Abstract 7432: Transformer-based glioma patient recurrence prediction model infers novel genetic markers

Abstract Approximately 90% of glioma patients experience recurrence after initial treatment, leading to poor prognosis and short survival time. While established genetic markers, such as IDH mutation and 1p/19q codeletion, are linked to glioma prognosis, there is a critical need to identify novel targets associated with recurrence to overcome therapeutic challenges. In this study, we developed a transformer-based deep learning model to predict glioma recurrence by integrating multi-omics data, including mutations and copy number variations, from the Glioma Longitudinal Analysis (GLASS) Consortium. Our model outperformed traditional machine learning approaches, including support vector machines, logistic regression, and decision trees, achieving the best performance when utilizing mutational features alone, while copy number variations were less informative. Feature importance analysis identified key contributors to glioma recurrence, including frequently mutated genes such as TP53, ATRX, and PTEN, as well as novel markers like MUC16, TTN, and CIC. Our approach represents an early application of state-of-the-art deep learning to dissect the genomic drivers of glioma recurrence, offering valuable insights for developing targeted therapies to improve patient outcomes. Citation Format: Ko-Hong Lin, Kai Zhang, Dung-Fang Lee, Xiaoqian Jiang. Transformer-based glioma patient recurrence prediction model infers novel genetic markers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7432.