Projeto Portugal 2030

Sumário

Overall, it is clear that tumors rewire their metabolism and become heterogeneous, although how these two features correlate, especially in brain tumors, remains unclear. Our preliminary results point out that LDH heterogeneity occurs in brain tumors (Fig 1) and that LDH function is essential for tumor growth (Fig 2A). Nonetheless, how this heterogeneity is regulated and associated with tumor progression remains an open question. Thus, the CENTRAL AIM of this proposal is to reveal how LDH heterogeneity is regulated and supports the metabolic requirements of brain tumor cells to ultimately correlate metabolic heterogeneity with glioblastoma progression. In specific, the proposed objectives are to (see graphical abstract): 1) Dissect the mechanism by which HIF1 pathway regulates LDH expression in tumor cell subsets Our preliminary data points out that HIF1 Hypoxia inducible factor 1, the regulator of the molecular hypoxic response[12], mediates LDH upregulation (Fig 3). Hence, we will unravel if the mechanism by which HIF1 pathway mediates LDH upregulation is oxygen-dependent (e.g tumor hypoxic niches) or independent (e.g. signalling mechanisms derived from oncogenes). 2) Characterize the functional role of LDH upregulation in a subset of tNBs in brain tumors. To understand how LDH metabolic heterogeneity contributes to tumor progression, we aim to perform and integrate the transcriptomic and metabolomic profiles of tumor cells with high and low LDH expression. This will allow us to produce a global integrative view of the expression profile of metabolic enzymes/transporters and their metabolic content to ultimately correlate with proliferative signatures in each cell population. With this data we then aim to experimentally confirm whether differentially expressed genes/active metabolic pathways between LDH-High and LDH-Low tNBs have a functional relevance to brain tumor progression and LDH heterogeneity. Additionally, we aim to analyse the role of LDH heterogeneity in tumor metabolism by characterizing lactate dynamics in brain tumors. 3) Determine if LDH and metabolic heterogeneity are conserved in human glioblastoma and if it correlates with disease severity. To assess if metabolic heterogeneity is conserved in human glioblastomas and if differential LDH expression is a common factor, we aim to perform the bioinformatic analysis of already generated single cell transcriptome datasets of human glioblastoma biopsies[13]. With this analysis, we will be able to produce a global characterization of the metabolic heterogeneity in glioblastoma and understand if certain subpopulations are associated with a specific metabolic profile. Additionally, we will analyse an independent dataset of single-nucleus RNA sequencing of profiled paired specimens from human primary untreated glioblastoma tumors and matched first recurrence[14] to understand if metabolic heterogeneity can be correlated with resistance to therapy. This analysis will help the identification of prognostic markers and metabolic dependencies in specific subsets of cells that might appear after tumor recurrence. This project presents a novel way of tackling metabolic heterogeneity in glioblastoma, by using two approaches that combine a mechanistic and bioinformatic study. Additionally, this proposal will provide an incredible amount of data and metabolic profiles, both in fly and human brain tumors, that will be available for the scientific community for further exploration.