Glioblastoma multiforme (GBM) is a malignant tumour originating from glial cells. It is the most common and devastating form of brain tumour, containing self-renewing, tumorigenic cancer stem cells (CSCs) that contribute to tumour initiation and therapeutic resistance. It leads to 225,000 deaths per year in the entire world (Bush NA, et al. 2017 Neurosurg). Standard treatment consists of maximal surgical resection, followed by radiotherapy with or without concomitant and adjuvant Temozolomide. Treatment hardly increases patient survival and leads to a median overall survival of only 12–18 months (Stupp R, et al. 2015 JAMA). By contrast to other types of cancers, it appears uncertain that GBM incidence can be decreased by changing certain environmental factors, or anticipated from the presence of another disease or condition (Alphandery E, et al. 2018 Front Pharm).
Although many efforts have been attempted to improve patient’s life for the last 60 years, new methods for diagnosis, prognosis and treatment are needed.
The link between cancer and altered metabolism is not new (Caims RA, et al. 2011 Nat Rev Cancer). One of the best known is the Warburg effect, a metabolic shift towards aerobic glycolysis. Moreover, metabolic changes have been used in cancer detection, e.g. phosphocholine is used in magnetic resonance spectroscopy to diagnose tumour tissues (Hattingen E, et al. 2013 PLoS One), or hyaluronan in the urine, is used as an indicator of a poor prognosis (Deen AJ, et al. 2016 Cell Mol Life Sci). Therefore, metabolomics could be used to find altered metabolites in a pathophysiological situation that consequently could be exploited for early detection. Moreover, it could provide a unique opportunity for finding the cancer Achilles' heel.
To better understand GBM tumour biology, researchers worldwide have turned to high dimensional profiling studies. On 2018 Verhaak and collaborators described several GBM phenotypes; Proneural (PN), Neural, Classical and Mesenchymal (MCh), each with distinguishing hallmark mutations, copy number alterations, epigenetic alterations, and clinical features. Additionally, treatment efficacy differs per subtype. Nevertheless, a full metabolomic profile had not been performed until date.
The first objective of our study was to investigate the underlying metabolic differences between the most extreme phenotypes (PN and MCh). These generate a characteristic fingerprint, not yet reported until now, thereby providing a better understanding of glioma biology. This finding could lead to the development of new strategies to fight the tumour and personalized therapy.
Another difficulty of GBM is that both, confirmation and follow up of the tumour process are restricted by anatomical location. Nevertheless, neural cells are able to release extracellular vesicles (EVs), which cross the blood-brain barrier and could be detected within the blood, offering a potential new way for detection and treatment monitoring. Literature has shown the involvement of EV secreted by GBM cells in tumour growth, angiogenesis, metastasis and immune responses (Kanada M, et al. 2016 Trends in Cancer). So then, the EV composition and its biological function are going to depend on the cell-type origin. The second objective of the project was to study the metabolite profile of EVs released by those toumour subtypes.
Our research has helped to draw the EVs metabolome and to elucidate whether or not the metabolites are directly packaged into specific EVs and their possible function in the surrounding cells. Moreover, it gives the opportunity of finding a metabolite profile characteristic of tumour subtype that could be used as a biomarker.
