Unlocking ferroptosis in prostate cancer - the road to novel therapies and imaging markers.

Ferroptosis is a distinct form of regulated cell death that is predominantly driven by the build-up of intracellular iron and lipid peroxides. Ferroptosis suppression is widely accepted to contribute to the pathogenesis of several tumours including prostate cancer. Results from some studies reported that prostate cancer cells can be highly susceptible to ferroptosis inducers, providing potential for an interesting new avenue of therapeutic intervention for advanced prostate cancer. In this Perspective, we describe novel molecular underpinnings and metabolic drivers of ferroptosis, analyse the functions and mechanisms of ferroptosis in tumours, and highlight prostate cancer-specific susceptibilities to ferroptosis by connecting ferroptosis pathways to the distinctive metabolic reprogramming of prostate cancer cells. Leveraging these novel mechanistic insights could provide innovative therapeutic opportunities in which ferroptosis induction augments the efficacy of currently available prostate cancer treatment regimens, pending the elimination of major bottlenecks for the clinical translation of these treatment combinations, such as the development of clinical-grade inhibitors of the anti-ferroptotic enzymes as well as non-invasive biomarkers of ferroptosis. These biomarkers could be exploited for diagnostic imaging and treatment decision-making.

Post-Acquisition Hyperpolarized 29Silicon Magnetic Resonance Image Processing for Visualization of Colorectal Lesions Using a User-Friendly Graphical Interface.

Medical imaging devices often use automated processing that creates and displays a self-normalized image. When improperly executed, normalization can misrepresent information or result in an inaccurate analysis. In the case of diagnostic imaging, a false positive in the absence of disease, or a negative finding when disease is present, can produce a detrimental experience for the patient and diminish their health prospects and prognosis. In many clinical settings, a medical technical specialist is trained to operate an imaging device without sufficient background information or understanding of the fundamental theory and processes involved in image creation and signal processing. Here, we describe a user-friendly image processing algorithm that mitigates user bias and allows for true signal to be distinguished from background. For proof-of-principle, we used antibody-targeted molecular imaging of colorectal cancer (CRC) in a mouse model, expressing human MUC1 at tumor sites. Lesion detection was performed using targeted magnetic resonance imaging (MRI) of hyperpolarized silicon particles. Resulting images containing high background and artifacts were then subjected to individualized image post-processing and comparative analysis. Post-acquisition image processing allowed for co-registration of the targeted silicon signal with the anatomical proton magnetic resonance (MR) image. This new methodology allows users to calibrate a set of images, acquired with MRI, and reliably locate CRC tumors in the lower gastrointestinal tract of living mice. The method is expected to be generally useful for distinguishing true signal from background for other cancer types, improving the reliability of diagnostic MRI.

A mechanistic modeling framework reveals the key principles underlying tumor metabolism.

While aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, not only in their presentation of the Warburg effect but also in the nutrients and the metabolic pathways they are dependent on. Moreover, tumor cells can switch between different metabolic phenotypes in response to environmental cues and therapeutic interventions. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive heterogeneity with respect to the presentation of the Warburg effect. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. Next, we report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer.

Identifying the Metabolic Signatures of PPARD-Overexpressing Gastric Tumors.

Peroxisome proliferator-activated receptor delta (PPARD) is a nuclear receptor known to play an essential role in regulation of cell metabolism, cell proliferation, inflammation, and tumorigenesis in normal and cancer cells. Recently, we found that a newly generated villin-PPARD mouse model, in which PPARD is overexpressed in villin-positive gastric progenitor cells, demonstrated spontaneous development of large, invasive gastric tumors as the mice aged. However, the role of PPARD in regulation of downstream metabolism in normal gastric and tumor cells is elusive. The aim of the present study was to find PPARD-regulated downstream metabolic changes and to determine the potential significance of those changes to gastric tumorigenesis in mice. Hyperpolarized [1-13C] pyruvate magnetic resonance spectroscopy, nuclear magnetic resonance spectroscopy, and liquid chromatography-mass spectrometry were employed for metabolic profiling to determine the PPARD-regulated metabolite changes in PPARD mice at different ages during the development of gastric cancer, and the changes were compared to corresponding wild-type mice. Nuclear magnetic resonance spectroscopy-based metabolomic screening results showed higher levels of inosine monophosphate (p = 0.0054), uracil (p = 0.0205), phenylalanine (p = 0.017), glycine (p = 0.014), and isocitrate (p = 0.029) and lower levels of inosine (p = 0.0188) in 55-week-old PPARD mice than in 55-week-old wild-type mice. As the PPARD mice aged from 10 weeks to 35 weeks and 55 weeks, we observed significant changes in levels of the metabolites inosine monophosphate (p = 0.0054), adenosine monophosphate (p = 0.009), UDP-glucose (p = 0.0006), and oxypurinol (p = 0.039). Hyperpolarized [1-13C] pyruvate magnetic resonance spectroscopy performed to measure lactate flux in live 10-week-old PPARD mice with no gastric tumors and 35-week-old PPARD mice with gastric tumors did not reveal a significant difference in the ratio of lactate to total pyruvate plus lactate, indicating that this PPARD-induced spontaneous gastric tumor development does not require glycolysis as the main source of fuel for tumorigenesis. Liquid chromatography-mass spectrometry-based measurement of fatty acid levels showed lower linoleic acid, palmitic acid, oleic acid, and steric acid levels in 55-week-old PPARD mice than in 10-week-old PPARD mice, supporting fatty acid oxidation as a bioenergy source for PPARD-expressing gastric tumors.

Metabolic Imaging Using Hyperpolarization for Assessment of Premalignancy.

There is an unmet need for noninvasive surrogate markers that can help identify premalignant lesions across different tumor types. Here we describe the methodology and technical details of protocols employed for in vivo 13C pyruvate metabolic imaging experiments. The goal of the method described is to identify and understand metabolic changes, to enable detection of pancreatic premalignant lesions, as a proof of concept of the high sensitivity of this imaging modality.

Measuring the Metabolic Evolution of Glioblastoma throughout Tumor Development, Regression, and Recurrence with Hyperpolarized Magnetic Resonance.

Rapid diagnosis and therapeutic monitoring of aggressive diseases such as glioblastoma can improve patient survival by providing physicians the time to optimally deliver treatment. This research tested whether metabolic imaging with hyperpolarized MRI could detect changes in tumor progression faster than conventional anatomic MRI in patient-derived glioblastoma murine models. To capture the dynamic nature of cancer metabolism, hyperpolarized MRI, NMR spectroscopy, and immunohistochemistry were performed at several time-points during tumor development, regression, and recurrence. Hyperpolarized MRI detected significant changes of metabolism throughout tumor progression whereas conventional MRI was less sensitive. This was accompanied by aberrations in amino acid and phospholipid lipid metabolism and MCT1 expression. Hyperpolarized MRI can help address clinical challenges such as identifying malignant disease prior to aggressive growth, differentiating pseudoprogression from true progression, and predicting relapse. The individual evolution of these metabolic assays as well as their correlations with one another provides context for further academic research.

Beyond Colonoscopy: Exploring New Cell Surface Biomarkers for Detection of Early, Heterogenous Colorectal Lesions.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths among both men and women in the United States. Early detection and surgical removal of high-risk lesions in the colon can prevent disease from developing and spreading. Despite implementation of programs aimed at early detection, screening colonoscopies fail to detect a fraction of potentially aggressive colorectal lesions because of their location or nonobvious morphology. Optical colonoscopies, while highly effective, rely on direct visualization to detect changes on the surface mucosa that are consistent with dysplasia. Recent advances in endoscopy techniques and molecular imaging permit microscale visualization of the colonic mucosa. These technologies can be combined with various molecular probes that recognize and target heterogenous lesion surfaces to achieve early, real-time, and potentially non-invasive, detection of pre-cancerous lesions. The primary goal of this review is to contextualize existing and emergent CRC surface biomarkers and assess each's potential as a candidate marker for early marker-based detection of CRC lesions. CRC markers that we include were stratified by the level of support gleaned from peer-reviewed publications, abstracts, and databases of both CRC and other cancers. The selected biomarkers, accessible on the cell surface and preferably on the luminal surface of the colon tissue, are organized into three categories: (1) established biomarkers (those with considerable data and high confidence), (2) emerging biomarkers (those with increasing research interest but with less supporting data), and (3) novel candidates (those with very recent data, and/or supportive evidence from other tissue systems). We also present an overview of recent advances in imaging techniques useful for visual detection of surface biomarkers, and discuss the ease with which these methods can be combined with microscopic visualization.

Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.

BACKGROUND: There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). OBJECTIVE: Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. METHODS: A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. RESULTS: Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR-related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. CONCLUSIONS: Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

Hyperpolarized MRI with silicon micro and nanoparticles: Principles and applications.

Silicon-based micro and nanoparticles are ideally suited for use as biomedical imaging agents because of their biocompatibility, biodegradability, and simple surface chemistry that facilitates drug loading and targeting. A method to hyperpolarize silicon particles using dynamic nuclear polarization (DNP), which increases magnetic resonance (MR) imaging signals by several orders-of-magnitude through enhanced nuclear spin alignment, was developed to allow silicon particles to function as contrast agents for in vivo magnetic resonance imaging. In this review, we describe the application of the DNP technique to silicon particles and nanoparticles for background-free real-time molecular MR imaging. This review provides a summary of the state-of-the-science in silicon particle hyperpolarization with a detailed protocol for hyperpolarizing silicon particles. This information will foster awareness and spur interest in this emerging area of nanoimaging and provide a path to new developments and discoveries to further advance the field. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.

NMR Spectroscopy-Based Metabolomics of Platelets to Analyze Brain Tumors.

"Tumor-educated platelets" have recently generated substantial interest for the diagnosis of cancer. We hypothesized that tumor educated platelets from patients with brain tumors will reflect altered metabolism compared to platelets from healthy volunteers. Here, in a pilot study, we have employed nuclear magnetic resonance (NMR) spectroscopy in platelets from brain tumor patients to demonstrate altered metabolism compared to the platelets obtained from healthy volunteers.

Melanoma Evolves Complete Immunotherapy Resistance through the Acquisition of a Hypermetabolic Phenotype.

Despite the clinical success of T-cell checkpoint blockade, most patients with cancer still fail to have durable responses to immunotherapy. The molecular mechanisms driving checkpoint blockade resistance, whether preexisting or evolved, remain unclear. To address this critical knowledge gap, we treated B16 melanoma with the combination of CTLA-4, PD-1, and PD-L1 blockade and a Flt3 ligand vaccine (≥75% curative), isolated tumors resistant to therapy, and serially passaged them in vivo with the same treatment regimen until they developed complete resistance. Using gene expression analysis and immunogenomics, we determined the adaptations associated with this resistance phenotype. Checkpoint resistance coincided with acquisition of a "hypermetabolic" phenotype characterized by coordinated upregulation of the glycolytic, oxidoreductase, and mitochondrial oxidative phosphorylation pathways. These resistant tumors flourished under hypoxic conditions, whereas metabolically starved T cells lost glycolytic potential, effector function, and the ability to expand in response to immunotherapy. Furthermore, we found that checkpoint-resistant versus -sensitive tumors could be separated by noninvasive MRI imaging based solely on their metabolic state. In a cohort of patients with melanoma resistant to both CTLA-4 and PD-1 blockade, we observed upregulation of pathways indicative of a similar hypermetabolic state. Together, these data indicated that melanoma can evade T-cell checkpoint blockade immunotherapy by adapting a hypermetabolic phenotype.

Early Detection of Pancreatic Intraepithelial Neoplasias (PanINs) in Transgenic Mouse Model by Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy.

While pancreatic cancer (PC) survival rates have recently shown modest improvement, the disease remains largely incurable. Early detection of pancreatic cancer may result in improved outcomes and therefore, methods for early detection of cancer, even premalignant lesions, may provide more favorable outcomes. Pancreatic intraepithelial neoplasias (PanINs) have been identified as premalignant precursor lesions to pancreatic cancer. However, conventional imaging methods used for screening high-risk populations do not have the sensitivity to detect PanINs. Here, we have employed hyperpolarized metabolic imaging in vivo and nuclear magnetic resonance (1H-NMR) metabolomics ex vivo to identify and understand metabolic changes, towards enabling detection of early PanINs and progression to advanced PanINs lesions that precede pancreatic cancer formation. Progression of disease from tissue containing predominantly low-grade PanINs to tissue with high-grade PanINs showed a decreasing alanine/lactate ratio from high-resolution NMR metabolomics ex vivo. Hyperpolarized magnetic resonance spectroscopy (HP-MRS) allows over 10,000-fold sensitivity enhancement relative to conventional magnetic resonance. Real-time HP-MRS was employed to measure non-invasively changes of alanine and lactate metabolites with disease progression and in control mice in vivo, following injection of hyperpolarized [1-13C] pyruvate. The alanine-to-lactate signal intensity ratio was found to decrease as the disease progressed from low-grade PanINs to high-grade PanINs. The biochemical changes of alanine transaminase (ALT) and lactate dehydrogenase (LDH) enzyme activity were assessed. These results demonstrate that there are significant alterations of ALT and LDH activities during the transformation from early to advanced PanINs lesions. Furthermore, we demonstrate that real-time conversion kinetic rate constants (kPA and kPL) can be used as metabolic imaging biomarkers of pancreatic premalignant lesions. Findings from this emerging HP-MRS technique can be translated to the clinic for detection of pancreatic premalignant lesion in high-risk populations.

Assessing Metabolic Intervention with a Glutaminase Inhibitor in Real-Time by Hyperpolarized Magnetic Resonance in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is an aggressive hematopoietic disease characterized by glutamine-dependent metabolism. A novel glutaminase (GLS) inhibitor, CB-839, is currently under evaluation for treatment of hematopoietic malignancies and solid tumors. Our purpose was to measure cellular changes in AML associated with CB-839 treatment and to test the ability of hyperpolarized pyruvate for interrogating these changes to OCI-AML3 cells. Our results show that treatment with CB-839 interfered with the citric acid cycle, reduced the NADH/NAD+ ratio and ATP levels, reduced cell proliferation and viability, and reduced the basal and maximal respiratory capacities [oxygen consumption rate (OCR)]. We observed a reduction of the conversion of hyperpolarized pyruvate to lactate in cell lines and in a mouse AML model after CB-839 treatment. Our in vitro and in vivo results support the hypothesis that, in AML, glutamine is utilized to generate reducing equivalents (NADH, FADH2) through the citric acid cycle and that reduction in redox state by GLS inhibition decreases the rate of pyruvate to lactate conversion catalyzed by lactate dehydrogenase. We propose hyperpolarized pyruvate/lactate measurement as a method for direct monitoring of metabolic changes occurring in AML patients receiving CB-839. With further optimization, this method may provide a noninvasive imaging tool to assess the early efficacy of therapeutic intervention with GLS inhibitors.

Combining Hyperpolarized Real-Time Metabolic Imaging and NMR Spectroscopy To Identify Metabolic Biomarkers in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly cancer that progresses without any symptom, and oftentimes, it is detected at an advanced stage. The lack of prior symptoms and effective treatments have created a knowledge gap in the management of this lethal disease. This issue can be addressed by developing novel noninvasive imaging-based biomarkers in PDAC. We explored in vivo hyperpolarized (HP) 13C MRS of pyruvate to lactate conversion and ex vivo 1H NMR spectroscopy in a panel of well-annotated patient-derived PDAC xenograft (PDXs) model and investigated the correlation between aberrant glycolytic metabolism and aggressiveness of the tumor. Real-time metabolic imaging data demonstrate the immediate intracellular conversion of HP 13C pyruvate to lactate after intravenous injection interrogating upregulated lactate dehydrogenase (LDH) activity in aggressive PDXs. Total ex vivo lactate measurement by 1H NMR spectroscopy showed a direct correlation with in vivo dynamic pyruvate-to-lactate conversion and demonstrated the potential of dynamic metabolic flux as a biomarker of total lactate concentration and aggressiveness of the tumor. Furthermore, the metabolite concentrations were very distinct among all four tumor types analyzed in this study. Overexpression of LDH-A and hypoxia-inducible factor (HIF-1α) plays a significant role in the conversion kinetics of HP pyruvate-to-lactate in tumors. Collectively, these data identified aberrant metabolic characteristics of pancreatic cancer PDXs and could potentially delineate metabolic targets for therapeutic intervention. Metabolic imaging with HP pyruvate and NMR metabolomics may enable identification and classification of aggressive subtypes of patient-derived xenografts. Translation of this real-time metabolic technique to the clinic may have the potential to improve the management of patients at high risk of developing pancreatic diseases.

Assessing Therapeutic Efficacy in Real-time by Hyperpolarized Magnetic Resonance Metabolic Imaging.

Precisely measuring tumor-associated alterations in metabolism clinically will enable the efficient assessment of therapeutic responses. Advances in imaging technologies can exploit the differences in cancer-associated cell metabolism as compared to normal tissue metabolism, linking changes in target metabolism to therapeutic efficacy. Metabolic imaging by Positron Emission Tomography (PET) employing 2-fluoro-deoxy-glucose ([18F]FDG) has been used as a routine diagnostic tool in the clinic. Recently developed hyperpolarized Magnetic Resonance (HP-MR), which radically increases the sensitivity of conventional MRI, has created a renewed interest in functional and metabolic imaging. The successful translation of this technique to the clinic was achieved recently with measurements of 13C-pyruvate metabolism. Here, we review the potential clinical roles for metabolic imaging with hyperpolarized MRI as applied in assessing therapeutic intervention in different cancer systems.

Real-Time Interrogation of Aspirin Reactivity, Biochemistry, and Biodistribution by Hyperpolarized Magnetic Resonance Spectroscopy.

Hyperpolarized magnetic resonance spectroscopy enables quantitative, non-radioactive, real-time measurement of imaging probe biodistribution and metabolism in vivo. Here, we investigate and report on the development and characterization of hyperpolarized acetylsalicylic acid (aspirin) and its use as a nuclear magnetic resonance (NMR) probe. Aspirin derivatives were synthesized with single- and double-13 C labels and hyperpolarized by dynamic nuclear polarization with 4.7 % and 3 % polarization, respectively. The longitudinal relaxation constants (T1 ) for the labeled acetyl and carboxyl carbonyls were approximately 30 seconds, supporting in vivo imaging and spectroscopy applications. In vitro hydrolysis, transacetylation, and albumin binding of hyperpolarized aspirin were readily monitored in real time by 13 C-NMR spectroscopy. Hyperpolarized, double-labeled aspirin was well tolerated in mice and could be observed by both 13 C-MR imaging and 13 C-NMR spectroscopy in vivo.

Prostate Cancer Energetics and Biosynthesis.

Cancers must alter their metabolism to satisfy the increased demand for energy and to produce building blocks that are required to create a rapidly growing tumor. Further, for cancer cells to thrive, they must also adapt to an often changing tumor microenvironment, which can present new metabolic challenges (ex. hypoxia) that are unfavorable for most other cells. As such, altered metabolism is now considered an emerging hallmark of cancer. Like many other malignancies, the metabolism of prostate cancer is considerably different compared to matched benign tissue. However, prostate cancers exhibit distinct metabolic characteristics that set them apart from many other tumor types. In this chapter, we will describe the known alterations in prostate cancer metabolism that occur during initial tumorigenesis and throughout disease progression. In addition, we will highlight upstream regulators that control these metabolic changes. Finally, we will discuss how this new knowledge is being leveraged to improve patient care through the development of novel biomarkers and metabolically targeted therapies.

Metabolic Differences in Glutamine Utilization Lead to Metabolic Vulnerabilities in Prostate Cancer.

The new oncologic paradigm of precision medicine is focused on identifying metabolic, proteomic, transcriptomic and genomic variabilities in tumors that can be exploited to tailor treatments and improve patient outcomes. Metabolic changes are a hallmark of cancer, and inhibition of metabolic pathways is now a major strategy in medicinal chemistry for targeting cancers. However, non-invasive biomarkers to categorize metabolic subtypes are in short supply. The purpose of this study was to characterize the intracellular and extracellular metabolic profiles of four prostate cancer cell lines with varying degrees of aggressiveness. We observed metabolic differences between the aggressive prostate cancer cell line PC3 and the even more aggressive, metastatic subline PC3M assessed by hyperpolarized in vivo pyruvate studies, nuclear magnetic resonance spectroscopy, and carbon-13 feeding studies. On further examination of the differences between these two cell lines, we found increased glutamine utilization in the metastatic PC3M subline that led directly to sensitivity to glutaminase inhibitor CB-839. Our study supports the theory that metastatic progression increases glutamine utilization and the inhibition of glutaminolysis could have clinical implications.

Induction of autophagy by ARHI (DIRAS3) alters fundamental metabolic pathways in ovarian cancer models.

BACKGROUND: Autophagy is a bulk catabolic process that modulates tumorigenesis, therapeutic resistance, and dormancy. The tumor suppressor ARHI (DIRAS3) is a potent inducer of autophagy and its expression results in necroptotic cell death in vitro and tumor dormancy in vivo. ARHI is down-regulated or lost in over 60 % of primary ovarian tumors yet is dramatically up-regulated in metastatic disease. The metabolic changes that occur during ARHI induction and their role in modulating death and dormancy are unknown. METHODS: We employed Nuclear Magnetic Resonance (NMR)-based metabolomic strategies to characterize changes in key metabolic pathways in both cell culture and xenograft models of ARHI expression and autophagy. These pathways were further interrogated by cell-based immunofluorescence imaging, tracer uptake studies, targeted metabolic inhibition, and in vivo PET/CT imaging. RESULTS: Induction of ARHI in cell culture models resulted in an autophagy-dependent increase in lactate production along with increased glucose uptake and enhanced sensitivity to glycolytic inhibitors. Increased uptake of glutamine was also dependent on autophagy and dramatically sensitized cultured ARHI-expressing ovarian cancer cell lines to glutaminase inhibition. Induction of ARHI resulted in a reduction in mitochondrial respiration, decreased mitochondrial membrane potential, and decreased Tom20 staining suggesting an ARHI-dependent loss of mitochondrial function. ARHI induction in mouse xenograft models resulted in an increase in free amino acids, a transient increase in [18F]-FDG uptake, and significantly altered choline metabolism. CONCLUSIONS: ARHI expression has previously been shown to trigger autophagy-associated necroptosis in cell culture. In this study, we have demonstrated that ARHI expression results in decreased cellular ATP/ADP, increased oxidative stress, and decreased mitochondrial function. While this bioenergetic shock is consistent with programmed necrosis, our data indicates that the accompanying up-regulation of glycolysis and glutaminolysis is autophagy-dependent and serves to support cell viability rather than facilitate necroptotic cell death. While the mechanistic basis for metabolic up-regulation following ARHI induction is unknown, our preliminary data suggest that decreased mitochondrial function and increased metabolic demand may play a role. These alterations in fundamental metabolic pathways during autophagy-associated necroptosis may provide the basis for new therapeutic strategies for the treatment of dormant ovarian tumors.