Pathway analysis of kidney cancer using proteomics and metabolic profiling

Analysis of peptides in biological fluids by LC-MS/MS

Urine contains large amounts of small peptides, which may represent a rich, yet largely unexplored, source of novel biomarkers for disease monitoring. This chapter describes detailed procedures for the analysis of urinary polypeptides by LC-MS/MS. Hundreds to thousands of small peptides (∼700 to ∼7000 Da) can be detected in urine with the described techniques. Extraction procedures, based on commercially available reagents, effectively remove interfering urinary organic and inorganic salts and neutral compounds, making this a robust and simple assay with the power to detect hundreds to thousands of polypeptides in urine. Analysis time is relatively short, making this protocol a valuable alternative to conventional proteomic techniques based on multidimensional separations. The methodology is therefore particularly useful when the aim is to analyse samples with sufficient depth and throughput so as to make it useful to compare large numbers of specimens. Procedures for enhancing quantitative and qualitative analysis of LC-MS/MS data are also detailed.

Introduction to omics

Exploiting the potential of omics for clinical diagnosis, prognosis, and therapeutic purposes has currently been receiving a lot of attention. In recent years, most of the effort has been put into demonstrating the possible clinical applications of the various omics fields. The cost-effectiveness analysis has been, so far, rather neglected. The cost of omics-derived applications is still very high, but future technological improvements are likely to overcome this problem. In this chapter, we will give a general background of the main omics fields and try to provide some examples of the most successful applications of omics that might be used in clinical diagnosis and in a therapeutic context.

Toxicometabolomics of urinary biomarkers for human gastric cancer in a mouse model

Toxicometabolomics of urinary biomarkers for human gastric cancer in a mouse model was investigated using (1)H-nuclear magnetic resonance (NMR) spectroscopy. A human gastric adenocarcinoma cell line (1 × 10(7) cells/ml) was grafted onto the skin of the back of intact male BALB/c-nu/nu mice. After the xenografted tumors developed, urine was collected and analyzed for endogenous metabolites. Global profiling combined with principal components analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal projections to latent squares-discriminant analysis (OPLS-DA) showed distinct separation of clusters between control and tumor-bearing mice. Targeted profiling revealed significant changes in trimethylamine oxide (TMAO), 3-indoxylsulfate, hippurate, and citrate levels in mice carrying human gastric cancer cells compared to normal mice. The levels of TMAO (0.41-fold) and hippurate (0.26-fold) in tumor-bearing mice were significantly decreased, whereas the levels of 3-indoxylsulfate (3.39-fold), 2-oxoglutarate (2.32-fold), and citrate (1.9-fold) were significantly increased in urine samples of tumor-bearing mice. Data suggest that TMAO, hippurate, 3-indoxylsulfate, 2-oxoglutarate, and citrate may serve as useful urinary biomarkers for gastric tumorigenesis in a mouse model.

Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer

We report here the results of a pilot study in which ultra-high performance liquid chromatography/time-of- flight-mass spectrometry (UPLC/TOF-MS) and multivariate statistical analysis (supervised partial least squares discriminant analysis, PLS-DA) were applied for urinary metabolite profiling and data interpretation. The results of the PLS-DA indicated that the metabolic pattern as a whole was significantly different between the groups of preoperative colorectal cancer (CRC) patients, postoperative CRC patients, and healthy volunteers, respectively. The preoperative group of patients showed significantly increased levels of low-molecular weight compounds (LMC) MW 283 and MW 234 in comparison to the group of healthy volunteers group. After the operation, the levels of these two LMC significantly decreased. These preliminary results suggest that the UPLC-MS-based method coupled with pattern recognition will likely lead to procedures with the potential to be clinically applicable for the diagnosis of CRC and, consequently, to an improvement in patient prognosis.

Systematic comparative protein expression profiling of clear cell renal cell carcinoma: a pilot study based on the separation of tissue specimens by two-dimensional gel electrophoresis

Proteome-based technologies represent powerful tools for the analysis of protein expression profiles, including the identification of potential cancer candidate biomarkers. Thus, here we provide a comprehensive protein expression map for clear cell renal cell carcinoma established by systematic comparative two-dimensional gel electrophoresis-based protein expression profiling of 16 paired tissue systems comprising clear cell renal cell carcinoma lesions and corresponding tumor-adjacent renal epithelium using overlapping narrow pH gradients. This approach led to the mapping of 348 distinct spots corresponding to 248 different protein identities. By implementing restriction criteria concerning their detection frequency and overall regulation mode, 28 up- and 56 down-regulated single target spots were considered as potential candidate biomarkers. Based on their gene ontology information, these differentially expressed proteins were classified into distinct functional groups and according to their cellular distribution. Moreover, three representative members of this group, namely calbindin, gelsolin, and heart fatty acid-binding protein, were selected, and their expression pattern was analyzed by immunohistochemistry using tissue microarrays. Thus, this pilot study provides a significant update of the current renal cell carcinoma map and defines a number of differentially expressed proteins, but both their potential as candidate biomarkers and clinical relevance has to be further explored in tissues and for body fluids like serum and urine.

Tumor-Associated Antigens and Biomarkers in Cancer and Immune Therapy

Cancer has currently overtaken heart disease as the major cause of mortality in the United States. The Human Genome Project, advances in informatics, miniaturization of sample collection, and increased knowledge of cell signaling pathways has revolutionized the study of disease. Genomics, proteomics, and metabolomics are currently being used to develop molecular signatures for disease diagnosis, prognosis, and therapeutic efficacy. Tumor-associated antigens discovered by these methods are being used to develop passive (humoral) as well as active immunotherapy strategies to stimulate the immune system. Development and validation of biomarkers on a parallel track with therapeutics can speed development times by accurate screening of patient populations and substituting surrogate markers that correlate well with clinical outcomes.

The non-invasive biopsy--will urinary proteomics make the renal tissue biopsy redundant?

Proteomics is a rapidly advancing technique which gives functional insight into gene expression in living organisms. Urine is an ideal medium for study as it is readily available, easily obtained and less complex than other bodily fluids. Considerable progress has been made over the last 5 years in the study of urinary proteomics as a diagnostic tool for renal disease. Advantages over the traditional renal biopsy include accessibility, safety, the possibility of serial sampling and the potential for non-invasive prognostic and diagnostic monitoring of disease and an individual's response to treatment. Urinary proteomics is now moving from a discovery phase in small studies to a validation phase in much larger numbers of patients with renal disease. Whilst there are still some limitations in methodology, which are assessed in this review, the possibility of urinary proteomics replacing the invasive tissue biopsy for diagnosis of renal disease is becoming an increasingly realistic option.

Early alterations in protein and gene expression in rat kidney following bromate exposure

Bromate, a common disinfectant byproduct of drinking water ozonation, has been linked to human and animal renal toxicity, including renal cell carcinomas in multiple animal species. Here, we evaluate changes in protein and gene expression through two-dimensional difference gel electrophoresis (2D-DIGE) and Affymetrix arrays to identify potential modes of action involved in potassium bromate carcinogenicity. Male rats were exposed to potassium bromate in drinking water at concentrations of 0, 1, 20 and 400 ppm for two weeks. Differential expression of glycolytic proteins including enolase 1 (Eno1), triosephosphate isomerase 1 (Tpi1) and glyceraldehyde-3-phosphate dehydrogenase (Gapdh) suggests that bromate toxicity is associated with changes in energy consumption and utilization in renal cells involving up-regulation of glycolytic processes that may be the result of altered mitochondrial function. Several alterations in glycolysis and mitochondrial gene transcripts were also observed to be consistent with this mode of action. These studies provide insight into early events in renal cell physiology altered by bromate exposure.

PLIPS, an automatically collected database of protein lists reported by proteomics studies

The spectrum of problems covered by proteomics studies range from the discovery of compartment specific cell proteomes to clinical applications, including the identification of diagnostic markers and monitoring the effects of drug treatments. In most cases, the ultimate results of a proteomics study are lists of proteins found to be present (or differentially present) at cell physiological conditions under study. Normally, the results are published directly in the article in one or several tables. In many cases, this type of information remains disseminated in hundreds of proteomics publications. We have developed a Web mining tool which allows the collection of this information by searching through full text papers and automatically selecting tables, which report a list of protein identifiers. By searching through major proteomics journals, we have collected approximately 800 independent studies published recently, which reported about 1000 different protein lists. On the basis of this data, we developed a computational tool PLIPS (Protein Lists Identified in Proteomics Studies). PLIPS accepts as input a list of protein/gene identifiers. With the use of statistical analyses, PLIPS infers recently published proteomics studies, which report protein lists that significantly intersect with a query list. PLIPS is a freely available Web-based tool ( http://mips.helmholtz-muenchen.de/proj/plips ).

Clara cells attenuate the inflammatory response through regulation of macrophage behavior

Chronic lung diseases are marked by excessive inflammation and epithelial remodeling. Reduced Clara cell secretory function and corresponding decreases in the abundance of the major Clara cell secretory protein (CCSP) are characteristically seen in these disease states. We sought to define the impact of Clara cell and CCSP depletion on regulation of the lung inflammatory response. We used chemical and genetic mouse models of Clara cell and CCSP deficiency (CCSP(-/-)) coupled with Pseudomonas aeruginosa LPS elicited inflammation. Exposure of Clara cell-depleted or CCSP(-/-) mice to LPS resulted in augmented inflammation as assessed by polymorphonuclear leukocyte recruitment to the airspace. Gene expression analysis and pathway modeling of the CCSP(-/-) inflammatory response implicated increased TNF-alpha signaling. Consistent with this model was the demonstration of significantly elevated TNF-alpha in airway fluid of LPS-stimulated CCSP(-/-) mice compared with similarly exposed wild-type mice. Increased LPS-elicited TNF-alpha production was also observed in cultured lung macrophages from CCSP(-/-) mice compared with wild-type mice. We demonstrate that macrophages from Clara cell-depleted and CCSP(-/-) mice displayed increased Toll-like receptor 4 surface expression. Our results provide evidence that Clara cells can attenuate inflammation through regulation of macrophage behavior, and suggest that epithelial remodeling leading to reduced Clara cell secretory function is an important factor that increases the intensity of lung inflammation in chronic lung disease.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.

From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma

Among the adult population, renal cell carcinoma (RCC) constitutes the most prevalent form of kidney neoplasm. Unfortunately, RCC is relatively asymptomatic and there are no tumor markers available for diagnostic, prognostic or predictive purposes. Molecular profiling, the global analysis of gene and protein expression profiles, is an emerging promising tool for new biomarker identification in RCC. In this review, we summarize the existing knowledge on RCC regarding clinical presentation, treatment options, and tumor marker status. We present a general overview of the more commonly used approaches for molecular profiling at the genomic, transcriptomic and proteomic levels. We also highlight the emerging role of molecular profiling as not only revolutionizing the process of new tumor marker discovery, but also for providing a better understanding of the pathogenesis of RCC that will pave the way towards new targeted therapy discovery. Furthermore, we discuss the spectrum of clinical applications of molecular profiling in RCC in the current literature. Finally, we highlight some of the potential challenging that faces the era of molecular profiling and its transition into clinical practice, and provide an insight about the future perspectives of molecular profiling in RCC.

Grade-dependent proteomics characterization of kidney cancer

Kidney cancer is frequently metastatic on presentation at which point the disease is associated with a 95% mortality. Assessment of tumor grade on pathological examination is the most powerful means for prognostication as well as for stratification of patients into those who might respond to conventional or targeted therapy. Although there exist several grading systems in common use, all suffer from significant disparity among observers. In an attempt to objectify this process as well as to acquire grade-specific mechanistic information, we performed LC-MS/MS-based proteomics analysis on 50 clear cell kidney cancers equally distributed among normal tissues and Fuhrman grades 1-4. Initial experiments confirmed the utility of using archived formalin-fixed paraffin-embedded samples for LC-MS/MS-based proteomics analysis, and the LC-MS/MS findings were validated by extensive immunoblotting. We now show that changes among many biochemical processes and pathways are strongly grade-dependent with the glycolytic and amino acid synthetic pathways highly represented. In addition, proteins relating to acute phase and xenobiotic metabolism signaling are highly represented. Self-organized mapping of proteins with similar patterns of expression led to the creation of a heat map that will be useful in grade characterization as well as in future research relating to oncogenic mechanisms and targeted therapies for kidney cancer.

KEGG spider: interpretation of genomics data in the context of the global gene metabolic network

KEGG spider is a web-based tool for interpretation of experimentally derived gene lists in order to gain understanding of metabolism variations at a genomic level. KEGG spider implements a 'pathway-free' framework that overcomes a major bottleneck of enrichment analyses: it provides global models uniting genes from different metabolic pathways. Analyzing a number of experimentally derived gene lists, we demonstrate that KEGG spider provides deeper insights into metabolism variations in comparison to existing methods.

Metabolomic analyses of body fluids after subchronic manganese inhalation in rhesus monkeys

Neurotoxicity is linked with high-dose manganese inhalation. There are few biomarkers that correlate with manganese exposure. Blood manganese concentrations depend upon the magnitude and duration of the manganese exposure and inconsistently reflect manganese exposure concentrations. The objective of this study was to search for novel biomarkers of manganese exposure in the urine and blood obtained from rhesus monkeys following subchronic manganese sulfate (MnSO(4)) inhalation. Liquid chromatography-mass spectrometry was used to identify putative biomarkers. Juvenile rhesus monkeys were exposed 5 days/week to airborne MnSO(4) at 0, 0.06, 0.3, or 1.5 mg Mn/m(3) for 65 exposure days or 1.5 mg Mn/m(3) for 15 or 33 days. Monkeys exposed to MnSO(4) at >or= 0.06 mg Mn/m(3) developed increased brain manganese concentrations. A total of 1097 parent peaks were identified in whole blood and 2462 peaks in urine. Principal component analysis was performed on a subset of 113 peaks that were found to be significantly changed following subchronic manganese exposure. Using the Nearest Centroid analysis, the subset of 113 significantly perturbed components predicted globus pallidus manganese concentrations with 72.9% accuracy for all subchronically exposed monkeys. Using the five confirmed components, the prediction rate for high brain manganese levels remained > 70%. Three of the five identified components, guanosine, disaccharides, and phenylpyruvate, were significantly correlated with brain manganese levels. In all, 27 metabolites with statistically significant expression differences were structurally confirmed by MS-MS methods. Biochemical changes identified in manganese-exposed monkeys included endpoints relate to oxidative stress (e.g., oxidized glutathione) and neurotransmission (aminobutyrate, glutamine, phenylalanine).

Overexpression of calcineurin B subunit (CnB) enhances the oncogenic potential of HEK293 cells

Calcineurin (CaN) is a Ca(2+)/calmodulin (CaM)-stimulated protein phosphatase. It is a heterodimeric enzyme consisting of a catalytic A subunit (CnA) and a Ca(2+)-binding regulatory B subunit (CnB). CaN's key role in vivo is well known, while the function of CnB keeps unclear except that it acts as a regulator of CaN. The present paper shows that CnB overexpression promotes proliferation of human embryonic kidney HEK293 cells by comparing with vector control cells in the complete or serum reduced medium. Furthermore, stable CnB transfectants showed dramatically improved growth in soft agar. And the migration ability of CnB overexpressors also was enhanced remarkably. But in the progress of transformation, the activity of CaN remained unchanged between CnB overexpressors and controls. Therefore, CnB, rather than CaN, is involved in the proliferation promotion of HEK293 cells. Subsequently, 11 proteins with different expression levels between CnB transfectants and controls were identified using two-dimensional gel electrophoresis and electrospray ionization time-of-fight mass spectrometry. Therein, the expression of heat shock protein 27 (HSP27) and protein DJ-1 increased along with CnB overexpression. The important role of CnB in cell neoplastic transformation was found and the possible mechanism was analyzed.

Integration of metabolomic and proteomic phenotypes: analysis of data covariance dissects starch and RFO metabolism from low and high temperature compensation response in Arabidopsis thaliana

Statistical mining and integration of complex molecular data including metabolites, proteins, and transcripts is one of the critical goals of systems biology (Ideker, T., Galitski, T., and Hood, L. (2001) A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372). A number of studies have demonstrated the parallel analysis of metabolites and large scale transcript expression. Protein analysis has been ignored in these studies, although a clear correlation between transcript and protein levels is shown only in rare cases, necessitating that actual protein levels have to be determined for protein function analysis. Here, we present an approach to investigate the combined covariance structure of metabolite and protein dynamics in a systemic response to abiotic temperature stress in Arabidopsis thaliana wild-type and a corresponding starch-deficient mutant (phosphoglucomutase-deficient). Independent component analysis revealed phenotype classification resolving genotype-dependent response effects to temperature treatment and genotype-independent general temperature compensation mechanisms. An observation is the stress-induced increase of raffinose-family-oligosaccharide levels in the absence of transitory starch storage/mobilization in temperature-treated phosphoglucomutase plants indicating that sucrose synthesis and storage in these mutant plants is sufficient to bypass the typical starch storage/mobilization pathways under abiotic stress. Eventually, sample pattern recognition and correlation network topology analysis allowed for the detection of specific metabolite-protein co-regulation and assignment of a circadian output regulated RNA-binding protein to these processes. The whole concept of high-dimensional profiling data integration from many replicates, subsequent multivariate statistics for dimensionality reduction, and covariance structure analysis is proposed to be a major strategy for revealing central responses of the biological system under study.

AQP1 expression analysis in human diseases: implications for proteomic characterization

Aquaporin (AQP)1 belongs to a ubiquitous family of water channel proteins characterized by sequence similarity and the presence of two NPA (Asp-Pro-Ala) motifs existing in almost all organs and tissues. Currently, 13 human AQPs are known and they are divided into two subgroups according to their ability to transport only water molecules, such as AQP1, or also glycerol and other small solutes. The genomic, structural and functional aspects of AQP1 are briefly described. An in-depth discussion is devoted to proteomic approaches that are useful for identifying and characterizing AQP1, mainly through electrophoretic techniques combined with different extraction procedures followed by mass spectrometry analysis. Moreover, the relevance of AQP1 in human diseases is also explained. Its role in human tumors and, in particular, those of the kidney (e.g., clear cell renal carcinoma) is discussed.

Identification of small molecules from human embryonic stem cells using metabolomics

Metabolomics enables the discovery of small molecules that may serve as candidate biomarkers of pharmacological efficacy or toxicity. Biochemical pathways of human development are likely active in human embryonic stem (hES) cells and derivatives, since they recapitulate organogenesis in vitro. We hypothesized that small molecules could be measured from undifferentiated hES cells and hES cell-derived neural precursors (hNPs) using metabolomics and that these compounds are altered in response to known disruptors of human development. Metabolite profiling was performed in hES cells and hNPs after exposure to valproate, an inducer of neurodevelopmental disorders. Kynurenine, an intermediate in tryptophan metabolism, and other small molecules in glutamate metabolism were significantly upregulated in response to valproate. Thus, for the first time, we have been able to measure and identify small molecules secreted from hES cells and cells derived from hES cells. The hES cell metabolome may thus serve as a source of candidate biomarkers to predict or measure pharmacological efficacy or toxic response.

Urine proteomics: the present and future of measuring urinary protein components in disease

For centuries, physicians have attempted to use the urine for noninvasive assessment of disease. Today, urinalysis, in particular the measurement of proteinuria, underpins the routine assessment of patients with renal disease. More sophisticated methods for assessing specific urinary protein losses have emerged; however, albumin is still the principal urinary protein measured. Changes in the pattern of urinary protein excretion are not necessarily restricted to nephrourological disease; for instance, the appearance of beta-human chorionic gonadotropin in the urine of pregnant women is the basis for all commercially available pregnancy kits. Similarly, microalbuminuria is a clinically important marker not only of early diabetic nephropathy but also of concomitant cardiovascular disease. With the emergence of newer technologies, in particular mass spectrometry, it has become possible to study urinary protein excretion in even more detail. A variety of techniques have been used both to characterize the normal complement of urinary proteins and also to identify proteins and peptides that may facilitate earlier detection of disease, improve assessment of prognosis and allow closer monitoring of response to therapy. Such proteomics-based approaches hold great promise as the basis for new diagnostic tests and as the means to better understand disease pathogenesis. In this review, we summarize the currently available methods for urinary protein analysis and describe the newer approaches being taken to identify urinary biomarkers.

Key clinical issues in renal cancer: a challenge for proteomics

Renal cancer has many clinical challenges which proteomics is ideally placed to address. The issues cover all aspects of the disease including diagnosis, prognosis, treatment selection and monitoring to detect metastatic disease. In all cases novel biomarkers would considerably help in clinical management and with the relative resistance to conventional chemotherapy and radiotherapy, a better understanding of the underlying pathogenesis may contribute to the much needed development of novel therapeutic targets and the better use of promising new anti-angiogenic treatments. This review briefly highlights some of the clinical issues and describes proteomics-based approaches generally, before focussing on reviewing the proteomic studies to date in this area.

Contribution of oncoproteomics to cancer biomarker discovery

Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.

Attenuation of PTEN increases p21 stability and cytosolic localization in kidney cancer cells: a potential mechanism of apoptosis resistance

BACKGROUND

The PTEN (Phosphatase and Tensin homolog deleted on chromosome Ten) tumor suppressor gene is frequently mutated or deleted in a wide variety of solid tumors, and these cancers are generally more aggressive and difficult to treat than those possessing wild type PTEN. While PTEN lies upstream of the phosphoinositide-3 kinase signaling pathway, the mechanisms that mediate its effects on tumor survival remain incompletely understood. Renal cell carcinoma (RCC) is associated with frequent treatment failures (approximately 90% in metastatic cases), and these tumors frequently contain PTEN abnormalities.

RESULTS

Using the ACHN cell line containing wild type PTEN, we generated a stable PTEN knockdown RCC cell line using RNA interference. We then used this PTEN knockdown cell line to show that PTEN attenuation increases resistance to cisplatin-induced apoptosis, a finding associated with increased levels of the cyclin kinase inhibitor p21. Elevated levels of p21 result from stabilization of the protein, and they are dependent on the activities of phosphoinositide-3 kinase and Akt. More specifically, the accumulation of p21 occurs preferentially in the cytosolic compartment, which likely contributes to both cell cycle progression and resistance to apoptosis.

CONCLUSION

Since p21 regulates a decision point between repair and apoptosis after DNA damage, our data suggest that p21 plays a key role in mechanisms used by PTEN-deficient tumors to escape chemotherapy. This in turn raises the possibility to use p21 attenuators as chemotherapy sensitizers, an area under active continuing investigation in our laboratories.

A comprehensive urinary metabolomic approach for identifying kidney cancerr

The diagnosis of cancer by examination of the urine has the potential to improve patient outcomes by means of earlier detection. Due to the fact that the urine contains metabolic signatures of many biochemical pathways, this biofluid is ideally suited for metabolomic analysis, especially involving diseases of the kidney and urinary system. In this pilot study, we test three independent analytical techniques for suitability for detection of renal cell carcinoma (RCC) in urine of affected patients. Hydrophilic interaction chromatography (HILIC-LC-MS), reversed-phase ultra performance liquid chromatography (RP-UPLC-MS), and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) all were used as complementary separation techniques. The combination of these techniques is best suited to cover a very large part of the urine metabolome by enabling the detection of both lipophilic and hydrophilic metabolites present therein. In this study, it is demonstrated that sample pretreatment with urease dramatically alters the metabolome composition apart from removal of urea. Two new freely available peak alignment methods, MZmine and XCMS, are used for peak detection and retention time alignment. The results are analyzed by a feature selection algorithm with subsequent univariate analysis of variance (ANOVA) and a multivariate partial least squares (PLS) approach. From more than 2000 mass spectral features detected in the urine, we identify several significant components that lead to discrimination between RCC patients and controls despite the relatively small sample size. A feature selection process condensed the significant features to less than 30 components in each of the data sets. In future work, these potential biomarkers will be further validated with a larger patient cohort. Such investigation will likely lead to clinically applicable assays for earlier diagnosis of RCC, as well as other malignancies, and thereby improved patient prognosis.