MicroRNA-21 is Induced by Rapamycin in a Model of Tuberous Sclerosis (TSC) and Lymphangioleiomyomatosis (LAM)

Lymphangioleiomyomatosis (LAM), a multisystem disease of women, is manifest by the proliferation of smooth muscle-like cells in the lung resulting in cystic lung destruction. Women with LAM can also develop renal angiomyolipomas. LAM is caused by mutations in the tuberous sclerosis complex genes (TSC1 or TSC2), resulting in hyperactive mammalian Target of Rapamycin (mTOR) signaling. The mTOR inhibitor, Rapamycin, stabilizes lung function in LAM and decreases the volume of renal angiomyolipomas, but lung function declines and angiomyolipomas regrow when treatment is discontinued, suggesting that factors induced by mTORC1 inhibition may promote the survival of TSC2-deficient cells. Whether microRNA (miRNA, miR) signaling is involved in the response of LAM to mTORC1 inhibition is unknown. We identified Rapamycin-dependent miRNA in LAM patient angiomyolipoma-derived cells using two separate screens. First, we assayed 132 miRNA of known significance to tumor biology. Using a cut-off of >1.5-fold change, 48 microRNA were Rapamycin-induced, while 4 miRs were downregulated. In a second screen encompassing 946 miRNA, 18 miRs were upregulated by Rapamycin, while eight were downregulated. Dysregulation of miRs 29b, 21, 24, 221, 106a and 199a were common to both platforms and were classified as candidate “RapamiRs.” Validation by qRT-PCR confirmed that these microRNA were increased. miR-21, a pro-survival miR, was the most significantly increased by mTOR-inhibition (p<0.01). The regulation of miR-21 by Rapamycin is cell type independent. mTOR inhibition promotes the processing of the miR-21 transcript (pri-miR-21) to a premature form (pre-miR-21). In conclusion, our findings demonstrate that Rapamycin upregulates multiple miRs, including pro-survival miRs, in TSC2-deficient patient-derived cells. The induction of miRs may contribute to the response of LAM and TSC patients to Rapamycin therapy

AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer

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, Erzsébet 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 Científicas y Técnicas; 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

5′AMP-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

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 &lt; 0.05 and false discovery rate (q value) &lt; 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

Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells

Lymphangioleiomyomatosis (LAM) is a progressive neoplastic disorder that leads to lung destruction and respiratory failure primarily in women. LAM is typically caused by tuberous sclerosis complex 2 (TSC2) mutations resulting in mTORC1 activation in proliferative smooth muscle–like cells in the lung. The female predominance of LAM suggests that estradiol contributes to disease development. Metabolomic profiling identified an estradiol-enhanced prostaglandin biosynthesis signature in Tsc2-deficient (TSC−) cells, both in vitro and in vivo. Estradiol increased the expression of cyclooxygenase-2 (COX-2), a rate-limiting enzyme in prostaglandin biosynthesis, which was also increased at baseline in TSC-deficient cells and was not affected by rapamycin treatment. However, both Torin 1 treatment and Rictor knockdown led to reduced COX-2 expression and phospho-Akt-S473. Prostaglandin production was also increased in TSC-deficient cells. In preclinical models, both Celecoxib and aspirin reduced tumor development. LAM patients had significantly higher serum prostaglandin levels than healthy women. 15-epi-lipoxin-A4 was identified in exhaled breath condensate from LAM subjects and was increased by aspirin treatment, indicative of functional COX-2 expression in the LAM airway. In vitro, 15-epi-lipoxin-A4 reduced the proliferation of LAM patient–derived cells in a dose-dependent manner. Targeting COX-2 and prostaglandin pathways may have therapeutic value in LAM and TSC-related diseases, and possibly in other conditions associated with mTOR hyperactivation

Plasma metabolomics and clinical predictors of survival differences in COPD patients

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

Untargeted metabolomics for profiling oncogene-specific metabolic signatures of prostate cancer

Oncogene-associated metabolic signatures in prostate cancer, identified by an integrative analysis of cultured cells and murine and human tumors, suggest that AKT activation results in a glycolytic phenotype whereas MYC induces aberrant lipid metabolism. Heterogeneity in human tumors makes this simplistic interpretation obtained from experimental models more challenging. Metabolic reprogramming as a function of distinct molecular aberrations has major diagnostic and therapeutic implications

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

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