Glioblastoma Metabolism: Insights and Therapeutic Strategies

Tumor metabolism is emerging as a potential target for cancer therapies. This new approach holds particular promise for the treatment of glioblastoma, a highly lethal brain tumor that is resistant to conventional treatments, for which improving therapeutic strategies is a major challenge. The presence of glioma stem cells is a critical factor in therapy resistance, thus making it essential to eliminate these cells for the long-term survival of cancer patients. Recent advancements in our understanding of cancer metabolism have shown that glioblastoma metabolism is highly heterogeneous, and that cancer stem cells exhibit specific metabolic traits that support their unique functionality. The objective of this review is to examine the metabolic changes in glioblastoma and investigate the role of specific metabolic processes in tumorigenesis, as well as associated therapeutic approaches, with a particular focus on glioma stem cell populations.

redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and Applications in Cancer

The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP, ATP1A2, IGFBPL1, and ALDOC, and remain silenced for quiescent markers such as ID3. Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH.

Clinical development of cancer therapeutics that target metabolism

Glucose and glutamine metabolism in cancer cells are markedly elevated relative to non-transformed normal cells. This metabolic reprogramming enables the production of adenosine triphosphate and the anabolic precursors needed for survival, growth and motility. The recent observations that mutant oncogenic proteins and the loss of tumor suppressors activate key metabolic enzymes suggest that selective inhibition of these enzymes may yield effective cancer therapeutics with acceptable toxicities. In support of this concept, pre-clinical studies of small molecule antagonists of several metabolic enzymes in tumor-bearing mice have demonstrated reasonable therapeutic indices. We will review the rationale for targeting metabolic enzymes as a strategy to treat cancer and will detail the results of several recent clinical trials of metabolic inhibitors in advanced cancer patients.