LINKING TRANSCRIPTIONAL AND MORPHOLOGICAL HETEROGENEITY TO THERAPEUTIC VULNERABILITIES IN GLIOBLASTOMA

This thesis is the result of my doctoral studies conducted within the Iorio Laboratory at Human Technopole and whose overarching goal has been to design and use data-driven approaches for the integration of molecular and functional-genetics data from preclinical models with the aim of prioritizing subtype-specific therapeutics vulnerabilities in Glioblastoma (GBM). Particularly, in an initial phase of my activities, I have designed and implemented computational methods for reliability and reproducibility assessment of “essentiality profiles” from genome-wide recessive pooled CRISPR-Cas9 screens. Furthermore I have contributed methods and analyses for benchmarking state-of-the-art computational methods aiming at preprocessing CRISPR screening data, and correcting technology-specific biases, such as the tendency of the CRISR-Cas9 system to elicit a gene-independent detrimental effect on cellular survival when targeting genomic copy-number amplified regions (CN bias) and spurious effect due to Cas9-mediated whole chromosome arm truncations (proximity bias). Subsequently, I have focused on the integration of transcriptomics and CRISPR-based genome-editing data for prioritizing potential therapeutic targets that regulate Glioblastoma stem cells (GSCs) morphology and survival, in collaboration with the Kalebic research group. A key result of this collaboration has been the identification of adducin-γ (ADD3), a previously known morpho-regulator of human neural progenitor cells, as a context-specific essential gene in GBM. I further investigated ADD3 expression at the single-cell level in GBM primary tumours, where I found it to be associated with an oligodendrocyte-progenitorlike (OPC-like) signature, suggesting a specific role of ADD3 in this transcriptional subtype. Adopting this specific case as a proof-of-concept, I have then designed an analytical framework that integrates pharmacogenomics and CRISPR genome-editing with single cell RNA sequencing (scRNA-seq) data, leveraging the Cancer 13Dependency Map (DEPMAP) resource. This framework aims to prioritize GBM subtypes’ vulnerabilities upon a broad spectrum of genetics and drugs perturbation, aiding in the identification of new potential targets and therapeutic compounds that may be most effective in reducing viability of specific subtypes.