Regulation of hypoxia-induced autophagy in glioblastoma involves ATG9A.

BACKGROUND: Hypoxia is negatively associated with glioblastoma (GBM) patient survival and contributes to tumour resistance. Anti-angiogenic therapy in GBM further increases hypoxia and activates survival pathways. The aim of this study was to determine the role of hypoxia-induced autophagy in GBM. METHODS: Pharmacological inhibition of autophagy was applied in combination with bevacizumab in GBM patient-derived xenografts (PDXs). Sensitivity towards inhibitors was further tested in vitro under normoxia and hypoxia, followed by transcriptomic analysis. Genetic interference was done using ATG9A-depleted cells. RESULTS: We find that GBM cells activate autophagy as a survival mechanism to hypoxia, although basic autophagy appears active under normoxic conditions. Although single agent chloroquine treatment in vivo significantly increased survival of PDXs, the combination with bevacizumab resulted in a synergistic effect at low non-effective chloroquine dose. ATG9A was consistently induced by hypoxia, and silencing of ATG9A led to decreased proliferation in vitro and delayed tumour growth in vivo. Hypoxia-induced activation of autophagy was compromised upon ATG9A depletion. CONCLUSIONS: This work shows that inhibition of autophagy is a promising strategy against GBM and identifies ATG9 as a novel target in hypoxia-induced autophagy. Combination with hypoxia-inducing agents may provide benefit by allowing to decrease the effective dose of autophagy inhibitors.

Comprehensive analysis of glycolytic enzymes as therapeutic targets in the treatment of glioblastoma.

Major efforts have been put in anti-angiogenic treatment for glioblastoma (GBM), an aggressive and highly vascularized brain tumor with dismal prognosis. However clinical outcome with anti-angiogenic agents has been disappointing and tumors quickly develop escape mechanisms. In preclinical GBM models we have recently shown that bevacizumab, a blocking antibody against vascular endothelial growth factor, induces hypoxia in treated tumors, which is accompanied by increased glycolytic activity and tumor invasiveness. Genome-wide transcriptomic analysis of patient derived GBM cells including stem cell lines revealed a strong up-regulation of glycolysis-related genes in response to severe hypoxia. We therefore investigated the importance of glycolytic enzymes in GBM adaptation and survival under hypoxia, both in vitro and in vivo. We found that shRNA-mediated attenuation of glycolytic enzyme expression interfered with GBM growth under normoxic and hypoxic conditions in all cellular models. Using intracranial GBM xenografts we identified seven glycolytic genes whose knockdown led to a dramatic survival benefit in mice. The most drastic effect was observed for PFKP (PFK1, +21.8%) and PDK1 (+20.9%), followed by PGAM1 and ENO1 (+14.5% each), HK2 (+11.8%), ALDOA (+10.9%) and ENO2 (+7.2%). The increase in mouse survival after genetic interference was confirmed using chemical inhibition of PFK1 with clotrimazole. We thus provide a comprehensive analysis on the importance of the glycolytic pathway for GBM growth in vivo and propose PFK1 and PDK1 as the most promising therapeutic targets to address the metabolic escape mechanisms of GBM.

Gene set based integrated data analysis reveals phenotypic differences in a brain cancer model.

A key challenge in the data analysis of biological high-throughput experiments is to handle the often low number of samples in the experiments compared to the number of biomolecules that are simultaneously measured. Combining experimental data using independent technologies to illuminate the same biological trends, as well as complementing each other in a larger perspective, is one natural way to overcome this challenge. In this work we investigated if integrating proteomics and transcriptomics data from a brain cancer animal model using gene set based analysis methodology, could enhance the biological interpretation of the data relative to more traditional analysis of the two datasets individually. The brain cancer model used is based on serial passaging of transplanted human brain tumor material (glioblastoma--GBM) through several generations in rats. These serial transplantations lead over time to genotypic and phenotypic changes in the tumors and represent a medically relevant model with a rare access to samples and where consequent analyses of individual datasets have revealed relatively few significant findings on their own. We found that the integrated analysis both performed better in terms of significance measure of its findings compared to individual analyses, as well as providing independent verification of the individual results. Thus a better context for overall biological interpretation of the data can be achieved.