Background: This proposal aims at investigating treatment resistance mechanisms in glioblastoma (GBM), the most aggressive and incurable brain tumor. GBMs resist extensive standard-of-care treatment and only 5.7% of patients survive longer than 5 years. Results from targeted therapies are also disappointing, suggesting activation of escape mechanisms. It is still unclear which mechanisms allow GBMs to escape different therapeutics, including radio-chemotherapy and targeted therapies.
Hypothesis: GBM cells display strong intrinsic plasticity and adapt reversibly to microenvironmental conditions, forming a dynamic ecosystem. The role of GBM plasticity in creating resistant states upon treatment is still elusive. We hypothesize that high plasticity allows GBM persister cells to adapt dynamically towards drug resistant states upon treatment. We posit that treatment can simultaneously modulate microenvironment, leading to an overall resistant ecosystem. Such alterations may lead to a long-term evolution of recurrent tumors.
Aims: Here we will investigate molecular mechanisms allowing GBM cells to adapt to treatment pressure in time and space. We aim to identify novel therapeutic strategies against intrinsic plasticity of persister cells. Our aims are (i) to reveal the dynamic adaptation of the GBM ecosystem in tumor and non-tumor cells during treatment and the long-term consequences at recurrence; (ii) to identify molecular regulators of adaptive resistance mechanisms as novel therapeutic targets; (iii) to validate novel biomarkers and combinatory treatment strategies in patient-derived preclinical models.
Methods: We will investigate resistance to standard-of-care chemotherapy and targeted therapies. Using spatial transcriptomics, we will interrogate longitudinal changes in patient tumor cohort with paired samples prior and after treatment. The dynamic adaptation to treatment in time will be functionally assessed in a cohort of patient-derived organoids, including cell death, proliferation and dormancy. Treatment in patient-derived orthotopic xenografts combined with spatial transcriptomics will assess transcriptomic changes in time and space in tumor cells and tumor microenvironment. Epigenetic mechanisms will be examined with single cells ATAC-seq. Genetic evolution will be verified by exome sequencing. Innovative machine learning approaches will reveal biomarkers of resistance and regulators of plasticity, which will be validated at the protein level in a spatial context and functionally in co-treatment efficacy study. Expected results and impact: This project will bring better understanding of the role of tumor plasticity in GBM resistance. Our analyses will elucidate therapeutic targets in tumor and non-tumor cells for next-generation combinatorial treatments. Assessing tumor composition prior and after treatment may reveal predictive biomarkers of response to treatment to improve stratification of patients for personalized therapies.
