Glioblastoma (GBM) is the most aggressive primary brain tumor, with a median survival at diagnosis of 16–20 months. Metabolism represents a new attractive therapeutic target; however, due to high intratumoral heterogeneity, the application of metabolic drugs in GBM is challenging. We characterized the basal bioenergetic metabolism and antiproliferative potential of metformin (MF), dichloroacetate (DCA), sodium oxamate (SOD) and diazo-5-oxo-L-norleucine (DON) in three distinct glioma stem cells (GSCs) (GBM18, GBM27, GBM38), as well as U87MG. GBM27, a highly oxidative cell line, was the most resistant to all treatments, except DON. GBM18 and GBM38, Warburg-like GSCs, were sensitive to MF and DCA, respectively. Resistance to DON was not correlated with basal metabolic phenotypes. In combinatory experiments, radiomimetic bleomycin exhibited therapeutically relevant synergistic effects with MF, DCA and DON in GBM27 and DON in all other cell lines. MF and DCA shifted the metabolism of treated cells towards glycolysis or oxidation, respectively. DON consistently decreased total ATP production. Our study highlights the need for a better characterization of GBM from a metabolic perspective. Metabolic therapy should focus on both glycolytic and oxidative subpopulations of GSCs.

Cancer is one of the leading causes of death worldwide and remains a major public health challenge. The introduction of more sensitive and powerful technologies has permitted the appearance of new tumor-specific molecular aberrations with a significant cancer management improvement. Therefore, molecular pathology profiling has become fundamental not only to guide tumor diagnosis and prognosis but also to assist with therapeutic decisions in daily practice. Although tumor biopsies continue to be mandatory in cancer diagnosis and classification, several studies have demonstrated that liquid biopsies could be used as a potential tool for the detection of cancer-specific biomarkers. One of the main advantages is that circulating free DNA (cfDNA) provides information about intra-tumoral heterogeneity, reflecting dynamic changes in tumor burden. This minimally invasive tool has become an accurate and reliable instrument for monitoring cancer genetics. However, implementing liquid biopsies across the clinical practice is still ongoing. The main challenge is to detect genomic alterations at low allele fractions. Droplet digital PCR (ddPCR) is a powerful approach that can overcome this issue due to its high sensitivity and specificity. Here we explore the real-world clinical utility of the liquid biopsy ddPCR assays in the most diagnosed cancer subtypes.

Aberrant metabolism is recognized as a hallmark of cancer, a pillar necessary for cellular proliferation. Regarding bioenergetics (ATP generation), most cancers display a preference not only toward aerobic glycolysis (“Warburg effect”) and glutaminolysis (mitochondrial substrate level-phosphorylation) but also toward other metabolites such as lactate, pyruvate, and fat-derived sources. These secondary metabolites can assist in proliferation but cannot fully cover ATP demands. The concept of a static metabolic profile is challenged by instances of heterogeneity and flexibility to meet fuel/anaplerotic demands. Although metabolic therapies are a promising tool to improve therapeutic outcomes, either via pharmacological targets or press-pulse interventions, metabolic plasticity is rarely considered. Lack of bioenergetic analysis in vitro and patient-derived models is hindering translational potential. Here, we review the bioenergetics of cancer and propose a simple analysis of major metabolic pathways, encompassing both affordable and advanced techniques. A comprehensive compendium of Seahorse XF bioenergetic measurements is presented for the first time. Standardization of principal readouts might help researchers to collect a complete metabolic picture of cancer using the most appropriate methods depending on the sample of interest.

Glioblastoma (GBM) is one of the most malignant central nervous system tumor types. Comparative analysis of GBM tissues has rendered four major molecular subtypes. From them, two molecular subtypes are mainly found in their glioblastoma cancer stem-like cells (GSCs) derived in vitro: proneural (PN) and mesenchymal (MES) with nodular (MES-N) and semi-nodular (MES-SN) disseminations, which exhibit different metabolic, growth, and malignancy properties. Many studies suggest that cancer cells communicate between them, and the surrounding microenvironment, via exosomes. Identifying molecular markers that allow the specific isolation of GSC-derived exosomes is key in the development of new therapies. However, the differential exosome composition produced by main GSCs remains unknown. The aim of this study was to determine ceramide (Cer) composition, one of the critical lipids in both cells and their cell-derived exosomes, from the main three GSC phenotypes using mass spectrometry-based lipidomics. GSCs from human tissue samples and their cell-derived exosomes were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) in an untargeted analysis. Complete characterization of the ceramide profile, in both cells and cell-derived exosomes from GSC phenotypes, showed differential distributions among them. Results indicate that such differences of ceramide are chain-length dependent. Significant changes for the C16 Cer and C24:1 Cer and their ratio were observed among GSC phenotypes, being different for cells and their cell-derived exosomes.

The isolation of circulating tumoral DNA (ctDNA) present in the bloodstream brings about the opportunity to detect genomic aberrations from the tumor of origin. However, the low amounts of ctDNA present in liquid biopsy samples makes the development of highly sensitive techniques necessary to detect targetable mutations for the diagnosis, prognosis, and monitoring of cancer patients. Here, we employ standard genomic DNA (gDNA) and eight liquid biopsy samples from different cancer patients to examine the newly described CRISPR-Cas13a-based technology in the detection of the BRAF p.V600E actionable point mutation and appraise its diagnostic capacity with two PCR-based techniques: quantitative Real-Time PCR (qPCR) and droplet digital PCR (ddPCR). Regardless of its lower specificity compared to the qPCR and ddPCR techniques, the CRISPR-Cas13a-guided complex was able to detect inputs as low as 10 pM. Even though the PCR-based techniques have similar target limits of detection (LoDs), only the ddPCR achieved a 0.1% variant allele frequency (VAF) detection with elevated reproducibility, thus standing out as the most powerful and suitable tool for clinical diagnosis purposes. Our results also demonstrate how the CRISPR-Cas13a can detect low amounts of the target of interest, but its base-pair specificity failed in the detection of actionable point mutations at a low VAF; therefore, the ddPCR is still the most powerful and suitable technique for these purposes.