A multi-omics based single-cell strategy to overcome therapy-driven resistance in Glioblastoma: An entry-point for personalized treatment

Malignant brain tumors, including low grade (LGGs) and high grade (HGG) glioma, are a highly heterogeneous disease with a very poor clinical outcome. In particular, Glioblastoma multiforme (GBM) displays very strong inter-patient but also intra-tumoral heterogeneity. Despite aggressive therapy including surgical resection, radiation and chemotherapy with Temoyolomide (TMZ), GBM patients show a very rapid disease progression with only a mean life expectancy of 12-15 months. Genetic heterogeneity is one of the major hallmarks of cancer responsible for inevitable tumor relapse, where resistant clones undergo selection and further evolution upon therapeutic pressure. Molecular stratification and classification of gliomas into global subtypes represent a first step towards precision medicine for brain cancer, which is also reflected in the newly released World Health Organization (WHO) classification of brain tumors. Nevertheless, the current clinical diagnostic still lacks reasonable resolution of intra-tumor heterogeneity for optimized and personalized treatment strategies. Novel single-cell based comprehensive multi-omics approaches appear as a promising strategy to overcome this obstacle. The current project aims to establish strategies for future clinical interrogation of intra-tumoral heterogeneity. We plan to analyze paired longitudinal GBM patient specimen of initial and recurrent tumors at a single-cell level to reveal genetic and transcriptomic identities contributing to disease progression and therapy-related resistance. At the same time we will establish patient-derived xenografts (PDXs) from initial and relapse GBM samples of the same patients to confirm these models as appropriate 'avatars' recapitulating patient intra-tumoral heterogeneity and treatment resistance. These PDXs will enable pre-clinical personalized treatment approaches overcoming resistance associated with heterogeneity and clonal evolution.