39 research outputs found

    AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer

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    Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry–based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our findings show how prostate tumors undergo a metabolic reprogramming that reflects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.Fil: Priolo, Carmen. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Pyne, Saumyadipta. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Rose, Joshua. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Regan, ErzsĂ©bet Ravasz. Harvard Medical School; Estados UnidosFil: Zadra, Giorgia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Photopoulos, Cornelia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Cacciatore, Stefano. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados UnidosFil: Schultz, Denise. Johns Hopkins University; Estados UnidosFil: Scaglia, Natalia. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados Unidos. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas; ArgentinaFil: McDunn, Jonathan. Metabolon Inc.; Estados UnidosFil: de Marzo, Angelo M.. Johns Hopkins University; Estados UnidosFil: Loda, Massimo. Department of Pathology. Brigham and Women's Hospital; Estados Unidos. Department of Medical Oncology. Dana Farber Cancer Institute. Brigham and Women's Hospital; Estados Unidos. University of Cambridge; Estados Unidos. King's College London. Division of Cancer Studies; Estados Unido

    A novel direct activator of AMPK inhibits prostate cancer growth by blocking lipogenesis

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    5â€ČAMP-activated kinase (AMPK) constitutes a hub for cellular metabolic and growth control, thus representing an ideal therapeutic target for prostate cancers (PCas) characterized by increased lipogenesis and activation of mTORC1 pathway. However, whether AMPK activation itself is sufficient to block cancer cell growth remains to be determined. A small molecule screening was performed and identified MT 63–78, a specific and potent direct AMPK activator. Here, we show that direct activation of AMPK inhibits PCa cell growth in androgen sensitive and castration resistant PCa (CRPC) models, induces mitotic arrest, and apoptosis. In vivo, AMPK activation is sufficient to reduce PCa growth, whereas the allelic loss of its catalytic subunits fosters PCa development. Importantly, despite mTORC1 blockade, the suppression of de novo lipogenesis is the underpinning mechanism responsible for AMPK-mediated PCa growth inhibition, suggesting AMPK as a therapeutic target especially for lipogenesis-driven PCas. Finally, we demonstrate that MT 63–78 enhances the growth inhibitory effect of AR signaling inhibitors MDV3100 and abiraterone. This study thus provides a rationale for their combined use in CRPC treatment

    Plasma metabolomics and clinical predictors of survival differences in COPD patients

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    Background: Plasma metabolomics profile (PMP) in COPD has been associated with clinical characteristics, but PMP''s relationship to survival has not been reported. We determined PMP differences between patients with COPD who died an average of 2 years after enrollment (Non-survivors, NS) compared to those who survived (S) and also with age matched controls (C). Methods: We studied prospectively 90 patients with severe COPD and 30 controls. NS were divided in discovery and validation cohorts (30 patients each) and the results compared to the PMP of 30 S and C. All participants completed lung function tests, dyspnea scores, quality of life, exercise capacity, BODE index, and plasma metabolomics by liquid and gas chromatography/mass spectometry (LC/MS, LC/MS2, GC/MS). Statistically, we used Random Forest Analysis (RFA) and Support Vector Machine (SVM) to determine metabolites that differentiated the 3 groups and compared the ability of metabolites vs. clinical characteristics to classify patients into survivors and non-survivors. Results: There were 79 metabolites statistically different between S and NS [p < 0.05 and false discovery rate (q value) < 0.1]. RFA and SVM classification of COPD survivors and non-survivors had a predicted accuracy of 74 and 85% respectively. Elevation of tricyclic acid cycle intermediates branched amino acids depletion and increase in lactate, fructose and xylonate showed the most relevant differences between S vs. NS suggesting alteration in mitochondrial oxidative energy generation. PMP had similar predictive power for risk of death as information provided by clinical characteristics. Conclusions: A plasma metabolomic profile characterized by an oxidative energy production difference between survivors and non-survivors was observed in COPD patients 2 years before death

    Plasma metabolomics and clinical predictors of survival differences in COPD patients

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    Background: Plasma metabolomics profile (PMP) in COPD has been associated with clinical characteristics, but PMP’s relationship to survival has not been reported. We determined PMP differences between patients with COPD who died an average of 2 years after enrollment (Non-survivors, NS) compared to those who survived (S) and also with age matched controls (C). Methods: We studied prospectively 90 patients with severe COPD and 30 controls. NS were divided in discovery and validation cohorts (30 patients each) and the results compared to the PMP of 30 S and C. All participants completed lung function tests, dyspnea scores, quality of life, exercise capacity, BODE index, and plasma metabolomics by liquid and gas chromatography / mass spectometry (LC/MS, LC/MS2 , GC/MS). Statistically, we used Random Forest Analysis (RFA) and Support Vector Machine (SVM) to determine metabolites that differentiated the 3 groups and compared the ability of metabolites vs. clinical characteristics to classify patients into survivors and non-survivors. Results: There were 79 metabolites statistically different between S and NS [p < 0.05 and false discovery rate (q value) < 0.1]. RFA and SVM classification of COPD survivors and non-survivors had a predicted accuracy of 74 and 85% respectively. Elevation of tricyclic acid cycle intermediates branched amino acids depletion and increase in lactate, fructose and xylonate showed the most relevant differences between S vs. NS suggesting alteration in mitochondrial oxidative energy generation. PMP had similar predictive power for risk of death as information provided by clinical characteristics. Conclusions: A plasma metabolomic profile characterized by an oxidative energy production difference between survivors and non-survivors was observed in COPD patients 2 years before death

    Protease activity sensors enable real-time treatment response monitoring in lymphangioleiomyomatosis

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    BackgroundBiomarkers of disease progression and treatment response are urgently needed for patients with lymphangioleiomyomatosis (LAM). Activity-based nanosensors, an emerging biosensor class, detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease. Because proteases are dysregulated in LAM and may directly contribute to lung function decline, activity-based nanosensors may enable quantitative, real-time monitoring of LAM progression and treatment response. We aimed to assess the diagnostic utility of activity-based nanosensors in a pre-clinical model of pulmonary LAM.MethodsTsc2-null cells were injected intravenously into female nude mice to establish a mouse model of pulmonary LAM. A library of 14 activity-based nanosensors, designed to detect proteases across multiple catalytic classes, was administered into the lungs of LAM mice and healthy controls, urine was collected, and mass spectrometry was performed to measure nanosensor cleavage products. Mice were then treated with rapamycin and monitored with activity-based nanosensors. Machine learning was performed to distinguish diseased from healthy and treated from untreated mice.ResultsMultiple activity-based nanosensors (PP03 (cleaved by metallo, aspartic and cysteine proteases), padjusted&lt;0.0001; PP10 (cleaved by serine, aspartic and cysteine proteases), padjusted=0.017)) were differentially cleaved in diseased and healthy lungs, enabling strong classification with a machine learning model (area under the curve (AUC) 0.95 from healthy). Within 2 days after rapamycin initiation, we observed normalisation of PP03 and PP10 cleavage, and machine learning enabled accurate classification of treatment response (AUC 0.94 from untreated).ConclusionsActivity-based nanosensors enable noninvasive, real-time monitoring of disease burden and treatment response in a pre-clinical model of LAM.</jats:sec
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