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Applying Metabolomics to Unveil follow-up treatment biomarkers and Identify Novel Therapeutic Targets in Glioblastoma

Periodic Reporting for period 1 - MaGMa (Applying Metabolomics to Unveil follow-up treatment biomarkers and Identify Novel TherapeuticTargets in Glioblastoma)

Reporting period: 2018-04-04 to 2020-04-03

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.
MaGMa has identified metabolomic profiles of CSCs isolated from fresh human GBM surgical samples and the differences between metabolomic fingerprints of the most extreme phenotypes (MCh and PN). This has provided a better understanding of glioma biology, showing the cellular pathways altered on PN and MCh. We believe these results will conduct the development of new strategies to fight the tumour.

We have characterized the metabolomic profile of the EVs released from those phenotypes. The study has revealed the enrichment of specific metabolites depending on the phenotype.This information could shed light on the existence of a specific metabolite trafficking mechanism. Additionally, the presence of a specific metabolite (or set of metabolites) can be used as biomarkers. We have used a statistical approach to generate a predictive model with a set of metabolites. This fact brings us closer to the development of more personalized medicine where, depending on the tumour phenotype, targeted treatments could be provided.

During MaGMa execution, it has been necessary to improve the EV isolation and set up the protocol of EV extraction for chromatography, and to create a protocol for liquid and gas chromatography. Additionally, to test whether hyaluronan (HA) coat could be used as a possible marker for tumour EV, we have developed a targeted methodology for measuring the precursor metabolites of HA in EVs.

To discover whether Temozolomide, the current clinical treatment, induces a significant change in the metabolomic pattern of CSC cultures and CSCs-released EVs, we have studied the EC50 (concentration of a drug that gives half-maximal response) for the different phenotypes.

Partial results of the project were communicated in a national meeting at the 1st Interuniversity Conference CEU (2020).

Complementary results will be published soon in “Cancer and Metabolism” and “Journal of Extracellular Vesicles”. A review article including the state-of-the-art methodology utilized for EVs untargeted and targeted metabolomics is expected to be submitted before December 2020 in “J Proteome Res”.
Basic and clinical science integration has been carried out with a novel approach using a multiplatform untargeted metabolic fingerprinting with clinical data. This has included innovative approaches for data pre-processing, data treatment, modern statistical chemometric methodologies and data integration tools. Our project has combined basic research with clinical data and state-of-the-art metabolomics, to provide deeper insights in the etiopathogenesis of GBM cells and the EV released from those cells.

Data from the comparison of profiles from different phenotypes could be used to identify specific metabolic changes leading to the understanding of physiology, and disease progression and could lead to the establishment of biomarkers. We believe the study of their metabolism could orientate scientists and clinicians on the path of a faster identification and could provide more individualized treatments of GBM patients. This tool could improve the early detection and by consequence the recovery of the patients. Socio-economic impact include a reduction of indirect costs and a better chance of patient's survival.

The new knowledge generated by this project has been used to raise awareness about GBM and the need of financial support for research from public and private institutions. We have stressed the importance of addressing “old” diseases, which are not currently curable, with a new perspective from the field of metabolomics. With our results, we also expect to make metabolomics and its possibilities more interesting for the research community.
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