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• W2897563882 abstract "Article15 October 2018free access Transparent process Infiltrative and drug-resistant slow-cycling cells support metabolic heterogeneity in glioblastoma Lan B Hoang-Minh Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Search for more papers by this author Florian A Siebzehnrubl European Cancer Stem Cell Research Institute, Cardiff University School of Biosciences, Cardiff, UK Search for more papers by this author Changlin Yang Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Silveli Suzuki-Hatano Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Kyle Dajac Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Tyler Loche Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Nicholas Andrews Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Michael Schmoll Massari Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Jaimin Patel Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Krisha Amin Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Alvin Vuong Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Ana Jimenez-Pascual European Cancer Stem Cell Research Institute, Cardiff University School of Biosciences, Cardiff, UK Search for more papers by this author Paul Kubilis Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Timothy J Garrett Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Craig Moneypenny Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA Search for more papers by this author Christina A Pacak Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Jianping Huang Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Elias J Sayour Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Duane A Mitchell Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Matthew R Sarkisian Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Search for more papers by this author Brent A Reynolds Corresponding Author [email protected] orcid.org/0000-0001-6273-7014 Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Loic P Deleyrolle Corresponding Author [email protected] orcid.org/0000-0002-1129-744X Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Lan B Hoang-Minh Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Search for more papers by this author Florian A Siebzehnrubl European Cancer Stem Cell Research Institute, Cardiff University School of Biosciences, Cardiff, UK Search for more papers by this author Changlin Yang Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Silveli Suzuki-Hatano Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Kyle Dajac Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Tyler Loche Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Nicholas Andrews Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Michael Schmoll Massari Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Jaimin Patel Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Krisha Amin Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Alvin Vuong Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Ana Jimenez-Pascual European Cancer Stem Cell Research Institute, Cardiff University School of Biosciences, Cardiff, UK Search for more papers by this author Paul Kubilis Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Timothy J Garrett Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Craig Moneypenny Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA Search for more papers by this author Christina A Pacak Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA Search for more papers by this author Jianping Huang Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Elias J Sayour Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Duane A Mitchell Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Matthew R Sarkisian Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Search for more papers by this author Brent A Reynolds Corresponding Author [email protected] orcid.org/0000-0001-6273-7014 Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Loic P Deleyrolle Corresponding Author [email protected] orcid.org/0000-0002-1129-744X Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA Search for more papers by this author Author Information Lan B Hoang-Minh1,2,‡, Florian A Siebzehnrubl3,‡, Changlin Yang2,4, Silveli Suzuki-Hatano5, Kyle Dajac4, Tyler Loche4, Nicholas Andrews4, Michael Schmoll Massari4, Jaimin Patel4, Krisha Amin4, Alvin Vuong4, Ana Jimenez-Pascual3, Paul Kubilis4, Timothy J Garrett6, Craig Moneypenny7, Christina A Pacak5, Jianping Huang2,4, Elias J Sayour2,4, Duane A Mitchell2,4, Matthew R Sarkisian1,2, Brent A Reynolds *,2,4 and Loic P Deleyrolle *,2,4 1Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA 2Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, FL, USA 3European Cancer Stem Cell Research Institute, Cardiff University School of Biosciences, Cardiff, UK 4Department of Neurosurgery, McKnight Brain Institute, University of Florida, Gainesville, FL, USA 5Department of Pediatrics, College of Medicine, University of Florida, Gainesville, FL, USA 6Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA 7Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +1 352 273 8476; E-mail: [email protected] *Corresponding author. Tel: +1 352 273 9000; E-mail: [email protected] EMBO J (2018)37:e98772https://doi.org/10.15252/embj.201798772 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Metabolic reprogramming has been described in rapidly growing tumors, which are thought to mostly contain fast-cycling cells (FCCs) that have impaired mitochondrial function and rely on aerobic glycolysis. Here, we characterize the metabolic landscape of glioblastoma (GBM) and explore metabolic specificities as targetable vulnerabilities. Our studies highlight the metabolic heterogeneity in GBM, in which FCCs harness aerobic glycolysis, and slow-cycling cells (SCCs) preferentially utilize mitochondrial oxidative phosphorylation for their functions. SCCs display enhanced invasion and chemoresistance, suggesting their important role in tumor recurrence. SCCs also demonstrate increased lipid contents that are specifically metabolized under glucose-deprived conditions. Fatty acid transport in SCCs is targetable by pharmacological inhibition or genomic deletion of FABP7, both of which sensitize SCCs to metabolic stress. Furthermore, FABP7 inhibition, whether alone or in combination with glycolysis inhibition, leads to overall increased survival. Our studies reveal the existence of GBM cell subpopulations with distinct metabolic requirements and suggest that FABP7 is central to lipid metabolism in SCCs and that targeting FABP7-related metabolic pathways is a viable therapeutic strategy. Synopsis Transcriptomic and metabolomic profiling of primary brain tumor cells demonstrate that functionally different glioblastoma (GBM) cell subpopulations depend on distinct metabolic pathways for their growth and survival. More invasive slow cycling tumor cells rely on oxidative phosphorylation and lipid metabolism, suggesting targetable candidates for the inhibition of treatment-resistant tumors. Patient-derived GBM cells contain fast-cycling cells (FCCs) relying on aerobic glycolysis and slow-cycling cells (SCCs) depending on mitochondrial oxidative phosphorylation in vivo. SCCs show increased resistance, invasion, and metabolic gene signatures characteristic of recurrent tumors. SCCs show increased levels of metabolites and components involved in lipid metabolism, storage, and transport. Block of FABP7-dependent exogenous fatty acid uptake decreases resistance of SCCs to chemotherapy and glucose deprivation. Introduction Intratumoral heterogeneity, which manifests in genetic, transcriptional, and functional levels, is increasingly recognized as a determinant of therapy resistance and disease recurrence. Indeed, tumor recurrence results from the ability of specific tumor subpopulations to resist treatment and expand. As has been shown for several malignancies, including glioblastoma (GBM), conventional cancer therapies most effectively eliminate rapidly dividing cells while sparing slower proliferating populations (Graham et al, 2002; Dembinski & Krauss, 2009; Gao et al, 2010; Pece et al, 2010; Roesch et al, 2010; Moore et al, 2012; Campos et al, 2014; Zeuner et al, 2014; Oshimori et al, 2015). GBM represents a prototypical example of heterogeneous cancer and is one of the most lethal malignancies, with a median survival of approximately 15–18 months despite multimodal therapy (Stupp et al, 2005, 2015). This dismal prognosis is attributable to therapy-resistant GBM cells that drive recurrence, and the identification and characterization of these cellular subpopulations and their dynamic properties are essential for the development of more effective treatments. A recent study demonstrated a proliferative hierarchy in human GBM, with slow-cycling, cancer stem-like cells via asymmetric division giving rise to rapidly proliferating progenies, which in turn generate limited-lived and non-proliferative offspring (Lan et al, 2017). A similar hierarchy has been proposed in a mouse model of glioma, with TMZ-resistant, slow-dividing cancer stem cells driving long-term tumor growth via the generation of a rapidly growing transient population of cells (Chen et al, 2012). These results were also confirmed in human GBM (Campos et al, 2014). Similarly, Vanner et al (2014) showed that quiescent, SOX2-positive cells drive long-term tumor propagation and relapse in a sonic hedgehog subgroup of medulloblastoma. Using single-cell RNA sequencing, Tirosh et al (2016b) reported a similar cellular hierarchy that is driven by developmental programs in oligodendroglioma. We have previously reported the existence, isolation, and functional characterization of fast-cycling cells (FCCs) and slow-cycling cells (SCCs) in GBM (Deleyrolle et al, 2011, 2012). We found that human GBM SCCs are consistently enriched in cancer stem cell markers in vitro. This SCC population is enriched in tumor-initiating cells, leading to enhanced tumorigenicity compared to the overall tumor population. SCCs were also identified and isolated in vivo and demonstrated all the key functional and phenotypic characteristics defining cancer stem cells, thus making them a clinically relevant target for new GBM treatment approaches (Deleyrolle et al, 2011). According to the Warburg hypothesis (Warburg, 1926), tumorigenesis is partly driven by an impairment of mitochondrial function and oxidative phosphorylation (OxPhos). These alterations result in the Warburg effect, which is characterized by cancer cells generating most of their energy from glucose fermentation, i.e., aerobic glycolysis, with a limited ability to perform nutrient oxidation (Koppenol et al, 2011). This metabolic reprogramming is thought to be an adaptation mechanism of rapidly growing tumor cells to cover their increasing energy demands. The intrinsic cellular heterogeneity of GBM raises the question as to whether the different cellular subpopulations (e.g., FCCs and SCCs) are restricted to glucose fermentation or other metabolic pathways for their survival and proliferation. Interestingly, recent studies have demonstrated residual activity of mitochondrial function in GBM cells (Marin-Valencia et al, 2012; Mashimo et al, 2014; Lin et al, 2017), suggesting that some of these cells might utilize mitochondrial OxPhos. However, the precise nature of the GBM cellular compartments harboring various metabolic specificities still needs to be established. Here, we demonstrate that functionally different GBM cell subpopulations depend on distinct metabolic pathways for their growth and survival. Importantly, GBM SCCs display unique phenotypic traits, chemoresistance, and metabolic profiles that are divergent from those of FCCs. SCCs engage metabolic pathways that overlap with those found in recurrent GBM. Our work uncovers a previously unidentified metabolic dichotomy in GBM, with FCCs depending on glucose metabolism and SCCs relying on oxidative phosphorylation and lipid metabolism for their growth and survival. We show that blocking the specific energy pathways utilized by GBM FCCs and SCCs inhibits overall tumor growth. Our studies also highlight the SCC subpopulation as a determinant for GBM's resistance to metabolic treatments targeting the Warburg effect and identify in this population new candidate therapeutic targets including FABP7. Results SCCs display migration, invasion, and chemoresistance characteristics that promote GBM recurrence GBM SCCs are associated with greater tumorigenicity and therefore enriched in stem-like cells (Deleyrolle et al, 2011). Using gene set enrichment analysis, we confirmed that SCCs overexpress a gene module defined as a stem cell signature (Fig EV1A; Wong et al, 2008). We have previously shown that GBM stem-like cells are more migratory and invasive (Siebzehnrubl et al, 2013). Therefore, we tested whether GBM cell proliferation rates might be inversely correlated with migration/invasion potentials (“go or grow hypothesis”) in vitro and in vivo. For this purpose, we isolated SCCs and FCCs from patient-derived primary GBM Line 0 (L0), Line 1 (L1), and Line 2 (L2) using a flow cytometry-based label retention paradigm (Deleyrolle et al, 2011; Fig EV1B). All downstream experiments were conducted immediately after purifying the SCC and FCC populations. Quantifying the migration abilities of these cellular subpopulations using in vitro scratch assays (Siebzehnrubl et al, 2013), we found that SCC migration distances were significantly higher than FCCs’ for all three cell lines (Fig 1A), despite the higher proliferation rates of FCCs (Deleyrolle et al, 2011). Click here to expand this figure. Figure EV1. Invasion and chemoresistance properties in SCCs A. GSEA of SCC (n = 3 biological replicates, L0-1-2) and FCC (n = 3 biological replicates, L0-1-2) RNA seq data sets for enrichment of the stem cell gene signature (Wong et al, 2008). FDR, false discovery rate; NES, normalized enrichment score; Nom., nominal. B. SCCs and FCCs were separated 6–8 days after (CFSE or CTV) CellTrace loading. Gates were set as 10% CellTracehi versus CellTracelo). C. Threshold images of human-specific nestin staining were used for quantification of tumor invasion (n = 5 animals per group, see Materials and Methods). D. Immunofluorescence imaging revealed notable differences between invasive/SCC-derived, and non-invasive/FCC-derived, tumors. Invasive SCC-derived tumors were positive for ZEB1 (green), while this marker was absent in the tumor masses derived from FCCs. Tumor cells were labeled with hNestin (red) and nuclei with Hoechst (blue). Scale bars, 10 μm. Arrowheads indicate infiltrative Zeb1+ GBM cells. E. Fluorescence imaging showed that the invasion of ZEB1 knockdown (shZEB1) SCC-derived tumors from orthotopic xenografts was greatly reduced compared to that of control SCC-derived tumors (shCo). Scale bars, 10 μm. F–J. Temozolomide-resistant cells (TMZR) and SCCs derived from the L1 patient-derived GBM line displayed similar growth rates (n = 3) (F), TMZ sensitivities (n = 6–8, *P < 0.05, ***P < 0.001, one-way ANOVA with Tukey post-test) (G), as well as migration (H) and invasion capabilities (scale bars, 500 μm) (I) (n = 3–12). These results were accompanied by the detection of high expression levels of ZEB1 in TMZR-derived tumors. The arrowhead indicates infiltrative Zeb1+ GBM cell (J). Values are mean ± SEM. Download figure Download PowerPoint Figure 1. Invasiveness and chemoresistance as hallmarks of SCCs in GBMSCCs and FCCs were identified and purified using the sorting paradigm described in Fig EV1A. Scratch assays demonstrated a greater migratory potential for SCCs than for FCCs within 24 h (mean ± SEM, n = 6, **P < 0.01, t-test). Following murine xenografts of L1 or L2 patient-derived cell lines, SCCs produced invasive tumors, while FCCs produced confined masses. Ten weeks after implantation, SCC-derived tumors, and total population-derived tumors for L1, exhibited greater invasion indices than FCC-derived tumors (mean ± SEM, n = 3–12, **P < 0.001, ***P < 0.001, one-way ANOVA with Tukey post-test). Scale bars, 500 μm. Representative fluorescence microscopy images of tumor sections derived from intracranial xenografts of lentivirally transduced green fluorescent protein (GFP)-labeled SCCs and red fluorescent protein (RFP)-expressing FCCs in a 1:1 ratio, 6 weeks after implantation (n = 5). SCCs (in green) generated a network of cells infiltrating the brain parenchyma while FCCs (red) remained contained, forming tight masses. Images are from two mice out of five. Scale bar, 10 μm. The invasion of tumors derived from orthotopic xenografts of ZEB1 knockdown SCCs was significantly lower than that of control SCC-derived tumors (mean ± SEM, n = 6, **P < 0.01, ***P < 0.001, t-test). SCCs were found to be significantly more resistant to TMZ than the total or FCC populations in vitro using MTT assays (mean ± SEM, n = 6–8, ***P < 0.001, one-way ANOVA with Tukey post-test). In vivo TMZ treatment yielded no survival benefit following SCC xenograft of the most TMZ-resistant GBM line, whereas TMZ treatment of animals xenografted with the non-SCC population resulted in significantly prolonged survival (mean ± SEM, n = 5, ##P < 0.01, t-test). TMZ treatment of animals xenografted with SCCs from a more TMZ-sensitive line increased overall survival, but to a lesser degree than for non-SCC-implanted animals (mean ± SEM, n = 5, ##P < 0.01, t-test). Download figure Download PowerPoint Importantly, 10 weeks after intracranial xenotransplantation in mice (which represents the approximate survival endpoint for FCC- and non-SCC-implanted animals), SCCs had generated more invasive tumors than FCCs, for two patient-derived GBM cell lines (Figs 1B and EV1C). In order to directly compare the invasion potentials of SCCs and FCCs in vivo, we lentivirally transduced SCCs with GFP and FCCs with RFP and intracranially implanted these together at a ratio of 1:1 into recipient mice. This labeling enabled us to assess whether the observed greater invasion of SCCs was due to slower tumor growth and thus smaller tumor size, whether FCC-derived tumors would appear more invasive when observed at earlier stages of tumor formation, and/or whether invasion of FCCs would be influenced by SCCs in their environment. We analyzed these tumors 6 weeks after implantation to differentiate tumor mass expansion from tumor invasion. In all animals, SCCs generated a network of invasive cells infiltrating the brain parenchyma and extending long processes that were consistent with tumor microtubes (Osswald et al, 2015), while FCCs generated more contained tumor masses (Fig 1C). These results show that invasion is intrinsic to SCCs, while FCCs generate non-infiltrating tumor masses. Epithelial-to-mesenchymal transition (EMT), and in particular the EMT transcription factor zinc-finger E-box-binding homeobox 1 (ZEB1), has been frequently associated with a loss of cell-to-cell contact and the distant spreading of tumors (Singh & Settleman, 2010; Siebzehnrubl et al, 2013). Moreover, ZEB1 promotes cancer cell stemness (Aigner et al, 2007; Shimono et al, 2009; Wellner et al, 2009; Chaffer et al, 2013), and the co-expression of SOX2, OLIG2, and ZEB1 transforms tumor-suppressor-deficient murine astrocytes into glioma-initiating cells in the absence of an upstream oncogene (Singh et al, 2017). Therefore, we hypothesized that ZEB1 may regulate SCC invasion and tested whether this transcription factor was differentially expressed in SCCs and FCCs in vivo. We found that FCC-derived, non-invasive tumors were devoid of ZEB1, while ZEB1-immunoreactive cells were consistently found throughout SCC-derived invasive tumors (Fig EV1D). To determine whether the higher ZEB1 levels in SCCs are linked to these cells’ greater capability for migration and invasion, we isolated SCC and FCC populations from control and ZEB1-knockdown cells. The invasion of tumor derived from ZEB1 knockdown SCC in orthotopic xenografts was greatly reduced compared to control SCC-derived tumors (Figs 1D and EV1E). These results demonstrate that ZEB1 is necessary for migration and invasion of SCCs. Because ZEB1 and other EMT regulators have been shown to induce chemoresistance in GBM (Qi et al, 2012; Siebzehnrubl et al, 2013; Depner et al, 2016), we next tested whether GBM SCCs, which are enriched in ZEB1, are more resistant to therapy than FCCs, as has been demonstrated for other quiescent subsets of GBM cells (Chen et al, 2012; Campos et al, 2014). We evaluated the in vitro effects of the standard-of-care chemotherapeutic drug temozolomide (TMZ) on the cell viabilities of the total tumor cell populations as well as FCCs and SCCs using MTT assays. While all three L0, L1, and L2 total cell populations displayed some sensitivity to TMZ, L0 was the most sensitive and L2 the most resistant line. Importantly, the SCCs from all three patient-derived GBM cell lines showed higher resistance to TMZ than the corresponding cell line's FCCs (Fig 1E). Moreover, by repeatedly exposing these primary GBM lines to TMZ, we selected for TMZ-resistant cell populations (TMZR) with expansion rates and TMZ resistance profiles similar to SCCs’ (Fig EV1F and G). TMZR and SCCs also showed comparable migration and invasion potentials (Fig EV1H–J). These results further underscore the link between GBM cell proliferation rate, invasiveness, and chemoresistance. We next tested whether SCCs were more chemoresistant than the rest of the GBM cell population in vivo. We treated tumor-bearing animals, orthotopically grafted with either SCCs or FCCs, with clinically relevant TMZ concentrations (20 mg/kg; Zhou et al, 2007). TMZ treatment prolonged the median survival of animals implanted with L2 FCCs but did not improve the survival of the L2 SCC-implanted group (Fig 1D). Similarly, TMZ treatment lengthened the survival of L0 FCC-grafted animals but had significantly smaller effects in L0 SCC-grafted animals (Fig 1E). These results further demonstrate that GBM SCCs are more resistant to TMZ than the rest of the tumor cell population and thus more likely to escape standard-of-care therapy (Fig EV1I). Together, our findings support critical roles of SCCs in both GBM invasion and chemoresistance and thus in tumor recurrence. Treatment-resistant/recurrent tumors share metabolic gene signatures with SCCs Based on the hypothesis that SCCs might contribute to tumor recurrence, and to identify the molecular mechanisms involved in their survival and growth, we investigated the molecular pathways that are characteristic of recurrent GBM tumors. We compared the RNA sequencing data of 153 primary and 14 recurrent GBM patient tumors from the TCGA database (Cancer Genome Atlas Research Network, 2008) and identified several genes that were significantly up-regulated in recurrent tumors compared to primary tumors (Fig 2A and Table EV1). Using the String database (Szklarczyk et al, 2015), we found that lipid metabolism was one of the top 5 most significantly enriched gene pathway groups in GBM recurrent tumors (GO:0033993, Fig EV2A and Table EV2). Interestingly, lipid metabolism constitutes the main source for mitochondrial energy production, and we found significantly higher mRNA expression levels of multiple genes involved in mitochondrial OxPhos, the tricarboxylic acid (TCA) cycle, and pyruvate and antioxidant metabolism in recurrent GBM (fold change > 2, Mann–Whitney test, P < 0.05; Fig EV2B and C, and Table EV3). Notably, GBM SCCs displayed these specific metabolic signatures, further supporting SCCs’ influential presence and role in tumor recurrence (Fig 2B). We also found that the mRNA expression levels of genes involved in the glycolytic/gluconeogenesis pathways were down-regulated in recurrent tumors (fold change > 2, Mann–Whitney test, P < 0.05; Table EV4). Together, these data support the presence of metabolic heterogeneity and plasticity in GBM. Figure 2. Shared metabolic gene signature between recurrent GBM and SCCs Volcano plot representation of 20,530 genes that were identified in primary and recurrent human GBMs using the TCGA database and showing differentially expressed genes between the two groups. Gray areas denote significant increases or decreases in gene expression. Recurrent tumors show a significant increase in the expression of genes involved in lipid metabolism, mitochondrial respiration, TCA cycle, as well as pyruvate and antioxidant metabolism (fold change > 2 and P < 0.05, Mann–Whitney U-test, Subio platform). These genes were then clustered into two signatures representing lipid metabolism and oxidative-reduction (Ox-Red) genes. GSEA" @default.
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• W2897563882 title "Infiltrative and drug‐resistant slow‐cycling cells support metabolic heterogeneity in glioblastoma" @default.
• W2897563882 cites W1516920003 @default.
• W2897563882 cites W1570407586 @default.
• W2897563882 cites W1591490606 @default.
• W2897563882 cites W1678795445 @default.
• W2897563882 cites W1928043468 @default.
• W2897563882 cites W1969115683 @default.
• W2897563882 cites W1974152703 @default.
• W2897563882 cites W1975967644 @default.
• W2897563882 cites W1977918192 @default.
• W2897563882 cites W1982002134 @default.
• W2897563882 cites W1984483719 @default.
• W2897563882 cites W1987239635 @default.
• W2897563882 cites W1992524428 @default.
• W2897563882 cites W1994318934 @default.
• W2897563882 cites W1995103178 @default.
• W2897563882 cites W1995495028 @default.
• W2897563882 cites W1997099893 @default.
• W2897563882 cites W1998166655 @default.
• W2897563882 cites W2001824102 @default.
• W2897563882 cites W2001916606 @default.
• W2897563882 cites W2005028383 @default.
• W2897563882 cites W2007505836 @default.
• W2897563882 cites W2008878539 @default.
• W2897563882 cites W2012034410 @default.
• W2897563882 cites W2018360235 @default.
• W2897563882 cites W2023300506 @default.
• W2897563882 cites W2025183726 @default.
• W2897563882 cites W2027694461 @default.
• W2897563882 cites W2028593825 @default.
• W2897563882 cites W2030017878 @default.
• W2897563882 cites W2031348772 @default.
• W2897563882 cites W2032580087 @default.
• W2897563882 cites W2034604831 @default.
• W2897563882 cites W2037026478 @default.
• W2897563882 cites W2041058430 @default.
• W2897563882 cites W2044433516 @default.
• W2897563882 cites W2047122678 @default.
• W2897563882 cites W2050093720 @default.
• W2897563882 cites W2057001750 @default.
• W2897563882 cites W2057639794 @default.
• W2897563882 cites W2060311418 @default.
• W2897563882 cites W2067187286 @default.
• W2897563882 cites W2072144231 @default.
• W2897563882 cites W2076118448 @default.
• W2897563882 cites W2078461037 @default.
• W2897563882 cites W2078510083 @default.
• W2897563882 cites W2080457126 @default.
• W2897563882 cites W2096173332 @default.
• W2897563882 cites W2096287682 @default.
• W2897563882 cites W2108687101 @default.
• W2897563882 cites W2108835820 @default.
• W2897563882 cites W2109420297 @default.
• W2897563882 cites W2110467543 @default.
• W2897563882 cites W2120246444 @default.
• W2897563882 cites W2123434257 @default.
• W2897563882 cites W2125395336 @default.
• W2897563882 cites W2128130529 @default.
• W2897563882 cites W2128983089 @default.
• W2897563882 cites W2129355962 @default.
• W2897563882 cites W2130264065 @default.
• W2897563882 cites W2140987848 @default.
• W2897563882 cites W2145388244 @default.
• W2897563882 cites W2147807172 @default.
• W2897563882 cites W2150163498 @default.
• W2897563882 cites W2151980465 @default.
• W2897563882 cites W2167241157 @default.
• W2897563882 cites W2168063614 @default.
• W2897563882 cites W2177430595 @default.
• W2897563882 cites W2185778972 @default.
• W2897563882 cites W2205491921 @default.
• W2897563882 cites W2258066491 @default.
• W2897563882 cites W2259044959 @default.
• W2897563882 cites W2265836483 @default.

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