P05.16.b Defining the Optimal MGMT Methylation Cutoff and Its Correlation With Significant Genetic Alterations

Abstract BACKGROUND glioblastoma is the deadliest primary malignancy of the central nervous system. MGMT promoter methylation is one of the few recognized prognostic markers related to temozolomide response. The aim of our study is to identify a more accurate MGMT methylation cutoff and assess the prognostic relevance of the most recurrent genetic alterations. MATERIAL AND METHODS we defined the MGMT methylation cutoff using a receiver operating characteristic (ROC) curve; the area under the curve (AUC) and Youden index were also calculated. We identified the cutoff with the highest likelihood ratio (LR+) and accuracy by testing methylation values in the 10%-25% range. NGS data from 78 patients diagnosed with glioblastoma between 2020 and 2024 at the University Hospital “Santa Maria della Misericordia” in Udine were analyzed. We compared overall survival (OS) between methylated and unmethylated groups, as well as between patients with the most common genetic alterations (TP53, PTEN, and EGFR amplification) using the Kaplan-Meier method and the log-rank test. A multivariate analysis by Cox regression model was performed to identify genetic alterations having a significant correlation with MGMT promoter methylation. RESULTS in our dataset the optimal methylation cutoff was 25%, with a LR+ of 2.54, an accuracy of 0.73, and a Youden index of 0.3, a significant improvement when compared to the 10% cutoff used at our center (LR+ = 1.83; accuracy = 0.62). Based on the new cutoff, 20 patients were classified as methylated and 59 as unmethylated. Kaplan-Meier analysis showed that methylation status was significantly associated with better OS (HR 0.54, 95% CI: 0.29-0.98, p = 0.041). The most common genetic alterations in our population were TP53 (N = 21), PTEN (N = 25), and EGFR amplification (N = 22). The log-rank test showed a significant difference in OS only for the TP53-mutated group (p = 0.011). We performed a multivariate Cox regression analysis including MGMT methylation status and TP53 mutation, confirming that this mutation is an independent prognostic factor (HR: 0.44, 95% CI: 0.24-0.81; p = 0.009). CONCLUSION our analysis suggests that a more precise definition of MGMT methylation cutoff is needed to improve its prognostic accuracy. Noteworthy the correlation between TP53 mutation and OS highlights the potential role of NGS analysis in the prognostic stratification of patients.