Periodic Reporting for period 4 - iGBMavatars (Glioblastoma Subtype Avatar models for Target Discovery and Biology)
Periodo di rendicontazione: 2022-01-01 al 2023-06-30
Glioblastoma (GBM), a formidable brain cancer, has remained a clinical challenge with limited therapeutic progress despite numerous clinical trials and advancements in tumor biology. The landscape of interventional trials in glioblastoma, with over 1,250 registered trials since 2005, primarily comprises early-phase studies that often lack the depth required to elucidate drug efficacy and resistance mechanisms. A recent and authoritative clinical perspective article contend that the current models for glioblastoma lack the predictive power required to inform effective drug development and treatment strategies, and this underscores the critical need for in-human "window of opportunity" (WoO) trials with serial tissue collection to address challenges in drug development (Singh et al 2023). The perspective also highlights the failures of promising drugs in clinical trials as a result of the lack of biological understanding in glioblastoma (Singh et al 2023). Improving animal models for GBM and the use of such models to advance their predictiveness of both biology and treatment of this disease is the central topic that my lab addressed under the umbrella of the iGBMavatars project (714922 ERC StG). In this project, we aimed to address two major challenges affect glioblastoma clinical management: (i) the tumor heterogeneity (which treatment will best fit this very patient?) and (ii) its resistance to available treatments (will the patient benefit in any way from the chosen therapy?). We have been generating new glioblastoma models reflecting glioblastoma molecular subtypes at molecular level. In these models, we seek to find molecular switches that aggravate the effect of DNA damaging agents, which constitute the standard of care for glioblastoma patients. To identify drug targets favoring patients’ responses to the current standard of care, we exploit our models for state-of-art genetic screens in vivo. To best understand the GBM heterogeneity in vivo, we combine genetic and phenotypic tracing. The overall goal of this project was and continues to be to identify therapeutic improvements that would enhance the response of the patients to the available treatments and to set the basis to discover more effective ones using transformative technologies.
GBM has been recognized as a highly heterogeneous tumor since the first half of the last century (Busch et al., 1947). The significance of glioblastoma intra-tumor and inter-tumor heterogeneity is continually evolving. GBM has been classified into distinct subtypes with specific combinations of genetic alterations, gene expression, methylation profiles, and prognostic survival features. At the single-cell level, GBM was recently reclassified into four states that account for both intra-tumor and inter-tumor heterogeneity. However, the number of cellular and molecular entities present in a single tumor and the interpretation of their boundaries are variable and obviously depend on technologies and thresholds.
Based on our work and that of many groups, one conservative interpretation is that GBM molecular heterogeneity reflects developmental/metabolic cell states/entities, with some level of spatial organization dependent on tissue macroareas. A fundamental question is whether the individual tumor cell identities represent stable and heritable entities or transient cell states. The rigid classification of GBM into subtypes (i.e. close to the entity definition) has evolved into the current plastic cell states classification. Moreover, several clinically relevant covariates, such as differentiation, inflammation, radiotherapy, hypoxia, and infiltration by innate immune cells, have been shown to correlate with glioblastoma phenotypic transitions.
The emerging picture is that GBM subtypes may represent dominant entities within extremely heterogeneous tumors, with mesenchymal glioblastoma being dominant at recurrence. However, causal links between these factors and a specific glioblastoma state have been elusive. In this regard, our work contributed to clarifying the pathophysiological and clinical relevance of one subtype (i.e. the mesenchymal) and its implications for GBM biology and treatment. Mesenchymal glioblastoma is the most aggressive form of these lethal tumors. We showed that this state is adaptive and metastable; it is driven by pro-inflammatory and differentiation cues and DNA damage, but not hypoxia. Importantly, we discovered that innate immune cells promote a proneural-to-mesenchymal transition that confers therapeutic resistance to glioblastoma cells. Thus, we were able to connect cellular and molecular heterogeneity to therapeutic responses. Our views were quickly incorporated into the current conceptualization of the disease, and our claim that innate immune cells drive a mesenchymal glioblastoma transition was later validated in other high-profile work in adult and pediatric brain tumors. These findings were directly informed by the collective work carried out during the iGBMavatar project and the successful achievements of the scientific plan proposed.
Based on our work and that of many groups, one conservative interpretation is that GBM molecular heterogeneity reflects developmental/metabolic cell states/entities, with some level of spatial organization dependent on tissue macroareas. A fundamental question is whether the individual tumor cell identities represent stable and heritable entities or transient cell states. The rigid classification of GBM into subtypes (i.e. close to the entity definition) has evolved into the current plastic cell states classification. Moreover, several clinically relevant covariates, such as differentiation, inflammation, radiotherapy, hypoxia, and infiltration by innate immune cells, have been shown to correlate with glioblastoma phenotypic transitions.
The emerging picture is that GBM subtypes may represent dominant entities within extremely heterogeneous tumors, with mesenchymal glioblastoma being dominant at recurrence. However, causal links between these factors and a specific glioblastoma state have been elusive. In this regard, our work contributed to clarifying the pathophysiological and clinical relevance of one subtype (i.e. the mesenchymal) and its implications for GBM biology and treatment. Mesenchymal glioblastoma is the most aggressive form of these lethal tumors. We showed that this state is adaptive and metastable; it is driven by pro-inflammatory and differentiation cues and DNA damage, but not hypoxia. Importantly, we discovered that innate immune cells promote a proneural-to-mesenchymal transition that confers therapeutic resistance to glioblastoma cells. Thus, we were able to connect cellular and molecular heterogeneity to therapeutic responses. Our views were quickly incorporated into the current conceptualization of the disease, and our claim that innate immune cells drive a mesenchymal glioblastoma transition was later validated in other high-profile work in adult and pediatric brain tumors. These findings were directly informed by the collective work carried out during the iGBMavatar project and the successful achievements of the scientific plan proposed.
During the project, our lab has developed an approach to create genetic tracing reporters for any cell state and transitions from one state to another. From the endogenous cis-regulatory program of 'signature' genes expressed in one cell type or tissue (i.e. its identity or state), we create synthetic DNA reporters (synthetic Locus Control Regions, sLCRs), which are activated when the targeted identity/state is acquired and inactivated when the identity changes. Through machine learning, we have dissected sLCR selectivity in silico, and upon genetic engineering in cells, sLCRs drive a fluorescent reporter gene (i.e. cell state mapping) or an effector protein (e.g. cell state ablation), or both, allowing us to observe or manipulate complex cell states. We propose that this approach will enable systematic genetic and lineage tracing of complex phenotypes, which is more challenging compared to classic lineage-tracing techniques that tag the expression of a single marker. To date, we have been able to 'see' epithelial cells becoming mesenchymal and returning to their original state, discover innate immune cells as drivers of mesenchymal cell state transition and chemoresistance in brain tumor cells, and discover pharmacological modulators of epithelial cell responses to SARS-CoV-2. Furthermore, we have provided a proof-of-concept for genome-scale CRISPR-based phenotypic and drug screens