Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

The metabolite pool of cells and tissues represents the end result of metabolism determined by genetic, environmental and nutritional factors. The metabolic profile of biological systems is closely related to the individual phenotype and reflects the biological endpoint of a multitude of pathways and their interaction with any confounding stimuli. Cancer cells exhibit activation of specific metabolic pathways to compensate for their extremely high energy demands. Indeed increased glucose uptake and lactate production and decreased respiration are key phenomena of tumour cell metabolism. In particular, the generation of an acidic microenvironment through increased lactate production, even under aerobic conditions, may confer extracellular matrix degeneration and exert toxic effects on surrounding cell populations, while being harmless for the cancer cell itself. Thus, the metabolic adaptations may indeed be critical for the development of accelerated proliferation and the invasive growth of tumour cell populations. The molecular mechanisms underlying the metabolic hallmarks of cancer are still poorly understood, although genetic, epigenetic and environmental factors driving cancer development and progression will interact to determine the metabolic phenotype of tumour cells. Recent studies suggest that metabolic changes play a pivotal role in the biology of renal cell carcinoma – a tumour entity that is largely resistant to conventional chemo- and radiotherapy. The metabolic profile of renal tumours may thus serve as a reliable biomarker of malignant transformation and biological behaviour.

Recent advances in metabolic profiling technologies by providing quantitative measures of metabolite profiles from gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) based technology present the opportunity to apply this technique in human specimens. Global metabolic profiling has emerged as a promising approach to characterize the metabolite pool within a cell, tissue or bodily fluid under certain conditions, such as health or disease status. Metabolic profiling is applied to monitor the health to disease continuum and has the potential of increasing our understanding of the mechanisms of disease. Thus the characterization of the metabolic features in tumours is expected to provide a better understanding of the mechanisms of malignant transformation and progression and may lead to the identification of metabolic biomarkers for cancer detection and prognostication. However, comparative profiling of low molecular weight compounds, such as sugars, lipids and amino acids, in cancer as compared to the corresponding normal tissue is a rather unexplored area. The objective of this study was to characterize the key metabolic features of renal cell carcinoma using GC-TOF-MS and mutual information as well as decision tree-based data analysis.

Hypoxia is key in tumour cell behaviour. Hypoxia, via hypoxia inducible factor, plays a key role in the metabolic changes in the kidney cancer cell and influences different pathways. Pathways that use tyrosine kinases and mammalian target of rapamycin are well studied. Hypoxia-related effects on vascular epithelial growth factors and angiogenesis will influence the metabolic status of the cell significantly. This is the basis of inhibitor-type drugs or antibody blocking agents and the effect on the clinical course of renal cell carcinoma patients. Detailed information about metabolic changes is crucial to understanding these mechanisms more clearly. Treating renal cell carcinoma patients is not like treating one disease. Renal cell carcinoma has different morphologic entities with distinct differences in cytogenetic background. These differences should be reflected in the different approaches of diagnostic and therapeutic strategies. This consideration will help to increase the efficacy of novel agents and decrease unnecessary side-effects. Metabolomics explains the importance of explaining genetic changes and the functional outcome of the tumour cells. In addition, epidemiologic differences in incidence and prevalence in different parts of the world may help provide insight into the etiology of kidney cancer. Factors that may influence renal cancer likelihood (eg, obesity, antihypertensive therapy) may have an explanation in metabolomics.

Source References:

http://www.europeanurology.com/article/S0302-2838(12)01352-8/fulltext

http://www.ncbi.nlm.nih.gov/pubmed/19845817

http://www.ncbi.nlm.nih.gov/pubmed/18072195

http://www.ncbi.nlm.nih.gov/pubmed/17123452

http://www.ncbi.nlm.nih.gov/pubmed/20464042

http://www.ncbi.nlm.nih.gov/pubmed/21930086

https://www.google.com.tw/url?sa=t&rct=j&q=&esrc=s&source=web&cd=8&cad=rja&ved=0CGwQFjAH&url=http%3A%2F%2Fjournals.sfu.ca%2Fcuaj%2Findex.php%2Fjournal%2Farticle%2Fdownload%2F665%2F466&ei=-ub8UbD0JcfolAXeyYGYBw&usg=AFQjCNFIzdti065pBEGKlEDkBDCPA4IwUw&sig2=scgq8qT9ffKa65MxAvpuNg&bvm=bv.50165853,d.dGI