Outcome Of Glioblastoma Patients Research Articles

Do Presenting Symptoms Predict Treatment Decisions and Survival in Glioblastoma? -Real World Data from 1458 patients in the Swedish Brain Tumour Registry

Abstract Background Glioblastoma is the most common malignant brain tumour in adults. Non-invasive clinical parameters could play a crucial role in treatment planning and serve as predictors of patient survival. Our register-based real-life study aimed to investigate the prognostic value of presenting symptoms. Methods Data on presenting symptoms and survival, as well as known prognostic factors, were retrieved for all glioblastoma patients in Sweden registered in the Swedish Brain Tumour Registry between 2018 and 2021. The prognostic impact of different presenting symptoms was calculated using the Cox proportional hazard model. Results Data from 1458 adults with pathologically verified IDH wild-type glioblastoma were analysed. Median survival time was 345 days. The two-year survival rate was 21.5%. Registered presenting symptoms were focal neurological deficits, cognitive dysfunction, headache, epilepsy, signs of raised intracranial pressure and cranial nerve symptoms, with some patients having multiple symptoms. Patients with initial cognitive dysfunction had significantly shorter survival than patients without; 265 days (245-285) vs. 409 days (365-453) (p<0.001). The reduced survival remained after Cox regression adjusting for known prognostic factors. Patients presenting with seizures and patients with headaches had significantly longer overall survival compared to patients without these symptoms, but the difference was not retained in multivariate analysis. Patients with cognitive deficits were less likely to have radical surgery and to receive extensive anti-neoplastic nonsurgical treatment. Conclusion This extensive real-life study reveals that initial cognitive impairment acts as an independent negative predictive factor for treatment decisions and adversely affects survival outcomes in glioblastoma patients.

Abstract CT230: Exploratory analyses of MGMT promoter methylation and PD-L1 expression from newly diagnosed glioblastoma patients in CheckMate 548 study suggest refinement in thresholds for predicting response to standard of care and nivolumab treatments

Abstract CheckMate 548 (NCT02667587, CM548) is a phase III, randomized trial evaluating first line nivolumab (NIVO) in combination with standard of care (SOC) [surgical resection followed by radiotherapy and temozolomide (TMZ)] versus placebo plus SOC in patients with newly diagnosed glioblastoma (GBM) positive for promoter methylation of O6-methylguanine DNA methyltransferase (MGMT). MGMT promoter methylation status and PD-L1 expression predict better outcomes in GBM patients treated with TMZ and in patients treated with NIVO in other indications, respectively. However, the impact of varying levels of MGMT promoter methylation and PD-L1 expression on patient outcome in GBM remains unclear. In CM548, MGMT promoter methylation levels were quantified through a central lab assay (LapCorp) and PD-L1 expression was determined by percentage of detectable membrane staining of tumor cells. MGMT methylation and PD-L1 data were prospectively generated at the time of enrolment. Our analyses showed that increasing levels of MGMT promoter methylation level were associated with longer overall survival in patients treated with SOC alone (n = 349). However, no survival benefit was observed with increasing levels of MGMT methylation in patients treated with NIVO in combination with SOC (n = 353). In addition, our data showed a non-linear relationship between PD-L1 expression and overall survival across both arms. In patients with intermediate PD-L1 expression (≥5%, <20%), longer survival was observed in the nivolumab + SOC arm compared to SOC alone. This benefit was not observed in patients with low (≥1%, <5%) and high (>20%) PD-L1 expression. Potential mechanisms driving these observed associations are currently explored using genomic data generated from patient samples. MGMT promoter methylation and PD-L1 expression are potential clinically relevant predictive biomarkers of response to treatments administered in the CM-548; however, the binary classification of positive/negative status of these markers does not fully capture their relationship with efficacy in CM548. Data generated from our exploratory analyses suggests that new, refined criteria are needed for better patient outcomes prediction. Citation Format: Yu-Han Hung, Aparna Chhibber, Michael Lim, Michael Weller, David A. Reardon, Antonio Omuro, Keith L. Ligon, Ana Lako, Ann Forslund. Exploratory analyses of MGMT promoter methylation and PD-L1 expression from newly diagnosed glioblastoma patients in CheckMate 548 study suggest refinement in thresholds for predicting response to standard of care and nivolumab treatments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT230.

100 The Impact of Race and Social Determinants of Health on Clinical Outcome of Glioblastoma Multiforme Patients Over a Decade.

OBJECTIVES/GOALS: While the evolving treatment paradigm for Glioblastoma (GBM) leverages different modalities to improve outcomes, treatment access might be limited by cost and disparities. This study explores the influence of race and social determinants of health (SDoH) on healthcare access and outcomes of GBM patients in a large metropolitan area over a decade. METHODS/STUDY POPULATION: Our institution’s tumor registry (2009-2019) was queried to identify our GBM cohort. Data were supplemented by electronic health records to include demographics, outcome, NCI Comorbidity Index, and the Agency for Healthcare Research and Quality (AHRQ) socioeconomic status (SES) index. RESULTS/ANTICIPATED RESULTS: Of the 559 GBM records, 361 unique patients met the inclusion criteria, and 43% were Non-White. Non-White patients predominantly comprised the lowest AHRQ SES index quartile and had longer hospital stays (LOS; p<0.001). White patients accounted for 61% of privately insured patients (p<0.001). Private insurance (p= 0.02) and age < 65 years (p= 0.039) were associated with a higher rate of home discharge. Patients diagnosed with GBM in the emergency department were more likely to be discharged to acute rehab than home (p<0.001). At 2 years, privately insured patients had longer OS (HR= 1.46; p= 0.04). DISCUSSION/SIGNIFICANCE: In contrast to previous studies, the study demonstrates that GBM affected a higher proportion of Non-White patients. Our data show that SDoH influences multiple outcomes in GBM patients. Efforts to identify and correct these barriers are needed to improve the care of all GBM patients.

Focal-to-bilateral tonic-clonic seizures and High-grade CMV-infection are poor survival predictors in Tumor-related Epilepsy Adult-type diffuse gliomas—A single-center study and literature review

IntroductionPrevious studies have reported a correlation between a high-grade CMV-infection and an unfavorable prognosis in glioblastoma (GB). Coversely, epilepsy has been associated with a more favorable outcome in GB patients. Despites epilepsy and CMV share similar molecular mechanisms in GB tumoral microenvironment, the correlation between Tumor-Related-Epilepsy (TRE) and CMVinfection remains unexplored. The aim of our study is to examine the correlation between the dregree of CMV infection and seizure types on the survival of TRE Adult-type-diffuse-glioma. To achieve this objective, we conducted a comprehensive literature review to assess our results regarding previous publications MethodsWe conducted a retrospective-observational study on TRE Adult-type-diffuse-gliomas treated at a single center in Mexico from 2010 to 2018. Tumor tissue and cDNA were analyzed by immunochemistry (IHC) for CMV (IE and LA antigens) at the Karolinska Institute in Sweden, and RT-PCR for CMV-gB in Torreon Mexico, respectively. Bivariate analysis (X2-test) was performed to evaluate the association between subtypes of Adult-type-diffuse-glioma (IDH-mut grade 4 astrocytoma vs. IDH-wt glioblastoma) and the following variables: type of hemispheric involvement (mesial vs. neocortical involvement), degree of CMV infection (<25%vs. >25% infected-tumoral cells) and seizure types [Focal awareness, focal impaired awareness, and FBTCS]. Kaplan Meier and Cox analyses were performed to determine the risk, p < 0.05 was considered statistically significant. ResultsSixty patients with TRE Adult type diffuse gliomas were included (80% IDH-wt glioblastoma and 20% IDH-mut grade 4astrocytomas). The mean age was 61.5 SD ± 18.4, and 57% were male. Fifty percent of the patients presented with mesial involvement of the hemysphere. Seizure types included focal awareness (15%), focal impaired awareness (43.3%), and FBTCS (41.7%). Ninety percent of cases were treated with Levetiracetam and 33.3% presented Engel-IA postoperative seizure control. More than 90% of samples were positive for CMV-immunohistochemistry (IHC). However, all cDNA analyzed by RT-PCR return negative results. The median of overall survival (OS) was 15 months. High-grade CMV-IE infection (14 vs. 25 months, p<0.001), mesial involvement (12 vs. 18 months, p<0.001), and FBTCS were associated with worse OS (9 vs.18 months for non-FBTCS). Multivariate analysis demonstrated that high-grade CMV infection (HR = 3.689, p=0.002) and FBTCS (HR=7.007, p<0.001) were independent unfavorable survival factors. ConclusionsCMV induces a proinflammatory tumoral microenvironment that contributes to the developmet of epilepsy. Tumor progression could be associated not only with a higher degree of CMV infection but also to epileptogenesis, resulting in a seizure phenotype chracterized by FBTCS and poor survival outcomes. This study represents the first survival analysis in Latin America to include a representative sample of TRE Adult-type diffuse gliomas considering CMV-infection-degree and distinguishing features (such as FBTCS) that might have potential clinical relevance in this group of patients. Further prospective studies are required to validate these results.

200 DWI and ADC Changes in Patients With Glioblastoma IDH-Wild Type Following Surgical Tumor Resection

INTRODUCTION: Current standard of care for glioblastoma (GBM) entails maximal tumor resection to achieve better outcomes while preserving neurological function. Early postoperative DWI has elucidated that peritumoral ischemia is common following glioma resection, but radiographic characteristics, risk factors, and outcomes have not been well described. METHODS: This retrospective cohort included 324 patients (18-86 years) who underwent surgical resection for glioblastoma IDH-wildtype between 2005 and 2022. All patients had structural MRI and DWI acquired preoperatively and within 72 hrs after resection. Areas of restricted diffusion on postoperative DWI were classified as rim, sector, mixed, or remote. RESULTS: Areas of restricted diffusion on postoperative DWI were found in 173 (53%) patients. 55 (32%) were rim-shaped, 103 (59%) sector-shaped, 10 (6%) mixed, and 5 (3%) remote. Preoperative tumor volume was positively correlated with areas of ischemia (p = 0.048), but peritumoral DWI was not correlated with decreased overall survival (OS) or Karnofsky Performance Score (KPS). In the full cohort analysis, gross total resection (GTR) correlated with improved OS compared to subtotal total resection (STR) (p = 0.016); however, subgroup analysis revealed that this survival benefit was exclusively found in patients without peritumoral infarction on DWI (21 months vs. 13 months, p = 0.043). CONCLUSIONS: Larger tumor volume was a risk factor for larger peritumoral infarction. The survival benefit of GTR was only seen in patients who did not experience postoperative peritumoral infarction, underscoring the importance of maximal safe tumor resection. Future study on specific causes of postoperative peritumoral infarctions may improve surgical management and outcomes in glioblastoma patients.

ELAVL2 loss promotes aggressive mesenchymal transition in glioblastoma

Glioblastoma (GBM), the most lethal primary brain cancer, exhibits intratumoral heterogeneity and molecular plasticity, posing challenges for effective treatment. Despite this, the regulatory mechanisms underlying such plasticity, particularly mesenchymal (MES) transition, remain poorly understood. In this study, we elucidate the role of the RNA-binding protein ELAVL2 in regulating aggressive MES transformation in GBM. We found that ELAVL2 is most frequently deleted in GBM compared to other cancers and associated with distinct clinical and molecular features. Transcriptomic analysis revealed that ELAVL2-mediated alterations correspond to specific GBM subtype signatures. Notably, ELAVL2 expression negatively correlated with epithelial-to-mesenchymal transition (EMT)-related genes, and its loss promoted MES process and chemo-resistance in GBM cells, whereas ELAVL2 overexpression exerted the opposite effect. Further investigation via tissue microarray analysis demonstrated that high ELAVL2 protein expression confers a favorable survival outcome in GBM patients. Mechanistically, ELAVL2 was shown to directly bind to the transcripts of EMT-inhibitory molecules, SH3GL3 and DNM3, modulating their mRNA stability, potentially through an m6A-dependent mechanism. In summary, our findings identify ELAVL2 as a critical tumor suppressor and mRNA stabilizer that regulates MES transition in GBM, underscoring its role in transcriptomic plasticity and glioma progression.

Treatment Strategies for Glioblastoma in the Elderly: What Should We Focus on Compared to Younger Patients.

(1) Background: Although the incidence of glioblastoma (GB) has a peak in patients aged 75-84 years, no standard treatment regimen for elderly patients has been established so far. The goal of this study was to analyze the outcome of GB patients ≥ 65 years to detect predictors with relevant impacts on overall survival (OS) and progression-free survival (PFS). (2) Methods: Medical records referred to our institution from 2006 to 2020 were analyzed. Adult GB patients with clinical data, postoperative MRI data, and ≥1 follow-up investigation after surgical resection were included. The complete cohort was divided into a younger (<65) and an elderly group (≥65 years). Multiple factors regarding OS and PFS were scanned using univariate and multivariable regression with p < 0.05. (3) Results: 1004 patients were included with 322 (61.0%) male individuals in the younger and 267 (56.1%) males in the older cohort. The most common tumor localization was frontal in both groups. Gross total resection (GTR) was the most common surgical procedure in both groups, followed by subtotal resection (STR) (145; 27.5%) in the younger group, and biopsy (156; 32.8%) in the elderly group. Multivariate analyses detected that in the younger cohort, MGMT promoter methylation and GTR were predictors for a longer OS, while MGMT methylation, GTR, and hypofractionated radiation were significantly associated with a longer OS in the elderly group. (4) Conclusions: Elderly patients benefit from surgical resection of GB when they show MGMT promoter methylation, undergo GTR, and receive hypofractionated radiation. Furthermore, MGMT methylation seems to be associated with a longer PFS in elderly patients. Further investigations are required to confirm these findings, especially within prospective radiation therapy studies and molecular examinations.

Upregulation of HLA-II related to LAG-3+CD4+ T cell infiltration is associated with patient outcome in human glioblastoma.

Glioblastoma (GBM) is the most common malignant diffuse glioma of the brain. Although immunotherapy with immune checkpoint inhibitors (ICIs), such as programmed cell death protein (PD)-1/PD ligand-1 inhibitors, has revolutionized the treatment of several cancers, the clinical benefit in GBM patients has been limited. Lymphocyte-activation gene 3 (LAG-3) binding to human leukocyte antigen-II (HLA-II) plays an essential role in triggering CD4+ T cell exhaustion and could interfere with the efficiency of anti-PD-1 treatment; however, the value of LAG-3-HLA-II interactions in ICI immunotherapy for GBM patients has not yet been analyzed. Therefore, we aimed to investigate the expression and regulation of HLA-II in human GBM samples and the correlation with LAG-3+CD4+ T cell infiltration. Human leukocyte antigen-II was highly expressed in GBM and correlated with increased LAG-3+CD4+ T cell infiltration in the stroma. Additionally, HLA-IIHighLAG-3High was associated with worse patient survival. Increased interleukin-10 (IL-10) expression was observed in GBM, which was correlated with high levels of HLA-II and LAG-3+ T cell infiltration in stroma. HLA-IIHighIL-10High GBM associated with LAG-3+ T cells infiltration synergistically showed shorter overall survival in patients. Combined anti-LAG-3 and anti-IL-10 treatment inhibited tumor growth in a mouse brain GL261 tumor model. Invitro, CD68+ macrophages upregulated HLA-II expression in GBM cells through tumor necrosis factor-α (TNF-α). Blocking TNF-α-dependent inflammation inhibited tumor growth in a mouse GBM model. In summary, T cell-tumor cell interactions, such as LAG-3-HLA-II, could confer an immunosuppressive environment in human GBM, leading to poor prognosis in patients. Therefore, targeting the LAG-3-HLA-II interaction could be beneficial in ICI immunotherapy to improve the clinical outcome of GBM patients.

Inflammatory response and cell cycle pathways contribute to innate resistance to Anti-PD-1 therapy in glioblastoma

Glioblastoma (GBM), as the most prevalent malignant primary brain tumor in adults, is characterized by limited treatment options and poor prognosis. Immune checkpoint inhibitors have revolutionized cancer therapy, prompting interest in their potential application in GBM treatment. This study identified potential targets for enhancing the efficacy of GBM immunotherapy by a statistical analysis of genomic and transcriptional data coupled with an exploration of the molecular mechanisms governing patient responses to immunotherapy. Our analysis revealed that the effectiveness of anti-PD-1 immunotherapy in GBM is closely associated with the mutational burden of the tumor and the age at which treatment is initiated. In addition, we found that the gene set signature of cell cycle regulation is upregulated, while the immune response regulation pathways are enriched in the tumors from non-responsive patients. These findings underscore the potential effectiveness of targeting these pathways in the context of anti-PD-1 immunotherapy, with the promise of enhancing patient outcomes in GBM.

Integrated multiomic analysis reveals disulfidptosis subtypes in glioblastoma: implications for immunotherapy, targeted therapy, and chemotherapy.

Glioblastoma (GBM) presents significant challenges due to its malignancy and limited treatment options. Precision treatment requires subtyping patients based on prognosis. Disulfidptosis, a novel cell death mechanism, is linked to aberrant glucose metabolism and disulfide stress, particularly in tumors expressing high levels of SLC7A11. The exploration of disulfidptosis may provide a new perspective for precise diagnosis and treatment of glioblastoma. Transcriptome sequencing was conducted on samples from GBM patients treated at Tiantan Hospital (January 2022 - December 2023). Data from CGGA and TCGA databases were collected. Consensus clustering based on disulfidptosis features categorized GBM patients into two subtypes (DRGclusters). Tumor immune microenvironment, response to immunotherapy, and drug sensitivity were analyzed. An 8-gene disulfidptosis-based subtype predictor was developed using LASSO machine learning algorithm and validated on CGGA dataset. Patients in DRGcluster A exhibited improved overall survival (OS) compared to DRGcluster B. DRGcluster subtypes showed differences in tumor immune microenvironment and response to immunotherapy. The predictor effectively stratified patients into high and low-risk groups. Significant differences in IC50 values for chemotherapy and targeted therapy were observed between risk groups. Disulfidptosis-based classification offers promise as a prognostic predictor for GBM. It provides insights into tumor immune microenvironment and response to therapy. The predictor aids in patient stratification and personalized treatment selection, potentially improving outcomes for GBM patients.

Spatiotemporal Insights into Glioma Oncostream Dynamics: Unraveling Formation, Stability, and Disassembly Pathways.

Glioblastoma (GBM) remains a challenge in Neuro-oncology, with a poor prognosis showing only a 5% survival rate beyond two years. This is primarily due to its aggressiveness and intra-tumoral heterogeneity, which limits complete surgical resection and reduces the efficacy of existing treatments. The existence of oncostreams-neuropathological structures comprising aligned spindle-like cells from both tumor and non-tumor origins- is discovered earlier. Oncostreams are closely linked to glioma aggressiveness and facilitate the spread into adjacent healthy brain tissue. A unique molecular signature intrinsic to oncostreams, with overexpression of key genes (i.e., COL1A1, ACTA2) that drive the tumor's mesenchymal transition and malignancy is also identified. Pre-clinical studies on genetically engineered mouse models demonstrated that COL1A1 inhibition disrupts oncostreams, modifies TME, reduces mesenchymal gene expression, and extends survival. An in vitro model using GFP+ NPA cells to investigate how various treatments affect oncostream dynamics is developed. Analysis showed that factors such as cell density, morphology, neurotransmitter agonists, calcium chelators, and cytoskeleton-targeting drugs influence oncostream formation. This data illuminate the patterns of glioma migration and suggest anti-invasion strategies that can improve GBM patient outcomes when combined with traditional therapies. This work highlights the potential of targeting oncostreams to control glioma invasion and enhance treatment efficacy.

Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.

Analysis of anemia and iron supplementation among glioblastoma patients reveals sex-biased association between anemia and survival

The association between anemia and outcomes in glioblastoma patients is unclear. We analyzed data from 1346 histologically confirmed adult glioblastoma patients in the TriNetX Research Network. Median hemoglobin and hematocrit levels were quantified for 6 months following diagnosis and used to classify patients as anemic or non-anemic. Associations of anemia and iron supplementation of anemic patients with median overall survival (median-OS) were then studied. Among 1346 glioblastoma patients, 35.9% of male and 40.5% of female patients were classified as anemic using hemoglobin-based WHO guidelines. Among males, anemia was associated with reduced median-OS compared to matched non-anemic males using hemoglobin (HR 1.24; 95% CI 1.00–1.53) or hematocrit-based cutoffs (HR 1.28; 95% CI 1.03–1.59). Among females, anemia was not associated with median-OS using hemoglobin (HR 1.00; 95% CI 0.78–1.27) or hematocrit-based cutoffs (HR: 1.10; 95% CI 0.85–1.41). Iron supplementation of anemic females trended toward increased median-OS (HR 0.61; 95% CI 0.32–1.19) although failing to reach statistical significance whereas no significant association was found in anemic males (HR 0.85; 95% CI 0.41–1.75). Functional transferrin-binding assays confirmed sexually dimorphic binding in resected patient samples indicating underlying differences in iron biology. Anemia among glioblastoma patients exhibits a sex-specific association with survival.

Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning.

Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited in glioblastoma patients, necessitating a proactive approach to anticipate treatment response and resistance. In this comprehensive study, we conducted a rigorous analysis involving two distinct glioblastoma patient cohorts subjected to PD-1 blockade treatments. Our investigation revealed that a significant portion (60%) of patients exhibit persistent disease progression despite ICB intervention. To elucidate the underpinnings of resistance, we characterized the immune profiles of glioblastoma patients with continued cancer progression following anti-PD1 therapy. These profiles revealed multifaceted defects, encompassing compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and heightened expression of immunosuppressive molecules (TGFB, IL2RA, and CD276). Building upon these resistance profiles, we leveraged cutting-edge machine learning algorithms to develop predictive models and accompanying software. This innovative computational tool achieved remarkable success, accurately forecasting the progression status of 82.82% of the glioblastoma patients in our study following ICBs, based on their unique immune characteristics. In conclusion, our pioneering approach advocates for the personalization of immunotherapy in glioblastoma patients. By harnessing patient-specific attributes and computational predictions, we offer a promising avenue for the enhancement of clinical outcomes in the realm of immunotherapy. This paradigm shift towards tailored therapies underscores the potential to revolutionize the management of glioblastoma, opening new horizons for improved patient care.

Exploring the Influence of tGLI-1 on Temozolomide Resistance in Glioblastomas: Unraveling Novel Therapeutic Targets

Background/Objective:Temozolomide (TMZ) is a standard chemotherapy treatment for patients with glioblastoma (GBM), but its effectiveness is limited, with only 50% of patients initially responding and developing resistance over time. Glioma stem cells (GSCs) have been implicated in TMZ resistance, particularly the mesenchymal subtype. The truncated form of GLI1, known as tGLI1, is highly expressed in mesenchymal GSCs and has been associated with poor patient outcomes in GBM. However, the role of tGLI1 in TMZ resistance remains unknown.&#x0D; Methods:The GBM cell line, U87MG, was utilized for this study. The IC50 of TMZ was determined using a cell viability assay. After successful transfection with vector, GLI1, and tGLI1, the cells were treated with the IC50 of TMZ to assess changes in cell viability between the groups.&#x0D; Results:The IC50 of TMZ is 290.1 μM, as averaged between replicate assays. Thus far, the results showed that tGLI1-expressing cells exhibited significantly higher cell viability (average: 39.09%) compared to Vector (average: 26.78%) and GLI1 (average: 27.11%). However, the tGLI1 group displayed higher variability in cell viability results, as evidenced by a larger standard deviation (0.2758) and standard error (0.1592) compared to vector (SD: 0.0325, SE: 0.0188) and GLI1 (SD: 0.1354, SE: 0.0781). The One-Way ANOVA, followed by Tukey's Multiple Comparison Test, results showed no statistically significant differences in cell viability between the groups.&#x0D; Conclusion/Impact:The increased cell viability observed in tGLI1-expressing cells suggests a potential association between tGLI1 and TMZ resistance, warranting additional research to fully comprehend its impact on GBM treatment response. Further investigation and replication studies are needed to establish the robustness of these results. This knowledge may contribute to the development oftargeted therapies aimed at inhibiting tGLI1 or its downstream signaling pathways, potentiallyimproving the response to TMZ and patient outcomes.

The Hospital Frailty Risk Score Independently Predicts Postoperative Outcomes in Glioblastoma Patients

Background and ObjectiveThe Hospital Frailty Risk Score (HFRS) was recently introduced as a tool for quantifying patient frailty using ICD-10 codes. The utility of HFRS has not yet been evaluated. The objective of the study is to determine the utility of HFRS in predicting surgical outcomes following resection of GBM and compare its prognostic ability to other validated indices, such as the American Society of Anesthesiologists (ASA) physical status classification and Charlson Comorbidity Index (CCI). MethodsA retrospective analysis was conducted using a GBM patient database (2017 to 2019) at a single institution. HFRS was calculated using ICD-10 codes. Bivariate logistic regression was used to model the prognostic ability of each frailty index, and model discrimination was assessed using the area under the receiver operating characteristic curve (AUROC). Multivariate linear and logistic regression models were used to assess for significant associations between HFRS and continuous and binary postoperative outcomes, respectively. Results263 GBM patients were included in the study. HFRS had a significantly greater AUROC when compared to ASA (p=0.016) and CCI (p=0.037) for predicting 30-day readmission. On multivariate analysis, HFRS was significantly and independently associated with hospital length of stay (LOS) (p=0.0038), nonroutine discharge (p=0.018) and 30-day readmission (p=0.0051). ConclusionHFRS has utility in predicting postoperative outcomes for GBM patients and more effectively predicts 30-day readmission than other frailty indices. HFRS may be used as a tool for optimizing clinical decision-making to reduce adverse post-operative outcomes among GBM patients.

Abstract IA006: Multifaceted effects of DNA damage response inhibitors on radiation responses of glioblastoma and the normal brain

Abstract PARP inhibitors (PARPi) and ATM inhibitors (ATMi) enhance radiation sensitivity in multiple cancer models, both in vitro and in vivo. Our observation that the radiosensitizing properties of PARPi are most pronounced in rapidly proliferating cells is reflected in early phase clinical trial data showing exacerbation of acute radiation toxicity in rapidly proliferating tissues such as oropharyngeal and esophageal mucosa. Lack of radiosensitization in late responding, slowly proliferating normal tissues indicates that PARPi may be more effectively combined with radiation therapy (RT) in patients with brain tumors. ATMi are much more potent radiosensitizers than PARPi but less is known about their impact on normal tissue toxicity since they have only recently progressed to the clinic, although preclinical in vivo data are encouraging. We are evaluating the oral PARPi olaparib and the oral ATMi AZD1390 in combination with RT +/- temozolomide (TMZ) in the treatment of glioblastoma (GBM), the most prevalent and most aggressive primary brain tumor. Patients with GBM experience very poor outcomes in terms of both median survival (c.1 year) and neurocognitive decline, which is caused primarily by RT. Early phase testing of the olaparib-RT combination is underway in three populations of patients with newly diagnosed GBM. Patients aged &amp;gt;65 with MGMT unmethylated GBM are being recruited to a randomized, placebo-controlled phase II study (PARADIGM, ISRCTN52658296) after a phase I dose escalation study showed that olaparib (200 mg twice daily) was extremely well tolerated when combined with brain irradiation (40 Gray in 15#). Good performance status patients aged under 70 are being recruited to PARADIGM-2 (ISRCTN51253312) which comprises two parallel phase I dose escalation studies: patients with MGMT unmethylated tumors are receiving daily olaparib with RT (60 Gy in 30#) without TMZ, while patients with MGMT methylated tumors are receiving intermittent olaparib with standard chemoradiation. AZD1390 is being evaluated in a first-in-human phase I dose escalation study (NCT03423628) in combination with re-irradiation (35 Gy in 10#) in patients with recurrent GBM, and with first-line radical radiotherapy (60 Gy in 30#) in patients with newly diagnosed, MGMT unmethylated GBM. In parallel, we are undertaking preclinical studies to investigate the impact of PARPi and ATMi on RT induced neurotoxicity. In vivo imaging studies support the emerging concept that RT induced neuroinflammation is important in the pathogenesis of neurotoxicity, and we have generated preliminary behavioral data indicating that both PARPi and ATMi can alleviate the neurotoxic effects of RT. Ongoing experiments are defining the roles of microglia, astrocytes and neurogenesis in this phenomenon. We will present these diverse datasets in the context of our hypothesis that combining PARPi or ATMi with RT has potential to improve outcomes for GBM patients by enhancing tumor control while simultaneously suppressing neuroinflammation and alleviating RT related neurocognitive decline. Citation Format: Anthony J. Chalmers. Multifaceted effects of DNA damage response inhibitors on radiation responses of glioblastoma and the normal brain [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr IA006.

Associations between recurrence patterns and outcome in glioblastoma patients undergoing re-resection: A complementary report of the RANO resect group.

Associations between recurrence patterns and outcome in glioblastoma patients undergoing re-resection: A complementary report of the RANO resect group.

Unlocking Glioblastoma Secrets: Natural Killer Cell Therapy against Cancer Stem Cells.

Glioblastoma (GBM) represents a paramount challenge as the most formidable primary brain tumor characterized by its rapid growth, aggressive invasiveness, and remarkable heterogeneity, collectively impeding effective therapeutic interventions. The cancer stem cells within GBM, GBM stem cells (GSCs), hold pivotal significance in fueling tumor advancement, therapeutic refractoriness, and relapse. Given their unique attributes encompassing self-renewal, multipotent differentiation potential, and intricate interplay with the tumor microenvironment, targeting GSCs emerges as a critical strategy for innovative GBM treatments. Natural killer (NK) cells, innate immune effectors recognized for their capacity to selectively detect and eliminate malignancies without the need for prior sensitization, offer substantial therapeutic potential. Harnessing the inherent capabilities of NK cells can not only directly engage tumor cells but also augment broader immune responses. Encouraging outcomes from clinical investigations underscore NK cells as a potentially effective modality for cancer therapy. Consequently, NK cell-based approaches hold promise for effectively targeting GSCs, thereby presenting an avenue to enhance treatment outcomes for GBM patients. This review outlines GBM's intricate landscape, therapeutic challenges, GSC-related dynamics, and elucidates the potential of NK cell as an immunotherapeutic strategy directed towards GSCs.

10075-CO-3 PREDICTION OF KPS DETERIORATION IN GLIOBLASTOMA USING MULTIMODAL LEARNING WITH PRE- AND POST-OPERATIVE MRI IMAGES AND CLINICAL INFORMATION

Abstract BACKGROUND Previous AI studies on glioblastoma have mostly focused on predicting genetic subtypes or survival outcomes using preoperative imaging alone. We aimed to investigate the feasibility of utilizing postoperative imaging in combination with clinical chart data for multimodal learning, to evaluate systemic functional outcome. METHODS We included 77 glioblastoma cases that received treatment at Kyoto University between December 2001 and January 2022. These cases had both pre- and postoperative MRI images, including T1WI, CE-T1WI, FLAIR, ADC, and DWI. Molecular diagnoses were confirmed for all cases. First, using external data (BRaTS2020), we constructed models for tumor segmentation prediction from MRI images followed by an image feature extraction model using an autoencoder (VAE), extracting 192 image feature variables from preoperative and postoperative images. These variables were combined with 24 clinical variables, and 6 variables of radiologists' findings. We built a CNN model to predict a decrease of 30% or more KPS at 6 months and evaluated its accuracy using the AUC. Feature importance was calculated using PermutationImportance. RESULTS When predicting KPS deterioration using only clinical data, the AUC was 0.79. When combined with radiologists' findings, the AUC increased to 0.84. Combination with preoperative image features resulted in an AUC of 0.78. On the other hand, when combined with both pre- and postoperative image features, the AUC increased to 0.94. Both pre- and post-operative images were identified as important features. DISCUSSION Our findings demonstrate the utility of incorporating postoperative imaging into multimodal learning for predicting systemic functional outcomes in glioblastoma patients. Further validation in independent cases from different medical facilities is warranted to confirm these results.