Prospective Biomarker Study in Newly Diagnosed Glioblastoma: Cyto-C Clinical Trial

Background Glioblastoma (GBM) has a 5-year survival rate of 3–5%. GBM treatment includes maximal resection followed by radiotherapy with concomitant and adjuvant temozolomide (TMZ). Cytochrome c oxidase (CcO) is a mitochondrial enzyme involved in the mechanism of resistance to TMZ. In a prior retrospective trial, CcO activity in GBMs inversely correlated with clinical outcome. The current Cyto-C study was designed to prospectively evaluate and validate the prognostic value of tumor CcO activity in patients with newly diagnosed primary GBM, and compared to the known prognostic value of MGMT promoter methylation status. Methods This multi-institutional, blinded, prospective biomarker study enrolled 152 patients with newly diagnosed GBM who were to undergo surgical resection and would be candidates for standard of care. The primary end point was overall survival time (OS), and the secondary end point was progression-free survival time (PFS). Tumor CcO activity and MGMT promoter methylation status were assayed in a centralized laboratory. Results OS and PFS did not differ by high or low tumor CcO activity, and the prognostic validity of MGMT promoter methylation was confirmed. Notably, a planned exploratory analysis suggested that the combination of low CcO activity and MGMT promoter methylation in tumors may be predictive of long-term survival. Conclusions Tumor CcO activity alone was not confirmed as a prognostic marker in GBM patients. However, the combination of low CcO activity and methylated MGMT promoter may reveal a sub-group of GBM patients with improved long term survival that warrants further evaluation. Our work also demonstrates the importance of performing large, multi-institutional, prospective studies to validate biomarkers. We also discuss lessons learned in assembling such studies.

Immunotherapeutic early-phase clinical trials and malignant gliomas: a single-center experience and comprehensive immunophenotyping of circulating leukocytes

Background Immunotherapeutic early-phase clinical trials (ieCTs) increasingly adopt large expansion cohorts exploring novel agents across different tumor types. High-grade glioma (HGG) patients are usually excluded from these trials. Methods Data of patients with recurrent HGGs treated within multicohort ieCTs between February 2014 and August 2019 (experimental group, EG) at our Phase I Unit were retrospectively reviewed and compared to a matched control group (CG) of patients treated with standard therapies. We retrospectively evaluated clinical, laboratory, and molecular parameters through univariate and multivariate analysis. A prospective characterization of circulating leukocyte subpopulations was performed in the latest twenty patients enrolled in the EG, with a statistical significance cutoff of p <0.1. Results Thirty HGG patients were treated into six ieCTs. Fifteen patients received monotherapies (anti PD-1, anti CSF-1R, anti TGFβ, anti cereblon), fifteen patients combination regimens (anti PD-L1 + anti CD38, anti PD-1 + anti CSF-1R). In the EG, median progression-free survival and overall survival (OS) from treatment initiation were 1.8 and 8.6 months; twelve patients survived more than 12 months, and two of them more than six years. Univariate analysis identified O 6-methylguanine DNA methyltransferase (MGMT) promoter methylation and total protein value at six weeks as significantly correlated with a better outcome. Decreased circulating neutrophils and increased conventional dendritic cells levels lead to significantly better OS. Conclusions A subgroup of EG patients achieved remarkably durable disease control. MGMT promoter methylation identifies patients who benefit more from immunotherapy. Monitoring dynamic changes of innate immune cell populations may help to predict clinical outcomes.

NCOG-27. STATUS AS A CLINICAL TRIAL PARTICIPANT AND OUTCOME IN IDH-WILDTYPE GLIOBLASTOMA

INTRODUCTION Standard of care for glioblastoma consists of surgery, followed by combined chemoradiation and adjuvant chemotherapy, as per the seminal EORTC study from 2005. Clinical trial patients, being a population selected for functional status, hepatic function, renal function, and lack of other malignancies, may have improved outcome over the general treated population. METHOD Single center retrospective analysis of status as a clinical trial patient in the upfront setting and other clinical factors/biomarkers, analyzed for correlation with outcomes (PFS/OS) in IDH-wildtype glioblastomas. RESULTS 82 patients with IDH-wildtype glioblastoma were identified between 2014 and 2020, treated with standard of care or with an upfront clinical study (43% women; median age 66 years, range 35-91 years of age). 22 patients (27%) were treated with upfront clinical study. Status as a patient treated in an upfront clinical study did not correlate with outcome (hazard ratio HR PFS 0.99, CI 0.57-1.7, p=0.97; HR OS 1.09, CI 0.56-2.1, p=0.81). Frontal lobe was most frequently involved (n=36, 44%), followed by parietal lobe (n=33, 40%). Age was not a strong predictor of survival (R2 0.01). No statistically significant correlation was observed between outcome and laterality or location. MGMT promoter methylation was associated with improved PFS (HR 0.56, CI 0.33-0.94, p=0.03) and OS (HR 0.40, CI 0.19-0.85, p=0.02), with mPFS 6 months vs 9 months and mOS 16 months vs 20 months (unmethylated vs methylated respectively). CONCLUSION In this retrospective cohort of IDH-wildtype glioblastomas, age, tumor laterality, and tumor location were not significant predictors of outcome. MGMT promoter methylation predicted for superior PFS/OS. Patient selection for clinical studies are influenced by entry criteria, however at least in this retrospective review, status as a clinical study patient in the upfront setting did not correlate with outcome compared to patients treated with upfront standard of care.

BIOM-07. QUANTITATIVE MGMT PROMOTER METHYLATION INDEX INDICATES A NON-LINEAR PROGNOSTIC EFFECT IN GLIOBLASTOMA, SUGGESTING THAT USE OF OPTIMAL CUTOFF POINTS MAY BE CLINICALLY DISADVANTAGEOUS

BACKGROUND Epigenetic inhibition of the O6-methylguanine-DNA-methyltransferase (MGMT) gene has emerged as a clinically relevant prognostic marker in glioblastoma (GBM). Methylation of the MGMT promoter has been shown to increase chemotherapy efficacy. While traditionally reported as a binary marker, recent methodological advancements have led to quantitative approaches that measure methylation, providing clearer insights into methylation’s functional relationship with survival. METHODS A CLIA assay and bisulfite sequencing was utilized to develop a quantitative, 17-point MGMT promoter methylation index derived from the number of methylated CpG sites. Retrospective review of 240 newly diagnosed GBM patients was performed in order to discern how risk for mortality transforms as promoter methylation increases. Non-linearities were captured by fitting splines to Cox proportional hazard models, plotting smoothed residuals, and creating survival plots. Covariates included age, KPS, IDH1 mutation, and extent of resection. RESULTS Median follow-up time and progression free survival were 16 and 9 months, respectively. 176 subjects experienced death. A one-unit increase in CpG methylation on a scale of 1-17 resulted in a 4% reduction in hazard (95% CI 0.93–0.99, P< 0.005). Moreover, GBM patients with low-levels of methylation (1-6 CpG sites) fared markedly worse (HR=1.62, 95% CI 1.03-2.54, P< 0.036) than individuals who were unmethylated (reference group). Subjects with medium-levels of methylation (7-12 CpG sites) had the greatest reduction in hazard (HR=0.48, 95% CI 0.29-0.80, P< 0.004), followed by individuals in the highest methylation tertile (HR=0.62, 95% CI 0.40-0.97, P< 0.035). CONCLUSION This novel approach offers greater bisulfite conversion efficiency when compared to alternative methods, reducing the likelihood of false positives. Analysis of the resulting methylation index scores demonstrates a non-linear relationship between MGMT methylation and survival, suggesting conformation of the marker’s protective effect. These findings challenge the current understanding of MGMT’s functional form and underline why implementing an “optimal cutoff point” may be disadvantageous.

NIMG-73. CAPTURING GLIOBLASTOMA HETEROGENEITY USING IMAGING AND DEEP LEARNING: APPLICATION TO MGMT PROMOTER METHYLATION

PURPOSE Intratumor heterogeneity is frequent in glioblastoma (GB), giving rise to the tumor’s resistance to standard therapies and, ultimately, poorer clinical outcomes. Yet heterogeneity is often not quantified when assessing the genomic or methylomic profile of a tumor, when a single tissue sample is analyzed. This study proposes a novel approach to non-invasively characterize heterogeneity across glioblastoma using deep learning analysis MRI scans, using MGMT promoter methylation (MGMTpm) as a test-case, and validates the imaging-derived heterogeneity maps with MGMTpm heterogeneity measured via multiple tissue samples. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR) of 181 patients with newly diagnosed glioblastoma, who underwent surgical tumor resection and had MGMT methylation assessment results, were retrospectively collected. We trained a 5-fold cross-validated deep convolutional neural network with six convolutional layers for a discovery cohort of 137 patients by placing overlapping regional patches over the whole tumor on mpMRI scans to capture spatial heterogeneity of MGMTpm status in different regions within the tumor. Our approach effectively hypothesized that despite heterogeneity in the training examples, dominant imaging patterns would be captured by deep learning. Trained model was independently applied to an unseen replication cohort of 44 patients, with multiple tissue specimens chosen from different spatial regions within the tumor, allowing us to compare imaging- and tissue-based MGMTpm estimates. RESULTS Our model yielded AUC of 0.75 (95% CI: 0.65–0.79) for global MGMT status prediction, which reflected the heterogeneity in MGMTpm, but also that a dominant imaging pattern of MGMT methylation seemed to emerge. In methylated patients with multiple tissue samples, a significant Pearson's correlation coefficient of 0.64 (p< 0.05) was found between imaging-based heterogeneity maps and MGMTpm heterogeneity. CONCLUSION A novel method based on mpMRI and deep neural networks yielded imaging-based heterogeneity maps that strongly associated with intratumor molecular heterogeneity in MGMT promoter methylated tumors.

Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant

Purpose In glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant. Methods From December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features. Results The micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains. Conclusion We built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers’ prediction probability.

Artificial intelligence for early prediction of treatment response in glioblastoma

Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.