to investigate how factors such as sex, tumor subtype, and IgE class influence the association of serum IgE levels with both glioma risk and survival outcomes. METHODS- In this investigation, we conducted a case-control study utilizing participants enrolled in the UCSF Adult Glioma Study from 1997 to 2010. We measured serum IgE levels for total, respiratory, and food allergy in adults diagnosed with glioma (n=1,696), and controls (n=1,135) matched based on age, sex, and race/ethnicity. Logistic regression, adjusted for patient demographics, was employed to evaluate the correlation between IgE levels and glioma risk. Multivariable Cox regression, adjusted for patient-specific and tumor-specific factors, was utilized to compare survival outcomes between the elevated and normal IgE groups. RESULTS- Increased levels of total IgE were linked to a decreased risk of both IDH wildtype (OR=0.65, 95% CI: 0.54-0.78) and IDH mutant glioma (OR=0.65, 95% CI: 0.50-0.85). In multivariable Cox regression analysis, heightened respiratory IgE levels were associated with enhanced survival among individuals with IDH wildtype glioma (HR=0.78, 95% CI: 0.67-0.91). Notably, this improved survival was more pronounced in females (HR=0.71, 95% CI: 0.53-0.96) compared to males (HR=0.80, 95% CI: 0.66-0.97), resulting in median survival improvements of 6.2 months (P&amp;lt;.001) and 1.6 months (P=0.003), respectively. CONCLUSION- Increased levels of serum IgE were associated with reduced risk of glioma overall, and improved prognosis for IDH wildtype glioma, which was particularly strong in females. These findings hint at potential sexual dimorphism in the antitumor activity of IgE-mediated immune responses, suggest an important role for IgE assessment in glioblastoma clinical trial design, and provide justification for investigations into IgE-based therapeutics.
DNAR-02. M33 CONDENSES CHROMATIN THROUGH NUCLEAR BODY FORMATION AND METHYLATION OF BOTH HISTONE H3 LYSINE 9 AND LYSINE 27
Authors: Chiung-Chyi Shen, Wen-Ming Yang, Yi-Chin Yang
Journal: Neuro-Oncology (2024)

Abstract Heterochromatin, a type of condensed DNA in eukaryotic cells, has two main categories: Constitutive heterochromatin, which contains H3K9 methylation, and facultative heterochromatin, which contains H3K27 methylation. Methylated H3K9 and H3K27 serve as docking sites for chromodomain-containing proteins that compact chromatin. M33 (also known as CBX2) is a chromodomain-containing protein that binds H3K27me3 and compacts chromatin in vitro. However, whether M33 mediates chromatin compaction in cellulo remains unknown. Here we show that M33 compacts chromatin into DAPI-intense heterochromatin domains in cells. The formation of these heterochromatin domains requires H3K27me3, which recruits M33 to form nuclear bodies. G9a and SUV39H1 are sequentially recruited into M33 nuclear bodies to create H3K9 methylated chromatin in a process that is independent of HP1α. Finally, M33 decreases progerin-induced nuclear envelope disruption caused by loss of heterochromatin. Our findings demonstrate that M33 mediates the formation of condensed chromatin by forming nuclear bodies containing both H3K27me3 and H3K9me3. Our model of M33-dependent chromatin condensation suggests H3K27 methylation corroborates with H3K9 methylation during the formation of facultative heterochromatin and provides the theoretical basis for developing novel therapies to treat heterochromatin-related diseases. Keywords: H3K27 methylation; H3K9 methylation; Heterochromatin; M33 (CBX2); Nuclear body (NB); Progerin.
BIOM-54. AI-DRIVEN RISK-OF-PROGRESSION (AIRIP) CLASSIFIER FOR DISTINGUISHING RECURRENT BRAIN METASTASES FROM RADIATION TREATMENT EFFECT: A MULTI-INSTITUTIONAL COMPARATIVE STUDY WITH ADVANCED MULTIMODAL IMAGING
Authors: Hyemin Um, Marwa Ismail, Virginia Hill, Sushant Puri, Jennifer Yu, Lan Lu, Ameya Nayate, Lisa Rogers, Prateek Prasanna, Mainak Bardhan, Chengnan Li, Mustafa Basree, Andrew Baschnagel, Alan McMillan, Ankush Bhatia, Manmeet Ahluwalia, Michael Veronesi, Pallavi Tiwari
Journal: Neuro-Oncology (2024)

Abstract Following radiation therapy, a significant challenge in brain metastases (BM) management is differentiating radiation-induced-treatment effect (TrE) from tumor recurrence (TuR). TrE can be indistinguishable from TuR using conventional MRI. Advanced imaging techniques (e.g., perfusion MRI, PET/MRI) are not consistently used, and the standardized Response Assessment in Neuro-Oncology for brain metastases (RANO-BM) is sensitive to inter-reader variability. The performance of an artificial intelligence (AI)-driven risk-of-progression (AiRiP) classifier, which has been shown to capture pathophysiologic differences between TrE and TuR on routine MRI, was compared to that of clinical assessments and advanced imaging methods, in a multi-institutional setting. A total of n=261 lesions with pathologically-confirmed diagnoses in n=189 patients were analyzed. 115 lesions (73 TuR, 42 TrE) from site 1, 86 lesions (38 TuR, 48 TrE) from site 2, and 60 lesions (33 TuR, 27 TrE) from site 3 were used for training and testing the AiRiP-model. Gd-T1w, T2w, FLAIR MRI were preprocessed, and lesions were segmented by experts. Texture features (n=856) were extracted from each lesion. Random-forest classifier was employed for 3-fold cross-validation. Top-performing AiRiP-features, RANO-BM criteria, perfusion MRI and PET/MRI were evaluated in a sub-group analysis. For n=51 lesions on the test-set (site 3), 14 were classified as stable disease and 37 as TuR using RANO-BM (accuracy=54.1%). AiRiP-model achieved an accuracy of 76.5% on the same test-set and accurately classified 78.6% of the stable lesions as TrE or TuR. For another subset of lesions (n=27) on the same test-set, perfusion MRI and AiRiP-model achieved an accuracy of 59.3% and 70.4%, respectively. Lastly, for a subset of lesions (n=35) on the test-set (site 2), multimodal (perfusion, PET) imaging and AiRiP-model accurately classified 60% and 74.3% of lesions, respectively. 15 lesions were considered indeterminate via multimodal imaging, 73.3% of which AiRiP-model accurately classified as TrE or TuR. Our results suggest AI-driven models on conventional MRI may reliably distinguish TuR from TrE.
PATH-62. CORRELATES OF NEURAL SIGNALING ELUCIDATE SURVIVAL BENEFIT IN GLIOMAS
Authors: Ihika Rampalli, Kalman Katlowitz, Malcolm McDonald, Collin English, Nabeel Diab, Zachary Perry, Ali Jalali, Ganesh Rao, Benjamin Deneen, Jacob Mandel
Journal: Neuro-Oncology (2024)

Abstract Recent literature has highlighted neural signaling markers (SM) as drivers for synaptogenesis, invasion, and proliferation in gliomas. Further work on elucidating key mutations can provide improved prognostication and potential treatment targets. Our objective is to identify correlates of neural signaling in relation to glioma histopathology and patient outcomes. We used a combination of bulk RNA sequencing (RNA-seq) and a DNA gene panel (DPG) by Tempus next-generation sequencing. Differential gene expression (DGE) was performed using DESeq2 after subsetting genes for neural SM. Dimensionality reduction was applied through t-SNE projections. Variant calls were analyzed using EnsemblVEP and maftools. Survival analysis was determined through log-rank tests. All analyses were conducted with R. 176 and 184 patient samples were analyzed for RNA-seq and DPG, respectively. t-SNE of DGE highlights two distinct functional clusters with no correlation to WHO classification status (Chi-squared p = 0.39) The median survival for the big cluster was observed at 957 days, while no median survival was reached even after 4,420 days of follow-up in the small cluster (p = 0.01). This contrast remains consistent when examining subsets of gliomas with unmethylated MGMT promoter (p = 0.02) and glioblastomas (p = 0.02), but not others such as IDH mutation status and low-grade gliomas (p &amp;gt; 0.1). Genes upregulated in the small cluster had equal representation of synaptic and paracrine SM (45% and 55%), while the big cluster had a greater representation of paracrine SM (69% and 31%). DPG of low-grade gliomas highlighted 3 somatic signaling mutations (FGFR1, PTPRD, and SLIT2) that are more prevalent in the big cluster. Functional classification based on neural signaling markers can elucidate a novel set of biomarkers that may impact survival in glioma patients. Further studies are required to validate such findings and develop therapeutic targets.
CNSC-42. PROGNOSTIC VALUE OF INTERLEUKIN-6 IN PATIENTS WITH GLIOBLASTOMA
Authors: Kyung Hwan Kim
Journal: Neuro-Oncology (2024)

Abstract Interleukin-6 (IL-6), a pro-inflammatory cytokine, has been implicated in the invasiveness and progression of glioma. However, the association between serum IL-6 levels at specific time points during treatment and patient prognosis remains unclear. This study was aimed to investigate the prognostic value of IL-6 measured at milestones in the treatment of patients with glioblastoma. Between January 2021 and December 2022, consecutive patients with newly diagnosed glioblastoma were enrolled. Serum IL-6 concentrations were measured at different time points, including preoperative, pre- and post-radiation period, and two-month intervals thereafter. Patients with incomplete data were excluded, and increase of IL-6 values due to the overlapping with systemic infection were also excluded from the analysis. During the study period, 26 patients and 270 IL-6 levels at different time points were evaluated. The median follow-up time was 11 months. Tumor progression was evident in 19 patients (73.1%) and 13 patients (50%) were expired. Preoperative and pre-radiation IL-6 values were available in 20 and 23 patients with the median 5.25 and 4.5 pg/mL, respectively. With the cut-off value of pre-radiation IL-6 at 4.5 pg/mL, high IL-6 group showed significantly worse progression-free survival and overall survival than low IL-6 group (p = 0.027 and 0.008, respectively). However, IL-6 levels at other milestone were not related to the patient prognosis. Serum IL-6 elevation in pre-radiation period is an unfavorable prognostic factor in tumor progression and patient survival. Further research is necessary to validate the prognostic role of IL-6 and explore the underlying mechanisms.
QLTI-13. A REVIEW OF THE BEST TREATMENT STRATEGY FOR NEUROFIBROMATOSIS TYPE 2 PATIENTS THROUGH A SINGLE-CENTER RETROSPECTIVE STUDY: HOW TO ORCHESTRATE MULTIMODAL TREATMENT OPTIONS
Authors: Jong Hee Chang, Seonah Choi
Journal: Neuro-Oncology (2024)

Abstract PURPOSE Neurofibromatosis type 2(NF2) is an autosomal-dominant tumor predisposition syndrome that affects the whole neuro-axis and causes various neurological symptoms decreasing the quality of life throughout the patient’s lifespan. However, due to the diversity of related diseases and the complexity of treatment, there are many cases in which only partial treatments are performed, the timing of the treatment is missed, and inappropriate treatment modality selection is made despite the intervention of many clinicians. Therefore, to increase the overall understanding of the disease, the single-institution experience was analyzed. MATERIALS AND METHODS Between 1998 and 2022, a total of 80 patients who clinically suspected NF2 visited our Brain Tumor Center. After excluding the patients with follow-up loss, the remaining 48 patients were finally analyzed. The patient’s symptoms, diagnosis process, treatment process, and outcomes were analyzed. RESULTS The average age at the time of symptom onset was 31.0(2-63) year-old. Hearing impairment was the major primary symptom(46.8%) among the patients. Five patients(10.9%) had family history of NF2. Thirty-five patients(76.1%) was clinically diagnosed as a NF2 for the bilateral vestibular schwannomas, and 34 patients(73.9%) had intracranial meningiomas, and 37 patients(80.4%) had spinal lesions at the time of diagnosis. Eleven patients were genetically confirmed as NF2. For vestibular schwannomas, 16 patients had surgical resection and 20 patients took gamma-knife surgery as a primary or secondary treatment. For intracranial meningiomas, 16 patients had surgical resection, 13 patients took gamma-knife surgery, and 15 patients took conventional radiation therapy as a primary or secondary treatment. Ventriculo-peritoneal shunt due to increased intracranial pressure was performed in 4 patients(8.7%). For spinal lesions, 16 patients had surgical resection. CONCLUSION NF2 is a disease group requiring a multidisciplinary team approach. The key to treatment is to set and implement a treatment sequence and method tailored to each patient. For this, an overall understanding of the disease and the treatment options is essential.
ANGI-05. UNSUPERVISED DECONVOLUTION OF SPATIOTEMPORAL HETEROGENEITY REVEALS HIGH-RESOLUTION INVASION STATES IN GLIOBLASTOMA
Authors: Varsha Thoppey Manoharan, Aly Abdelkareem, Samuel Brown, Aaron Gillmor, Courtney Hall, Heewon Seo, Kiran Narta, Sean Grewal, Ngoc Ha Dang, Bo Young Ahn, Kata Osz, Xueqing Lun, Douglas Mahoney, Donna Senger, Jennifer Chan, Sorana Morrissy
Journal: Neuro-Oncology (2024)

Abstract Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.
IMMU-32. DIPEPTIDYL PEPTIDASE-4 (DPP-4) MEDIATED CD8+ T-CELL REGULATION IN GLIOBLASTOMA
Authors: Oriana Y Teran Pumar, Clara Lopez Ruiz, Bruno Colon, Christine Isabelle Rafie, Natasha Karishma Khatwani, Erietta Stelekati, Dionysios C Watson, Defne Bayik
Journal: Neuro-Oncology (2024)

Abstract Glioblastoma (GBM) remains to be one of the deadliest cancers with poor prognosis and limited treatment options. While immunotherapies have revolutionized the field for other cancers, GBM remains resistant to these types of interventions partly due to its highly immunosuppressive tumor microenvironment, which is characterized by accumulation of exhausted CD8+ T cells. Therefore, there is a pressing need to identify molecular targets for improve anti-tumorigenic T cell response. Previous studies in our laboratory identified dipeptidyl peptidase-4 (DPP-4), an exopeptidase and a membrane-bound signaling receptor, as a potential marker of immunosuppression in GBM. While DPP-4 is well-drugged due to its role in insulin metabolism, the function of DPP-4 in immune regulation and cancer remains poorly defined. I observed that DPP-4 is highly expressed by CD8+ T cells GBM patients and preclinical models. Furthermore, DPP-4 levels increased with the exhaustion or memory differentiation of effector T cell. Pharmaceutical inhibition of DPP-4 enriched expression of genes related to lymphocyte activation and mitochondrial metabolism. These transcriptional changes were accompanied by enhanced T cell proliferation, cytotoxic function, and effector mediator production under exhaustion conditions. These findings suggest that pharmacological inhibition of DPP-4 promotes anti-tumorigenic function of CD8+ T-cell by preventing exhaustion and support future translational efforts to repurpose DPP-4 inhibitors as immunotherapy agents in GBM, given their favorable safety profiles.
NIMG-02. DEVELOPING A RADIOMIC HIERARCHICAL GAUSSIAN PROCESS BOOSTING MODEL TO PREDICT PRIMARY TUMOR ORIGIN FROM MULTICENTRIC LONGITUDINAL MRI DATA OF BRAIN METASTASES
Authors: Suhrud Panchawagh
Journal: Neuro-Oncology (2024)

Abstract Focal neurologic deficits due to brain metastases might be the initial presentation in patients with cancer. Common approaches to investigate the occult primary malignancy include a whole-body PET-CT, whole-body MRI, studying tumor markers, or histopathological examination. Most of these modalities are expensive, time-consuming, invasive, and delay treatment. The field of radiomics leverages quantitative features extracted from images to improve the decision-making process in clinical practices. Although not easily understandable, these features are proxies for the tumor’s shape, contour, texture, and intensity patterns. They can help understand tumor heterogeneity, predict tumor behavior, and assess treatment response. We propose a robust radiomic workflow using longitudinal MRI data from 914 high-resolution imaging studies from 12 different centers (Ocaña-Tienda et al. (2023), Ramakrishnan et al. (2023), Wang et al. (2023)) to predict the origin of brain metastases from brain MRI scans. After using ensemble feature selection methods, we trained three fixed-effects and one mixed-effects multiclass classification model to account for longitudinal data. Fixed-effects logistic regression and support vector kernel models performed the worst (AUC 0.74 and 0.75, respectively), while the random forest performed better (AUC 0.97). The mixed-effects hierarchical Gaussian process boosting (GPBoost) model performed the best with an accuracy of 85.8%, precision of 85.4%, sensitivity of 85.8%, specificity of 95.7%, and an AUC of 0.98. In one-versus-rest classification, the GPBoost model identified the primary tumor as melanoma with the highest accuracy (96.1%) and non-small cell lung cancer with the lowest accuracy (89.0%). Developing such models holds great promise in reducing patient and institutional costs, improving time to effective treatment, and enhancing overall survival rates through personalized therapy approaches. In the future, we hope that incorporating newer patient data and refining the model to improve external validity would enable more accurate and generalizable predictions, leading to better clinical outcomes across diverse populations.
TMIC-65. USING BIOINFORMATICS TO INVESTIGATE THE TUMOR MICROENVIRONMENT OF DIFFUSE MIDLINE GLIOMA
Authors: Havisha Kalpatthi, Andrea Cruz, Sameer Agnihotri
Journal: Neuro-Oncology (2024)

Abstract Diffuse midline gliomas, H3K27-altered (DMG) are highly malignant brain tumors with a 2-year survival rate of less than 10 percent. These tumors are primarily localized to the pons and thalamus regions of the brain. The cellular compartment of the tumor microenvironment (TME) which surrounds a tumor is composed of tumor and non-tumor cells, such as immune cells and oligodendrocytes. Communication between tumor cells and non-tumor cells in the TME can promote tumor growth and targeting communication networks could improve patient outcomes. Using a bioinformatics approach and publicly available pediatric DMG single-cell RNA-sequence (scRNA-seq) data, several methods of communication (secreted signaling, cell-cell contact, and extracellular matrix) within the DMG TME were identified. Based on the high communication probability, outgoing and incoming networks from non-tumor cells and tumor cells were inferred, such as the Complement, Galectin, and CD96 pathways. Specific ligand-receptor interactions between non-tumor and tumor cells were also found, including LGALS9-HAVCR2, PDGFB/PDGFRA, and COL4A5/ITGAV. A subsequent literature review was performed to validate these identified pathways in other tumors, including gliomas, and inform future in vitro experiments with primary DMG cell lines.