tbuckley/cabot-search huggingface dataset:
TMIC-43. BIOLOGICAL AND MOLECULAR CHARACTERIZATION OF NOVEL GLIOBLASTOMA TME SUBTYPES USING TRANSCRIPTOMIC ANALYSIS AND NETWORK MODELLING TO IDENTIFY NOVEL TARGETS OF VULNERABILITY
Authors: Mohannad Dabbour, Kieron White, Aleksandr Kalmykov, Alexander Kel, Hieko Dussman, Jochen Prehn, Annette Byrne
Journal: Neuro-Oncology (2024)

Abstract New precision medicine therapies are urgently required for glioblastoma (GBM). Recently, we developed a novel GBM classification system, identifying three patient clusters uniquely characterized by tumor microenvironment (TME) composition: TMELow, TMEMedium, and TMEHigh1. Our objective now is to further investigate these subtypes employing transcriptomic analysis and network modelling approaches to identify novel subtype-specific targets of vulnerability. We analyzed transcriptomic data from &amp;gt;600 GBM samples from publicly available and in-house datasets. All samples underwent TME subtyping. Next, we performed differential gene expression (using DESeq2, edgeR) and pathway analysis (using PROGENy). The ‘TRANSFAC’ tool 2 was used to identify potential transcription factors enriched in each TME subtype. TMELow tumours manifested highly upregulated NLGN3 (P=5.777e-5) and HES5 (P=2.233e-3) expression compared to TMEHigh. Moreover, TMEHigh compared to TMELow tumours were enriched for genes associated with tissue remodelling, and showed elevated MMP7 (P=3.051e-6), CLCL13 (P= 3.843e-4), and CXCL5 expression (P= 1.592e-6). PROGENy analysis suggested that the WNT, TRAIL, and JAK/STAT pathways were significantly enriched in TMEHigh, while the PI3K pathway was significantly upregulated in TMEMedium. Additionally, VEGF pathway activity was significantly upregulated in TMELow. TRANSFAC analysis measured by regulatory score (a measure of a transcription factor effect on gene expression) revealed that in TMELow, key transcription factors includes ETS2, NANOG, and MECP2 (regulatory score: 1.97, 1.45, and 1.4, respectively). In TMEMedium, transcription factors HIF1A, PPARA, and CDX2 were identified (regulatory score: 2.52, 2.08 and 1.96, respectively). In TMEHigh, TRANSFAC indicated activiation of transcription factors SMAD3, ETS2, and NFKB1 regulatory score: 2.56, 2.41, 2.24, respectively). Overall these findings highlight distinct molecular signatures across TME subtypes, and provide a deeper understanding of associated molecular mechanisms. Ongoing work is focused on validating key subtype specific genes and associated pathways using spatial proteomics. Prioritised targets will ultimately be interrogated in vivo, as novel subtype-specific treatments. 1. White et al. Ann Oncol. 2023;34(3):300–314. 2. Matys et al. Nucleic Acids Res. 2003;31.
TMIC-60. BRATS-PATH: ASSESSING HETEROGENEOUS HISTOPATHOLOGIC REGIONS IN GLIOBLASTOMA
Authors: Siddhesh Thakur, Shahriar Faghani, Mana Moassefi, Ujjwal Baid, Verena Chung, Sarthak Pati, Shubham Innani, Bhakti Baheti, Jacob Albrecht, Alexandros Karargyris, Hasan Kassem, Maclean Nasrallah, Jared Ahrendsen, Valeria Barresi, Maria Gubbiotti, Giselle Lopez, Calixto-Hope Lucas, Michael Miller, Lee Cooper, Jason Huse, William Bell, Spyridon Bakas
Journal: Neuro-Oncology (2024)

Abstract Glioblastoma, the most common malignant primary adult brain tumor, poses significant diagnostic and treatment challenges due to its heterogeneous molecular and micro-environmental profiles. To this end, we organize the BraTS-Path challenge to provide a public benchmarking environment and a comprehensive dataset to develop and validate AI models for identifying distinct histopathologic glioblastoma sub-regions in H&amp;E-stained digitized tissue sections. We identified 188 multi-institutional diagnostic slides of glioblastoma (IDH-wt, Gr.4) cases, from the TCGA-GBM and TCGA-LGG data collections, following their reclassification according to the 2021 WHO classification criteria. Sub-regions were selected according to distinctive morphology of histopathologic features and included aggressive tumor biology and areas consistent with potential treatment effect. Selected sub-region annotations included cellular tumor, geographic necrosis, cortical infiltration, pseudopalisading necrosis, microvascular proliferation, white matter penetration, regions dense with macrophages, leptomeningeal infiltration, and presence of lymphocytes. We obtained 107,340 patches of size 512x512 from the 9 sub-regions. A global network of board-certified expert neuropathologists defined and followed a systematic annotation protocol based on clinical definitions and only delineated sub-regions with high confidence, thus ensuring high-quality standardized data. Each tissue section was assigned to an annotator-approver pair, with the annotator delineating sub-regions and the approver ensuring the consistency of the annotations. By crowdsourcing annotations, the BraTS-Path challenge harnesses the collective expertise of clinical neuropathologists and fosters a collaborative environment to advance the neuro-oncology field. The anticipated developed algorithms are expected to integrate state-of-the-art computational methods, achieving high accuracy in identifying diverse histopathologic features and advancing clinical decision-making processes. The BraTS-Path challenge aims to bridge the gap between research and clinical practice by promoting the development of AI-driven tools for precise tumor characterization. This collaborative effort can significantly enhance our understanding of glioblastoma, improve diagnostic accuracy, and inform treatment strategies, thereby contributing to better patient outcomes.
PATH-55. RAMAN-BASED MACHINE LEARNING PLATFORM REVEALS UNIQUE METABOLIC DIFFERENCES BETWEEN IDHMUT AND IDHWT GLIOMA
Authors: Adrian Lita, Joel Sjöberg, David Păcioianu, Orieta Celiku, Tyrone Dowdy, Andrei Păun, Mark R Gilbert, Houtan Noushmehr, Ion Petre, Mioara Larion
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. METHODS Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. RESULTS Here we develop APOLLO (rAman-based PathOLogy of maLignant glioma) – a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wildtype (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. CONCLUSIONS Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.
DISP-06. THE IMPACT OF SOCIAL DETERMINANTS OF HEALTH ON TREATMENT DELAYS FOR GLIOBLASTOMA
Authors: Gino Cioffi, Samuel Gratzl, Mantas Dmukauskas, Kristin Waite, Andrew Sloan, Aaron Mammoser, Nicholas Stucky, Jill Barnholtz-Sloan
Journal: Neuro-Oncology (2024)

Abstract Social determinants of health (SDOH) contribute to treatment delays for a variety of diseases. However, the impact of SDOH on delays in treatment for glioblastoma (GBM) is not well established. Utilizing the Truveta database, which includes electronic health records, claims, linked SDOH data, and medication dispense data for a collective of US health systems, we evaluated individuals diagnosed with GBM from 1/1/2018-5/16/2024 who received either surgery, radiation, or temozolomide. Univariable and multivariable logistic regression modeling was performed to assess the odds of experiencing a delay in surgery (&amp;gt;7 days after diagnosis), radiation (&amp;gt;30 days after diagnosis), or temozolomide (&amp;gt;30 days after diagnosis). Adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) are reported. Models were assessed for demographic (sex, age, race) and SDOH factors (education, income, social support, transportation, housing stability). At the univariable level, college attendance and single-member household were associated with delays in surgery but were not significant in the multivariable model. Individuals diagnosed with GBM at ages of 45-64 and 65+ were less likely to experience delays in surgery than individuals ages 18-44 (45-64 aOR (95%CI): 0.58(0.38-0.89), 65+ aOR (95%CI): 0.42(0.25-0.71)). Additionally, individuals with an estimated yearly income of $60,001-$120,000 were 61% more likely to experience delays in surgery compared to individuals with an estimated income of &amp;lt;$40,000 (aOR (95%CI): 1.61(1.16-2.24)). In univariable analyses, male sex, age at diagnosis of 18-44, college attendance, and an estimated income &amp;gt;$120,000 were associated with delays in radiation. However, no significant factors were associated with delays in radiation after adjustment. Individuals ages 45-64 and 65+ were less likely to encounter delays in temozolomide than individuals ages 18-44 (45-64 aOR (95%CI): 0.64(0.46-0.88), 65+ aOR (95%CI): 0.62(0.43-0.91)). Understanding how SDOH impact treatment patterns provides vital insight on diverse factors that impact patient care.
NCOG-46. CHROMOSOMAL LOSSES CORRELATE WITH POOR OVERALL SURVIVAL IN H3K27M-ALTERED DIFFUSE MIDLINE GLIOMAS
Authors: J Steven Leal-Ekman, Thien Nguyen, Cynthia Campen, Katherine Ryan, Joshua Wheeler, Jasia Mahdi, Michelle Monje, Sonia Partap, Laura Prolo
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND H3K27M-altered diffuse midline gliomas (DMG) have poor outcomes with limited biomarkers to predict overall survival. Stratification based on chromosomal and molecular rearrangements represents a strategy to improve outcomes. METHODS We performed a retrospective analysis of 31 pediatric patients with biopsy-proven H3K27M-mutant DMG, who have undergone molecular next generation sequencing to define cytogenetics alterations that negatively impact overall survival. Median age at diagnosis in this cohort: 9.8 years (range 2.2-24.9 years). Among the cohort, 19 had H3F3A mutations confirmed by NGS, two had confirmed HIST1H3B mutations. Patients with intracranial masses received a standard fractionated radiation course with 54Gy or 59.4Gy XRT. RESULTS Pairwise comparisons demonstrate chromosomal losses and deletions are associated with poor overall survival (p=0.025, difference in Overall Survival [OS]: -19.9 months, 95%CI: -37.16 to -2.730) when compared to patients in the cohort without chromosomal deletions. Further subgroup analysis demonstrate worse outcomes in patients with deletions in chromosome 12 (p=0.003, difference in OS: -21.2 months, 95%CI: -34.74 to -7.702), deletions in chromosome 14 (p=0.007, difference in OS: -19.8 months, 95%CI: -33.72 to -5.892), or amplifications in chromosome 1 (p=0.003, difference in OS: -23.1 months, 95%CI: -37.53 to -8.613) relative to those without corresponding mutations. These data suggest that chromosomal alterations may serve as a potential biomarker to risk stratify patients. Likewise, genes within these deleted or amplified chromosomal segments may represent targets for therapeutic intervention.
NIMG-51. PROSPECTIVE CASE CONTROL STUDY OF HYPOXIA FMISO PET/MRI AS A DIAGNOSTIC FEATURE OF GLIOBLASTOMA IMMUNOTHERAPY FAILURE
Authors: Ramon Barajas Jr, Nadine Mallak, Gagandeep Choudhary, Laszlo Szidonya, Jared Stoller, Brian Thurston, Ahmed Raslan, Aclan Dogan, Woltjer Randy, Matthew Wood, Olabisi Sanusi, Josh Walker, Bill Rooney, Rochelle Fu, Leslie Muldoon, Samantha Holland, Edward Neuwelt, Sadek Nehmeh, Jana Ivanidze, Prakash Ambady
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Hypoxia is a prognostic biological feature of glioblastoma. 18F-fluoromisonidazole (FMISO) PET quantifies tumor hypoxia. Efficacy of checkpoint immunotherapy is being investigated, but specific response assessment criteria remain unknown. We hypothesized that tumor hypoxia is a defining feature of immunotherapy failure in patients with glioblastoma. METHODS A prospective case control study was undertaken in patients with newly diagnosed IDH wildtype glioblastoma (NCT03649880). Cases were concurrently enrolled in a phase II study (NCT03347617) of pembrolizumab immunotherapy (200 mg IV every 3 weeks) combined with Stupp protocol temozolomide-based chemoradiotherapy following maximal safe resection. Controls did not receive pembrolizumab. FMISO PET/MRI (3.7 MBq/kg, 90 min uptake) was performed at time of mRANO presumed progression. Hypoxic volume (HV) was defined by pixels with FMISO uptake ≥1.2x that of mean cerebellar uptake. MRI-Gd enhancing volume was segmented using a semi-automated approach. Hypoxic fraction (HF) was defined as the HV-to-MRI enhancing volume ratio. Disease progression or pseudoprogression (Psp) was confirmed through surgery or follow-up MRI. T-test and receiver operator characteristic (ROC) analysis assessed differences and diagnostic performance. RESULTS 18 patients (14 cases [11 progression and 3 Psp] and 4 controls [3 progression and 1 Psp]) provided evaluable disease. For all patients, HF was significantly elevated with progression (1.44 ± 0.8; N=14) compared to Psp (0.31 ± 0.3; N= 4; P=0.01). HV was not different (P=0.10). No difference in HV or HF was observed between Cases and Controls (P&amp;gt; 0.42). Use of HF yielded an AUROC of 0.96 with an optimal cut off of 0.74, to differentiate progression from Psp. CONCLUSION At mRANO presumed progression HF with FMISO PET/MRI accurately distinguishes tumor progression from neuroinflammatory Psp, irrespective of prior pembrolizumab immunotherapy. The incorporation of FMISO PET/MRI in larger prospective therapeutic clinical trials in patients with glioblastoma is underway and likely to improve response assessment criteria.
NCOG-48. CT SCAN COMPOSITE SCORE CAN IDENTIFY GLIOBLASTOMA PATIENTS WITH WORSE OUTCOMES
Authors: Farzaneh Rahmani, Joe Ippolito
Journal: Neuro-Oncology (2024)

Abstract OBJECTIVE Obesity is a risk factor for many cancer types including glioblastoma multiforme (GBM). We used CT body composition analyses to construct a sex-specific composite score that would optimally predict overall survival in GBM. METHODS Using a retrospective institutional cohort, we performed single slice (L3) and volumetric (L1-L5) automated body segmentation analyses on abdominal and pelvic computed tomography (CT) scans performed within 1 month of diagnosis of GBM. Multivariable Cox proportional hazards models were then used to identify variables that were independently associated with GBM overall survival. Lastly, a learning-tree algorithm was implemented to identify the relative importance of body composition metrics using a XGBoost-powered learning model. These weights were used to create sex-specific composite CT scan scores of protective and predisposing factor with relation to GBM survival. A log-rank pairwise t-test was used to compared GBM survival across patient groups stratified based on these CT composite scores. RESULTS Higher relative inter/intramuscular adipose tissue volume (HR(95%CI):1.9(1.01-3.06), p=0.02), visceral adipose tissue volume (HR(95%CI):2.1(1.04-4.1), p=0.04) and aortic calcification volume (HR(95%CI):2.6(1.2-5.5), p&amp;lt;0.001) were associated with lower GBM overall survival, while higher subcutaneous adipose tissue volume (HR(95%CI):0.4(0.2-0.8), p=0.02), iliopsoas muscle volume (HR(95%CI):0.4(0.2-0.9), p&amp;lt;0.001), and trabecular bone density (HR(95%CI):0.4(0.1-0.8), p=0.003) were predictive of higher GBM OS (p-value of all tests &amp;lt;0.05). Using quartiles of the CT scan composite predisposing and protective scores, we were able to stratify male and female participants with significantly different outcomes with regards to GBM OS (Figure). Male and female participants with higher burden of predisposing factors had significantly lower chances of survival compared to those with lower cumulative burden of these factors (Figure, p-value Q4 vs. Q1 males&amp;lt; 0.01 and 0.001 in females) CONCLUSION Using automated body segmentation analysis we optimized a CT scan composite score that was able to stratify GBM patients with worse overall survival.
DISP-17. OUTCOMES, CLINICAL AND MOLECULAR CHARACTERISTICS OF HISPANIC PATIENTS WITH IDH WILDTYPE GLIOBLASTOMA
Authors: Juan Blaquier, Fulvio D’Angelo, Dalissa Tejera, Ruham Nasany, Marina Kushnirsky, Colin Hill, Antonio Iavarone, Macarena de la Fuente
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Knowledge about outcomes in Hispanic patients with gliomas is scant and mainly derived from tumor registries. IDH wild-type glioblastomas (GBM) are the most common and aggressive malignant primary brain tumor. Hispanics account for 19% of US population, reaching 29% in Florida and 65% in Miami-Dade County. As a reference center for GBM serving the community of South Florida we provide care for many Hispanic patients. Herein, we aim to describe the clinical and molecular features, including RNA seq analysis of Hispanic vs non-Hispanic patients with IDH wild-type GBM. METHODS Retrospective study of adult patients with diagnosis of IDH Wild-type Glioblastoma, who had NGS testing, treated at University of Miami. RESULTS 179 patients were included, 63 (35%) were Hispanic. 41 (65%) of Hispanic patients were males as compared to 65 (56%) of the non-Hispanics. Median age at diagnosis was 54 years (45-67) for the Hispanic vs 62 (54-72) years for non-Hispanic, p=.002. With a median follow up of 17 months (CI 95% 14-19), median overall survival for Hispanic patients was 22 months (18-25) and 23 months (18-28) for non-Hispanics, p=.99. 42 (23%) patients were enrolled in clinical trials, of whom 21 (50%) were Hispanic, meaning 33% of Hispanic patients enrolled. The molecular characteristics from these patients by next-generation sequencing, including RNA seq, are being analyzed. CONCLUSIONS In our series of IDH wild-type Glioblastomas, Hispanic patients were significantly younger at diagnosis compared to non-Hispanic patients, but there were no differences in overall survival. 33% of Hispanic patients were enrolled in clinical trials. As the latest growing ethnic group in the USA, major efforts have to be made to better understand the disease characteristics and outcomes of this particular population. To our knowledge this will be the first report of the molecular characteristics by NGS from Hispanic patients with GBM.
NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM
Authors: Suyash Mohan, Jose Garcia, Hamed Akbari, Sunwoo Kwak, Kyunglok Baik, Mahmoud Shalaby, Yuji Matsumoto, Spyridon Bakas, Peter Kamel, Charit Tippareddy, Chaitra Badve, Matthew Lee, Jill Barnholtz-Sloan, Andrew Sloan, Arnab Chakravarti, Joshua Palmer, Adam Dicker, Adam Flanders, Wenyin Shi, Rajan Jain, Pamela LaMontagne, Daniel Marcus, Aris Sotiras, Carmen Balana, Jaume Capellades, Josep Puig, Santiago Cepeda, Anna Luisa Di Stefano, Benedikt Wiestler, Graeme Woodworth, Donald ORourke, MacLean Nasrallah, Christos Davatzikos
Journal: Neuro-Oncology (2024)

Abstract BACKGROUND Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a machine learning model constructed from a diverse, inter-institutional dataset can improve accuracy of generated tumor infiltration maps, thus guiding precision targeted therapies. METHODS 731 MRI scans of treatment-naïve glioblastoma patients from 10 institutions were included. All patients had pre-operative multiparametric-MRI (T1, T1Gd, T2, T2-FLAIR, ADC), and underwent complete resection of the enhancing tumor followed by standard-of-care chemoradiotherapy. 42 patients were used as an independent validation set, and 689 were used for training. Of these 239 patients had histopathologically confirmed recurrence with corresponding MRI scans, which were used as ground-truth for evaluating the location of recurrence using a leave-one-site-out (LSO) method. An AI model combining deep learning and SVM was used to develop a predictive model for infiltration. We validated the generalizability of our results in an unseen, multi-institutional data set. RESULTS Our model predicted locations of recurrence with odds ratio (99% CI) 37.6 (37.1-38.1) on the LSO testing set and 24.3 (23.4-25.2) on the validation set, indicating that areas labeled highly infiltrated were over 37 and 24 times more likely to coincide with future recurrence respectively. CONCLUSIONS We demonstrate that AI-based pattern analysis from multiparametric-MRI can predict tumor infiltration in peritumoral regions with high likelihood of recurrence by decrypting the visually imperceptible heterogeneity of peritumoral tissue. Model performance improved from training on a larger/diverse dataset and combining results of multiple AI methods. Independent validation confirmed the model’s ability to generalize to unseen data. We believe this will serve to advance AI-based biomarkers for predicting future recurrence and facilitate development of multi-modal targeted therapies in this era of precision neuro-oncology.