Transcriptional control of prostate cancer metabolism

242 p.La transformación y la progresión tumoral van acompañados de cambios metabólicos. Los principales co-reguladores metabólicos son capaces de orquestar la modulación de múltiples rutas metabólicas a través de la regulación de programas transcripcionales. En esta tesis hemos mostrado que el co-regulador transcripcional y metabólico PGC1 suprime la progresión tumoral y metastática en el cáncer de próstata. El análisis bioinformatico de los principales co-reguladores metabólicos reveló a PGC1 como factor principalmente alterado en cáncer de próstata, cuya expresión disminuía y estaba asociada a la progresión tumoral. Usando modelos de ratón y xenotransplantes, demostramos la inhibición de la progresión tumoral y metástasis por parte de PGC1. La integración de análisis metabólicos y transcriptómicos reveló que PGC1 activa un programa transcripcional dependiente del factor de transcripción ERR, el cual inducía un estado catabólico responsable de la supresión metastática. Además, hemos observado que la expresión de este co-regulador altera la remodelación del citoesqueleto, induciendo cambios en la morfología celular. Finalmente, una marca genética basada en la activación del programa transcripcional (PGC1-ERR) exhibe potencial pronóstico en cáncer de próstata. El uso tanto de la marca genética como del estudio metabólico realizado podría permitir la estratificación de pacientes y el desarrollo de nuevas terapias en el cáncer de próstata.CICbioGUN

Mitochondrial metabolism in cancer transformation and progression

Cancer cells undergo a multifaceted rewiring of cellular metabolism to support their biosynthetic needs. Although the major determinants of this metabolic transformation have been elucidated, their broad biological implications and clinical relevance are unclear. In this study, I systematically analysed the expression of metabolic genes across 20 different cancer types and investigated their impact on clinical outcome. I found that cancers undergo a tissue-specific metabolic rewiring, which converges towards a common metabolic landscape. Of note, downregulation of mitochondrial genes is associated with the worst clinical outcome across all cancer types and correlates with the expression of epithelial-to-mesenchymal transition (EMT) gene signature, a feature of invasive and metastatic cancers. Consistently, suppression of mitochondrial genes is identified as key metabolic signature of metastatic melanoma and renal cancer, and metastatic cell lines. This comprehensive analysis reveals unexpected facets of cancer metabolism, with important implications for cancer patients stratification, prognosis, and therapy. I then investigated how mitochondrial dysfunction could affect cell behaviour. I capitalised on a recently developed in vitro cell model with increasing levels of m.8993T>G mutation heteroplasmy. I found that impaired utilisation of reduced nicotinamide adenine dinucleotide (NADH) by the mitochondrial respiratory chain leads to cytosolic reductive carboxylation of glutamine as a new mechanism for cytosol-confined NADH recycling supported by malate dehydrogenase 1 (MDH1). This metabolic coupling is facilitated by the formation of a multienzymatic complex between MDH1 and GAPDH. Importantly, such metabolic coupling between glutamine metabolism and cytosolic NADH recycling is able to support increased glycolytic flux, an important hallmark of cells with dysfunctional mitochondria, as well as cancer cells. Finally, increased glycolysis in cells with mitochondrial dysfunction is associated with enhanced cell migration, in an MDH1-dependent fashion. These results describe a novel link between glycolysis and mitochondrial dysfunction, and uncover potential targets for cells that rely on aerobic glycolysis for proliferation and migration, such as cancer cells.MRC DTA studentshi

Computational models of melanoma.

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research

Metabolic rewiring in mutant Kras lung cancer.

Lung cancer is the leading cause of cancer-related death worldwide, reflecting an unfortunate combination of very high prevalence and low survival rates, as most cases are diagnosed at advanced stages when treatment efficacy is limited. Lung cancer comprises several disease groups with non small cell lung cancer (NSCLC) accounting for ~ 85% of cases and lung adenocarcinoma being its most frequent histological subtype. Mutations in Kirsten rat sarcoma viral oncogene homologue (KRAS) affect ~ 30% of lung adenocarcinomas but unlike other commonly altered proteins (EGFR and ALK, affected in ~ 14% and 7% of cases respectively), mutant KRAS remains untargetable. Therapeutic strategies that rely instead on the inhibition of mutant KRAS functional output or the targeting of mutant KRAS cellular dependencies (i.e. synthetic lethality) are an appealing alternative approach. Recent studies focused on the metabolic properties of mutant KRAS lung tumours have uncovered unique metabolic features that can potentially be exploited therapeutically. We review these findings here with a particular focus on in vivo, physiologic, mutant KRAS activity

Cysteine and Folate metabolism are targetable vulnerabilities of metastatic colorectal cancer

With most cancer-related deaths resulting from metastasis, the development of new therapeutic approaches against metastatic colorectal cancer (mCRC) is essential to increasing patient survival. The metabolic adaptations that support mCRC remain undefined and their elucidation is crucial to identify potential therapeutic targets. Here, we employed a strategy for the rational identification of targetable metabolic vulnerabilities. This strategy involved first a thorough metabolic characterisation of same-patient-derived cell lines from primary colon adenocarcinoma (SW480), its lymph node metastasis (SW620) and a liver metastatic derivative (SW620-LiM2), and second, using a novel multi-omics integration workflow, identification of metabolic vulnerabilities specific to the metastatic cell lines. We discovered that the metastatic cell lines are selectively vulnerable to the inhibition of cystine import and folate metabolism, two key pathways in redox homeostasis. Specifically, we identified the system xCT and MTHFD1 genes as potential therapeutic targets, both individually and combined, for combating mCRC

Research into cancer metabolomics: towards a clinical metamorphosis

The acknowledgement that metabolic reprogramming is a central feature of cancer has generated high expectations for major advances in both diagnosis and treatment of malignancies through addressing metabolism. These have so far only been partially fulfilled, with only a few clinical applications. However, numerous diagnostic and therapeutic compounds are currently being evaluated in either clinical trials or pre-clinical models and new discoveries of alterations in metabolic genes indicate future prognostic or other applicable relevance. Altogether, these metabolic approaches now stand alongside other available measures providing hopes for the prospects of metabolomics in the clinic. Here we present a comprehensive overview of both ongoing and emerging clinical, pre-clinical and technical strategies for exploiting unique tumour metabolic traits, highlighting the current promises and anticipations of research in the field

Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods

Background: Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far.Methods: Here, we propose and implement an interpretable multi-omics pipeline that combines mechanis-tic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma.Results and Conclusions: While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics

Defining Roles of Metabolic Reprogramming in Pancreatic Tumorigenesis and Tumor Maintenance

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Nearly all pancreatic tumors harbor mutations in oncogenic KRAS. Unfortunately, KRAS is difficult to target therapeutically, despite decades of efforts. As such, KRAS-dependent pathways remain promising targets for the development of new therapeutics. Pancreatic cancer extensively reprograms cellular metabolism to support uncontrolled growth and proliferation. Mutations in oncogenic KRAS drive metabolic rewiring that PDA cells are dependent on to supply biosynthetic precursors and energy. Understanding the metabolic dependencies of tumorigenesis and tumor maintenance could reveal targetable vulnerabilities for disease detection and/or treatment. Acinar cells can give rise to pancreatic tumors through acinar-to-ductal metaplasia (ADM), and inhibiting pathways that maintain acinar homeostasis can accelerate tumorigenesis. During ADM, acinar cells transdifferentiate to duct-like cells, a process driven by oncogenic KRAS, and one that we hypothesized was mediated by metabolic rewiring. Transcriptomic analysis revealed global enhancement of metabolic programs in acinar cells undergoing ADM. We previously demonstrated that pancreatic cancer cells rewire glucose and glutamine metabolism to support growth and survival. Using in vitro models of ADM, we found that glutamine availability is not required for ADM. In contrast, glucose availability and intact oxidative phosphorylation are required for ADM. A more detailed analysis of the pathways downstream of glucose metabolism revealed that disrupting the oxidative pentose phosphate pathway accelerates ADM in vitro and tumorigenesis in vivo, likely due to heightened oxidative stress. Changes in redox balance can attenuate or accelerate ADM in vitro and in vivo. Redox homeostasis is also tightly regulated in pancreatic cancer cells by rewiring glutamine metabolism through a glutamate oxaloacetate transaminase 1 (GOT1)-dependent pathway. GOT1 inhibition disrupts redox homeostasis in pancreatic cancer cells. These insights were leveraged in PDA, where we demonstrate that radiotherapy potently enhanced the effect of GOT1 inhibition on tumor growth. Understanding the metabolic pathways that contribute to pancreatic tumorigenesis and tumor maintenance, such as redox homeostasis, could provide biomarkers for diagnosis of early disease or development of better therapeutics for treating pancreatic cancer.PHDCancer BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163123/1/barbnels_1.pd