Background: We will investigate treatment resistance mechanisms in glioblastoma (GBM), the most aggressive brain tumor. It is still unclear which mechanisms allow GBMs to escape therapeutics, including targeted therapies.
Hypothesis: GBM display strong intrinsic plasticity and adapt reversibly to microenvironments, forming a dynamic ecosystem. The role of plasticity in creating resistant states upon treatment is elusive. We hypothesize that high plasticity allows GBM persister cells to adapt dynamically to resistant states upon treatment. Treatment may simultaneously modulate microenvironment, leading to an overall resistant ecosystem. Such alterations may lead to a long-term evolution upon recurrence.
Aims: We will investigate molecular mechanisms allowing GBM to adapt to treatment in time and space. We aim (i) to reveal the dynamic adaptation of the GBM ecosystem during treatment and the long-term consequences at recurrence; (ii) to identify molecular regulators of plasticity as therapeutic targets; (iii) to validate novel biomarkers and combinatory treatment strategies in patient avatars.
Methods: We will investigate resistance to standard-of-care chemotherapy and targeted therapies (EGFR, CDK4/6). Spatial transcriptomics will reveal longitudinal changes in patients after treatment. Dynamic adaptation to treatment in time and space will be assessed in patient-derived organoids and xenografts. Molecular mechanisms will be examined genetics and epigenetic levels. Machine learning approaches will reveal biomarkers of resistance and regulators of plasticity, which will be validated by spatial multiplexing and in co-treatment efficacy study.
Expected results and impact: PLASTIG will bring better understanding of the role of plasticity in GBM resistance. We will elucidate therapeutic targets for next-generation combinatorial treatments and predictive biomarkers of treatment response to improve stratification of patients for personalized therapies.