Cysteamine Suppresses Cancer Cell Invasion and Migration in Glioblastoma through Inhibition of Matrix Metalloproteinase Activity.

Glioblastoma (GBM) cells are highly invasive, infiltrating the surrounding normal brain tissue, thereby limiting the efficacy of surgical resection and focal radiotherapy. Cysteamine, a small aminothiol molecule that is orally bioavailable and approved for cystinosis, has potential as a cancer treatment by inhibiting tumor cell invasion and metastasis. Here we demonstrate that these potential therapeutic effects of cysteamine are likely due to the inhibition of matrix metalloproteinases (MMPs) in GBM. In vitro assays confirmed that micromolar concentrations of cysteamine were not cytotoxic, enabling the interrogation of the cellular effects without confounding tumor cell loss. Cysteamine's inhibition of MMP activity, especially the targeting of MMP2, MMP9, and MMP14, was observed at micromolar concentrations, suggesting the mechanism of action in suppressing invasion and cell migration is by inhibition of these MMPs. These findings suggest that achievable micromolar concentrations of cysteamine effectively inhibit cancer cell invasion and migration in GBM, supporting the potential for use as an adjunct cancer treatment.

Raman-based machine learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. METHODS: Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. RESULTS: Here we develop APOLLO (rAman-based PathOLogy of maLignant glioma) - a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wildtype (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types. CONCLUSIONS: Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

TGF-ß and BMP signaling are associated with the transformation of glioblastoma to gliosarcoma and then osteosarcoma.

Background: Gliosarcoma, an isocitrate dehydrogenase wildtype (IDH-WT) variant of glioblastoma, is defined by clonal biphasic differentiation into gliomatous and sarcomatous components. While the transformation from a glioblastoma to gliosarcoma is uncommon, the subsequent transformation to osteosarcoma is rare but may provide additional insights into the biology of these typically distinct cancers. We observed a patient initially diagnosed with glioblastoma, that differentiated into gliosarcoma at recurrence, and further evolved to osteosarcoma at the second relapse. Our objective was to characterize the molecular mechanisms of tumor progression associated with this phenotypic transformation. Methods: Tumor samples were collected at all 3 stages of disease and RNA sequencing was performed to capture their transcriptomic profiles. Sequential clonal evolution was confirmed by the maintenance of an identical PTEN mutation throughout the tumor differentiation using the TSO500 gene panel. Publicly available datasets and the Nanostring nCounter technology were used to validate the results. Results: The glioblastoma tumor from this patient possessed mixed features of all 3 TCGA-defined transcriptomic subtypes of an IDH-WT glioblastoma and a proportion of osteosarcoma signatures were upregulated in the original tumor. Analysis showed that enhanced transforming growth factor-ß (TGF-ß) and bone morphogenic protein signaling was associated with tumor transformation. Regulatory network analysis revealed that TGF-ß family signaling committed the lineage tumor to osteogenesis by stimulating the expression of runt-related transcription factor 2 (RUNX2), a master regulator of bone formation. Conclusions: This unusual clinical case provided an opportunity to explore the modulators of longitudinal sarcomatous transformation, potentially uncovering markers indicating predisposition to this change and identification of novel therapeutic targets.

Clinical outcome assessment trends in clinical trials-Contrasting oncology and non-oncology trials.

BACKGROUND: Clinical outcome assessments (COAs) are key to patient-centered evaluation of novel interventions and supportive care. COAs are particularly informative in oncology where a focus on how patients feel and function is paramount, but their incorporation in trial outcomes have lagged that of traditional survival and tumor responses. To understand the trends of COA use in oncology and the impact of landmark efforts to promote COA use, we computationally surveyed oncology clinical trials in ClinicalTrials.gov comparing them to the rest of the clinical research landscape. METHODS: Oncology trials were identified using medical subject heading neoplasm terms. Trials were searched for COA instrument names obtained from PROQOLID. Regression analyses assessed chronological and design-related trends. RESULTS: Eighteen percent of oncology interventional trials initiated 1985-2020 (N = 35,415) reported using one or more of 655 COA instruments. Eighty-four percent of the COA-using trials utilized patient-reported outcomes, with other COA categories used in 4-27% of these trials. Likelihood of COA use increased with progressing trial phase (OR = 1.30, p < 0.001), randomization (OR = 2.32, p < 0.001), use of data monitoring committees (OR = 1.26, p < 0.001), study of non-FDA-regulated interventions (OR = 1.23, p = 0.001), and in supportive care versus treatment-focused trials (OR = 2.94, p < 0.001). Twenty-six percent of non-oncology trials initiated 1985-2020 (N = 244,440) reported COA use; they had similar COA-use predictive factors as oncology trials. COA use increased linearly over time (R = 0.98, p < 0.001), with significant increases following several individual regulatory events. CONCLUSION: While COA use across clinical research has increased over time, there remains a need to further promote COA use particularly in early phase and treatment-focused oncology trials.

A critical analysis of neuro-oncology clinical trials.

BACKGROUND: Limitations in trial design, accrual, and data reporting impact efficient and reliable drug evaluation in cancer clinical trials. These concerns have been recognized in neuro-oncology but have not been comprehensively evaluated. We conducted a semi-automated survey of adult interventional neuro-oncology trials, examining design, interventions, outcomes, and data availability trends. METHODS: Trials were selected programmatically from ClinicalTrials.gov using primary malignant central nervous system tumor classification terms. Regression analyses assessed design and accrual trends; effect size analysis utilized survival rates among trials investigating survival. RESULTS: Of 3038 reviewed trials, most trials reporting relevant information were nonblinded (92%), single group (65%), nonrandomized (51%), and studied glioblastomas (47%) or other gliomas. Basic design elements were reported by most trials, with reporting increasing over time (OR = 1.24, P < .00001). Trials assessing survival outcomes were estimated to assume large effect sizes of interventions when powering their designs. Forty-two percent of trials were completed; of these, 38% failed to meet their enrollment target, with worse accrual over time (R = -0.94, P < .00001) and for US versus non-US based trials (OR = 0.5, P < .00001). Twenty-eight percent of completed trials reported partial results, with greater reporting for US (34.6%) versus non-US based trials (9.3%, P < .00001). Efficacy signals were detected by 15%-23% of completed trials reporting survival outcomes. CONCLUSION: Low randomization rates, underutilization of controls, and overestimation of effect size, particularly pronounced in early-phase trials, impede generalizability of results. Suboptimal designs may be driven by accrual challenges, underscoring the need for cooperative efforts and novel designs. The limited results reporting highlights the need to incentivize data reporting and harmonization.

Discovery of clinical and demographic determinants of symptom burden in primary brain tumor patients using network analysis and unsupervised clustering.

Background: Precision health approaches to managing symptom burden in primary brain tumor (PBT) patients are imperative to improving patient outcomes and quality of life, but require tackling the complexity and heterogeneity of the symptom experience. Network Analysis (NA) can identify complex symptom co-severity patterns, and unsupervised clustering can unbiasedly stratify patients into clinically relevant subgroups based on symptom patterns. We combined these approaches in a novel study seeking to understand PBT patients' clinical and demographic determinants of symptom burden. Methods: MDASI-BT symptom severity data from a two-institutional cohort of 1128 PBT patients were analyzed. Gaussian Graphical Model networks were constructed for the all-patient cohort and subgroups identified by unsupervised clustering based on co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Results: NA of the all-patient cohort revealed 4 core dimensions that drive the overall symptom burden of PBT patients: Cognitive, physical, focal neurologic, and affective. Fatigue/drowsiness was identified as pivotal to the symptom experience based on the network characteristics. Unsupervised clustering discovered 4 patient subgroups: PC1 (n = 683), PC2 (n = 244), PC3 (n = 92), and PC4 (n = 109). Moderately accurate networks could be constructed for PC1 and PC2. The PC1 patients had the highest interference scores among the subgroups and their network resembled the all-patient network. The PC2 patients were older and their symptom burden was driven by cognitive symptoms. Conclusions: In the future, the proposed framework might be able to prioritize symptoms for targeting individual patients, informing more personalized symptom management.

Probabilistic model checking of cancer metabolism.

Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the effect of IDH1/2-mutations in cancer using a combination of experimental metabolic flux data and patient-derived gene expression data. Our models demonstrate the potential of computational exploration to reveal biologic behavior: they show that an exogenously-mutated IDH1 experimental model utilizes glutamine as an alternative carbon source for lactate production under hypoxia, but does not fully-recapitulate the patient phenotype under normoxia. We also demonstrate the utility of using gene expression data as a proxy for relative differences in metabolic activity. We use the approach of probabilistic model checking and the freely-available Probabilistic Symbolic Model Checker to construct and reason about model behavior.

Histological analysis of sleep and circadian brain circuitry in cranial radiation-induced hypersomnolence (C-RIH) mouse model.

Disrupted sleep, including daytime hypersomnolence, is a core symptom reported by primary brain tumor patients and often manifests after radiotherapy. The biological mechanisms driving the onset of sleep disturbances after cranial radiation remains unclear but may result from treatment-induced injury to neural circuits controlling sleep behavior, both circadian and homeostatic. Here, we develop a mouse model of cranial radiation-induced hypersomnolence which recapitulates the human experience. Additionally, we used the model to explore the impact of radiation on the brain. We demonstrated that the DNA damage response following radiation varies across the brain, with homeostatic sleep and cognitive regions expressing higher levels of γH2AX, a marker of DNA damage, than the circadian suprachiasmatic nucleus (SCN). These findings were supported by in vitro studies comparing radiation effects in SCN and cortical astrocytes. Moreover, in our mouse model, MRI identified structural effects in cognitive and homeostatic sleep regions two-months post-treatment. While the findings are preliminary, they suggest that homeostatic sleep and cognitive circuits are vulnerable to radiation and these findings may be relevant to optimizing treatment plans for patients.

Living with a central nervous system (CNS) tumor: findings on long-term survivorship from the NIH Natural History Study.

BACKGROUND: Primary central nervous system (CNS) tumors are often associated with high symptom burden and a poor prognosis from the time of diagnosis. The purpose of this study is to describe patient-reported outcomes (PRO) data from long-term survivors (LTS; ≥5-year survival post-diagnosis). METHODS: Clinical/treatment/molecular characteristics and PROs (symptom burden/interference (MDASI-BT/SP), perceived cognition (Neuro-QoL), anxiety/depression (PROMIS), and general health status (EQ-5D-3L)) were collected on 248 adult LTS between 9/2016 and 8/2019. Descriptive statistics and regression analysis were used to report results. RESULTS: Participants had a median age of 47 years (19-82) and were primarily White (83%) males (51%) with high-grade tumors (59%) and few mutations. Forty-two percent of the 222 brain tumor LTS reported no moderate-to-severe symptoms, whereas 45% reported three or more; most common symptoms were fatigue (40%), difficulty remembering (29%), and drowsiness (28%). Among spine tumor LTS (n = 42), nearly half reported moderate-to-severe weakness, pain, fatigue, and numbness/tingling, with 72% experiencing activity-related interference. Severe anxiety, depression, and cognitive symptoms were reported in up to 23% of the sample. Brain tumor LTS at higher risk for severe symptoms were more likely to be young, unemployed, and have poor KPS (Karnofsky Performance Status), whereas high symptom-risk spinal cord tumor LTS had poor KPS and received any tumor treatment. CONCLUSIONS: Findings indicate LTS fall into distinct cohorts with no significant symptoms or very high symptom burden, regardless of tumor grade or mutational profile. These LTS data demonstrate the need for survivorship care programs and future studies to explore the symptom trajectory of all CNS tumor patients for prevention and early interventions.

Association of Circadian Clock Gene Expression with Glioma Tumor Microenvironment and Patient Survival.

Circadian clock genes have been linked to clinical outcomes in cancer, including gliomas. However, these studies have not accounted for established markers that predict the prognosis, including mutations in Isocitrate Dehydrogenase (IDH), which characterize the majority of lower-grade gliomas and secondary high-grade gliomas. To demonstrate the connection between circadian clock genes and glioma outcomes while accounting for the IDH mutational status, we analyzed multiple publicly available gene expression datasets. The unsupervised clustering of 13 clock gene transcriptomic signatures from The Cancer Genome Atlas showed distinct molecular subtypes representing different disease states and showed the differential prognosis of these groups by a Kaplan-Meier analysis. Further analyses of these groups showed that a low period (PER) gene expression was associated with the negative prognosis and enrichment of the immune signaling pathways. These findings prompted the exploration of the relationship between the microenvironment and clock genes in additional datasets. Circadian clock gene expression was found to be differentially expressed across the anatomical tumor location and cell type. Thus, the circadian clock expression is a potential predictive biomarker in glioma, and further mechanistic studies to elucidate the connections between the circadian clock and microenvironment are warranted.

A Single-Organelle Optical Omics Platform for Cell Science and Biomarker Discovery.

Research in fundamental cell biology and pathology could be revolutionized by developing the capacity for quantitative molecular analysis of subcellular structures. To that end, we introduce the Ramanomics platform, based on confocal Raman microspectrometry coupled to a biomolecular component analysis algorithm, which together enable us to molecularly profile single organelles in a live-cell environment. This emerging omics approach categorizes the entire molecular makeup of a sample into about a dozen of general classes and subclasses of biomolecules and quantifies their amounts in submicrometer volumes. A major contribution of our study is an attempt to bridge Raman spectrometry with big-data analysis in order to identify complex patterns of biomolecules in a single cellular organelle and leverage discovery of disease biomarkers. Our data reveal significant variations in organellar composition between different cell lines. We also demonstrate the merits of Ramanomics for identifying diseased cells by using prostate cancer as an example. We report large-scale molecular transformations in the mitochondria, Golgi apparatus, and endoplasmic reticulum that accompany the development of prostate cancer. Based on these findings, we propose that Ramanomics datasets in distinct organelles constitute signatures of cellular metabolism in healthy and diseased states.

Molecularly Targeted Clinical Trials.

Glioblastoma remains incurable despite advances in surgery, radiation, and chemotherapy, underscoring the need for new therapies. The genetic heterogenicity, presence of redundant molecular pathways, and the blood-brain barrier have limited the applicability of molecularly targeted agents. The therapeutic benefit seen with a small subset of patients suggests, however, that patient selection is critical. Recent investigations show that molecularly targeted synthetic lethality is a promising complementary approach. The article provides an overview of the challenges of molecularly targeted therapy in adults with glioblastoma, including current trials and future therapeutic directions.

IDH1 mutations induce organelle defects via dysregulated phospholipids.

Infiltrating gliomas are devastating and incurable tumors. Amongst all gliomas, those harboring a mutation in isocitrate dehydrogenase 1 mutation (IDH1mut) acquire a different tumor biology and clinical manifestation from those that are IDH1WT. Understanding the unique metabolic profile reprogrammed by IDH1 mutation has the potential to identify new molecular targets for glioma therapy. Herein, we uncover increased monounsaturated fatty acids (MUFA) and their phospholipids in endoplasmic reticulum (ER), generated by IDH1 mutation, that are responsible for Golgi and ER dilation. We demonstrate a direct link between the IDH1 mutation and this organelle morphology via D-2HG-induced stearyl-CoA desaturase (SCD) overexpression, the rate-limiting enzyme in MUFA biosynthesis. Inhibition of IDH1 mutation or SCD silencing restores ER and Golgi morphology, while D-2HG and oleic acid induces morphological defects in these organelles. Moreover, addition of oleic acid, which tilts the balance towards elevated levels of MUFA, produces IDH1mut-specific cellular apoptosis. Collectively, these results suggest that IDH1mut-induced SCD overexpression can rearrange the distribution of lipids in the organelles of glioma cells, providing new insight into the link between lipid metabolism and organelle morphology in these cells, with potential and unique therapeutic implications.

Metabolic plasticity of IDH1-mutant glioma cell lines is responsible for low sensitivity to glutaminase inhibition.

BACKGROUND: Targeting glutamine metabolism in cancer has become an increasingly vibrant area of research. Mutant IDH1 (IDH1 mut ) gliomas are considered good candidates for targeting this pathway because of the contribution of glutamine to their newly acquired function: synthesis of 2-hydroxyglutarate (2HG). METHODS: We have employed a combination of 13C tracers including glutamine and glucose for investigating the metabolism of patient-derived IDH1 mut glioma cell lines through NMR and LC/MS. Additionally, genetic loss-of-function (in vitro and in vivo) approaches were performed to unravel the adaptability of these cell lines to the inhibition of glutaminase activity. RESULTS: We report the adaptability of IDH1 mut cells' metabolism to the inhibition of glutamine/glutamate pathway. The glutaminase inhibitor CB839 generated a decrease in the production of the downstream metabolites of glutamate, including those involved in the TCA cycle and 2HG. However, this effect on metabolism was not extended to viability; rather, our patient-derived IDH1 mut cell lines display a metabolic plasticity that allows them to overcome glutaminase inhibition. CONCLUSIONS: Major metabolic adaptations involved pathways that can generate glutamate by using alternative substrates from glutamine, such as alanine or aspartate. Indeed, asparagine synthetase was upregulated both in vivo and in vitro revealing a new potential therapeutic target for a combinatory approach with CB839 against IDH1 mut gliomas.

Sphingolipid Pathway as a Source of Vulnerability in IDH1mut Glioma.

In addition to providing integrity to cellular structure, the various classes of lipids participate in a multitude of functions including secondary messengers, receptor stimulation, lymphocyte trafficking, inflammation, angiogenesis, cell migration, proliferation, necrosis and apoptosis, thus highlighting the importance of understanding their role in the tumor phenotype. In the context of IDH1mut glioma, investigations focused on metabolic alterations involving lipidomics' present potential to uncover novel vulnerabilities. Herein, a detailed lipidomic analysis of the sphingolipid metabolism was conducted in patient-derived IDH1mut glioma cell lines, as well as model systems, with the of identifying points of metabolic vulnerability. We probed the effect of decreasing D-2HG levels on the sphingolipid pathway, by treating these cell lines with an IDH1mut inhibitor, AGI5198. The results revealed that N,N-dimethylsphingosine (NDMS), sphingosine C17 and sphinganine C18 were significantly downregulated, while sphingosine-1-phosphate (S1P) was significantly upregulated in glioma cultures following suppression of IDH1mut activity. We exploited the pathway using a small-scale, rational drug screen and identified a combination that was lethal to IDHmut cells. Our work revealed that further addition of N,N-dimethylsphingosine in combination with sphingosine C17 triggered a dose-dependent biostatic and apoptotic response in a panel of IDH1mut glioma cell lines specifically, while it had little effect on the IDHWT cells probed here. To our knowledge, this is the first study that shows how altering the sphingolipid pathway in IDH1mut gliomas elucidates susceptibility that can arrest proliferation and initiate subsequent cellular death.

mTORC2/Rac1 Pathway Predisposes Cancer Aggressiveness in IDH1-Mutated Glioma.

Isocitrate dehydrogenase (IDH) mutations are common genetic abnormalities in lower grade gliomas. The neomorphic enzyme activity of IDH mutants leads to tumor formation through epigenetic alteration, dysfunction of dioxygenases, and metabolic reprogramming. However, it remains elusive as to how IDH mutants regulate the pathways associated with oncogenic transformation and aggressiveness. In the present study, by using unbiased transcriptomic profiling, we showed that IDH1 mutations result in substantial changes in the gene sets that govern cellular motility, chemotaxis, and invasion. Mechanistically, rapamycin-insensitive companion of mammalian target of rapamycin (Rictor)/Ras-related C3 botulinum toxin substrate 1 (Rac1) signaling plays an essential role in the motility and proliferation of IDH1-mutated cells by prompting cytoskeleton reorganization, lamellipodia formation, and enhanced endocytosis. Targeting the Rictor/Rac1 pathway suppresses IDH1-mutated cells by limiting endocytosis and cell proliferation. Overall, our findings indicate a novel metabolic reprogramming mechanism of IDH1-mutated cells by exploiting metabolites from the extracellular milieu. Targeting the Rictor/Rac1 pathway could be an alternative therapeutic strategy for IDH1-mutated malignancies.

Metabolic reprogramming associated with aggressiveness occurs in the G-CIMP-high molecular subtypes of IDH1mut lower grade gliomas.

BACKGROUND: Early detection of increased aggressiveness of brain tumors is a major challenge in the field of neuro-oncology because of the inability of traditional imaging to uncover it. Isocitrate dehydrogenase (IDH)-mutant gliomas represent an ideal model system to study the molecular mechanisms associated with tumorigenicity because they appear indolent and non-glycolytic initially, but eventually a subset progresses toward secondary glioblastoma with a Warburg-like phenotype. The mechanisms and molecular features associated with this transformation are poorly understood. METHODS: We employed model systems for IDH1 mutant (IDH1mut) gliomas with different growth and proliferation rates in vivo and in vitro. We described the metabolome, transcriptome, and epigenome of these models in order to understand the link between their metabolism and the tumor biology. To verify whether this metabolic reprogramming occurs in the clinic, we analyzed data from The Cancer Genome Atlas. RESULTS: We reveal that the aggressive glioma models have lost DNA methylation in the promoters of glycolytic enzymes, especially lactate dehydrogenase A (LDHA), and have increased mRNA and metabolite levels compared with the indolent model. We find that the acquisition of the high glycolytic phenotype occurs at the glioma cytosine-phosphate-guanine island methylator phenotype (G-CIMP)-high molecular subtype in patients and is associated with the worst outcome. CONCLUSION: We propose very early monitoring of lactate levels as a biomarker of metabolic reprogramming and tumor aggressiveness.

Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

Glioblastoma (GBM) is an aggressive type of brain cancer with remarkable cell migration and adaptation capabilities. Exploratory adaptation-utilization of random changes in gene regulation for adaptive benefits-was recently proposed as the process enabling organisms to survive unforeseen conditions. We investigate whether exploratory adaption explains how GBM cells from different anatomic regions of the tumor cope with micro-environmental pressures. We introduce new notions of phenotype and phenotype distance, and determine probable spatial-phenotypic trajectories based on patient data. While some cell phenotypes are inherently plastic, others are intrinsically rigid with respect to phenotypic transitions. We demonstrate that stochastic exploration of the regulatory network structure confers benefits through enhanced adaptive capacity in new environments. Interestingly, even with exploratory capacity, phenotypic paths are constrained to pass through specific, spatial-phenotypic ranges. This work has important implications for understanding how such adaptation contributes to the recurrence dynamics of GBM and other solid tumors.

Mitochondrial NIX Promotes Tumor Survival in the Hypoxic Niche of Glioblastoma.

Cancer cells rely on mitochondrial functions to regulate key survival and death signals. How cancer cells regulate mitochondrial autophagy (mitophagy) in the tumor microenvironment as well as utilize mitophagy as a survival signal is still not well understood. Here, we elucidate a key survival mechanism of mitochondrial NIX-mediated mitophagy within the hypoxic region of glioblastoma, the most malignant brain tumor. NIX was overexpressed in the pseudopalisading cells that envelop the hypoxic-necrotic regions, and mitochondrial NIX expression was robust in patient-derived glioblastoma tumor tissues and glioblastoma stem cells. NIX was required for hypoxia and oxidative stress-induced mitophagy through NFE2L2/NRF2 transactivation. Silencing NIX impaired mitochondrial reactive oxygen species clearance, cancer stem cell maintenance, and HIF/mTOR/RHEB signaling pathways under hypoxia, resulting in suppression of glioblastoma survival in vitro and in vivo. Clinical significance of these findings was validated by the compelling association between NIX expression and poor outcome for patients with glioblastoma. Taken together, our findings indicate that the NIX-mediated mitophagic pathway may represent a key therapeutic target for solid tumors, including glioblastoma. SIGNIFICANCE: NIX-mediated mitophagy regulates tumor survival in the hypoxic niche of glioblastoma microenvironment, providing a potential therapeutic target for glioblastoma.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/20/5218/F1.large.jpg.