An improved human organoid model that captures the plasticity of glioblastoma

We aim to develop a dynamic in silico and in vitro model of the evolutionary progression of cancer to better understand patient outcome, development of therapeutic resistance, and mechanisms of treatment evasion and recurrence.

We will focus on glioblastoma (GBM), a molecularly heterogeneous and highly aggressive brain tumor, with a median survival of merely 15 months.

Genomic sequencing studies have revealed that gliomas are highly heterogeneous tumors, with lineage tracing studies indicating that the cancer cells consist of many clones.

Individual clones can adapt to treatment by changing phenotype or migrating to a more favorable tumor niche, and recurrences are often derived from minor clones. In order to combat this highly adaptive malignant disease, novel methods for drug screening are needed.

Existing systems used to predict benefits of drug treatments, such as laboratory animals and cultured cancer stem cells, are insufficient in that they do not mimic human GBM tumor architecture and complexity, nor retain clinical features or biomarkers.

Our aim is to develop patient-specific organoid models, that not only can replace a large number of animal experiments, but that will also be superior to animal models.

We will use them to experimentally plot the progression of pre-treatment samples to post-treatment recurrence and conditions, and develop a computational model for predicting patient response and disease progression.