Abstract A040: Network Analysis and Deep Learning Identify Cascade Vulnerabilities in Glioblastoma Stem Cell Plasticity

Abstract

Glioblastoma (GBM) is the most aggressive primary brain tumor and remains universally fatal despite advances in surgery, radiation, and chemotherapy. This poor prognosis is largely driven by glioblastoma stem cells (GSCs), which evade therapy through transcriptional plasticity, which reprograms their cellular state via interconnected regulatory networks. Previous targeting of single pathways has failed due to compensatory redundancy within these networks. We developed a computational framework integrating transcriptomic data, network topology analysis, and deep learning to identify multi-target strategies. RNA-seq data from 546 GBM patients across The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts were analyzed using partial correlation and cross-cohort meta-analysis to identify stable regulatory interactions independent of major driver mutations. Network topology defined hub genes, and in silico perturbation modeled multi-gene inhibition. Two deep learning models were trained on full transcriptomes and stratified patients and predicted sensitivity to network-targeted interventions respectively. Structural modeling and molecular docking evaluated binding of a bispecific single chain fragment variable (scFv) targeting CD44 and CD133, major markers of GSCs. An HSV-1–based oncolytic virus was computationally designed to deliver shRNAs targeting EZH2, KDM1A, and DNMT1, along with miR-124 and inhibitors of NOTCH1 and STAT3 signaling. Network analysis identified reproducible hubs centered on MYC and NOTCH1. Additional hub genes (EZH2, DNMT1, KDM1A, STAT3) were associated with therapeutic resistance and poor survival. Patient stratification revealed a high-plasticity subgroup with worse survival (HR 1.53, p < 0.01) and elevated MYC and STAT3 associated programs. Molecular docking confirmed strong binding of the bispecific scFv to CD44 and CD133 (scores < −300 in HDOCK). The deep learning model demonstrated robust performance (AUC 0.96 internal; 0.857 cross-dataset). GBM resistance appears to arise from interconnected regulatory hubs rather than isolated pathways. Computational modeling suggests that simultaneous disruption of MYC and NOTCH1-centered networks may destabilize multiple resistance mechanisms. This study provides a systems-level framework for identifying multi-target therapeutic strategies and supports the conceptual development of network-directed oncolytic approaches for GSC-driven GBM. Experimental validation will be required to confirm these predictions. Generative AI was used to assist in editing and refining the text of this abstract. Darsh Dadhich. Network Analysis and Deep Learning Identify Cascade Vulnerabilities in Glioblastoma Stem Cell Plasticity [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A040.