Glioblastoma is the most aggressive form of brain cancer, with a median overall survival rate of 15 months, which has remained virtually unchanged for three decades. Standard of care treatments for patients diagnosed with Glioblastoma involves surgery, radiotherapy and temozolomide administration. However, tumour resistance and recurrence occur in almost every case of Glioblastoma, highlighting a desperate need for new treatment options.
The high inter and intra-tumoral heterogeneity of glioblastoma tumours occurs due to several factors, including sex, brain anatomy, histopathology, and transcriptional programs. Moreover, single-cell RNAseq data of tumour biopsies further show that these tumours form a complex microenvironment comprising a mixed population of tumour cells (~15%) in different transcriptional states and non-malignant cells.
Yet, our increased knowledge of glioblastoma tumour heterogeneity has not translated to the clinic, where the 'one-size-fits-all' approach is applied to every patient.
In this talk, I will present data from our laboratory exploiting artificial intelligence, single-cell seq, spatial-transcriptomics, bioinformatics and patient-derived tumour organoids that have helped us resolve tumour heterogeneity to identify potential new targets for the development of personalised therapies for Glioblastoma.