Speaker
Description
Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median survival between 14-21 months and no curative treatment currently available [1]. To investigate resistance mechanisms to temozolomide (TMZ), the standard-of-care chemotherapy, we generated perturbed proteomic data (with and without TMZ) for 12 patient-derived cell lines (PDCLs). Pathways enrichment and independent component analysis revealed a high inter-patient heterogeneity. Proteins linked to TMZ response were identified and matched to pharmacological compounds using a new pipeline, leading to 40 promising drugs from an initial screening. These candidates are currently evaluated in a second screening to assess synergistic effects. The next step is to build a digital twin for each PDCL, enabling personalized prediction of combination therapies. A previously developed quantitative systems pharmacology (QSP) model of TMZ pharmacokinetics-pharmacodynamics serves as a foundation [2]. To initiate model individualization, we developed a method to personalize model parameters using publicly available multi-omics and TMZ cytotoxicity data. Current work focuses on integrating proteomics-derived key species into the core model to obtain PDCL-specific digital twins and infer personalized treatment by combining QSP and machine learning. This integrative approach, combining data analysis, network reconstruction, and mechanistic modeling, opens the path for efficient patient specific therapies in GBM.
