Grade-dependent proteomics characterization of kidney cancer

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.

The Nephrologist's Tumor: Basic Biology and Management of Renal Cell Carcinoma

Kidney cancer, or renal cell carcinoma (RCC), is a disease of increasing incidence that is commonly seen in the general practice of nephrology. However, RCC is under-recognized by the nephrology community, such that its presence in curricula and research by this group is lacking. In the most common form of RCC, clear cell renal cell carcinoma (ccRCC), inactivation of the von Hippel-Lindau tumor suppressor is nearly universal; thus, the biology of ccRCC is characterized by activation of hypoxia-relevant pathways that lead to the associated paraneoplastic syndromes. Therefore, RCC is labeled the internist's tumor. In light of this characterization and multiple other metabolic abnormalities recently associated with ccRCC, it can now be viewed as a metabolic disease. In this review, we discuss the basic biology, pathology, and approaches for treatment of RCC. It is important to distinguish between kidney confinement and distant spread of RCC, because this difference affects diagnostic and therapeutic approaches and patient survival, and it is important to recognize the key interplay between RCC, RCC therapy, and CKD. Better understanding of all aspects of this disease will lead to optimal patient care and more recognition of an increasingly prevalent nephrologic disease, which we now appropriately label the nephrologist's tumor.

Novel drugs that target the metabolic reprogramming in renal cell cancer

Molecular profiling studies of tumor tissue from patients with clear cell renal cell cancer (ccRCC) have revealed extensive metabolic reprogramming in this disease. Associations were found between metabolic reprogramming, histopathologic Fuhrman grade, and overall survival of patients. Large-scale genomics, proteomics, and metabolomic analyses have been performed to identify the molecular players in this process. Genes involved in glycolysis, the pentose phosphate pathway, glutamine metabolism, and lipogenesis were found to be upregulated in renal cell cancer (RCC) specimens as compared to normal tissue. Preclinical research indicates that mutations in VHL, FBP1, and the PI3K-AKT-mTOR pathway drives aerobic glycolysis through transcriptional activation of the hypoxia-inducible factors (HIF). Mechanistic studies revealed glutamine as an important source for de novo fatty acid synthesis through reductive carboxylation. Amplification of MYC drives reductive carboxylation. In this review, we present a detailed overview of the metabolic changes in RCC in conjunction with potential novel therapeutics. We discuss preclinical studies that have investigated targeted agents that interfere with various aspects of tumor cell metabolism and emphasize their impact specifically on glycolysis, lipogenesis, and tumor growth. Furthermore, we describe a number of phase 1 and 2 clinical trials that have been conducted with these agents.

Formalin-Fixed, Paraffin-Embedded Tissues (FFPE) as a Robust Source for the Profiling of Native and Protease-Generated Protein Amino Termini

Dysregulated proteolysis represents a hallmark of numerous diseases. In recent years, increasing number of studies has begun looking at the protein termini in hope to unveil the physiological and pathological functions of proteases in clinical research. However, the availability of cryopreserved tissue specimens is often limited. Alternatively, formalin-fixed, paraffin-embedded (FFPE) tissues offer an invaluable resource for clinical research. Pathologically relevant tissues are often stored as FFPE, which represent the most abundant resource of archived human specimens. In this study, we established a robust workflow to investigate native and protease-generated protein N termini from FFPE specimens. We demonstrate comparable N-terminomes of cryopreserved and formalin-fixed tissue, thereby showing that formalin fixation/paraffin embedment does not proteolytically damage proteins. Accordingly, FFPE specimens are fully amenable to N-terminal analysis. Moreover, we demonstrate feasibility of FFPE-degradomics in a quantitative N-terminomic study of FFPE liver specimens from cathepsin L deficient or wild-type mice. Using a machine learning approach in combination with the previously determined cathepsin L specificity, we successfully identify a number of potential cathepsin L cleavage sites. Our study establishes FFPE specimens as a valuable alternative to cryopreserved tissues for degradomic studies.

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)

Proteomic analysis of neurons microdissected from formalin-fixed, paraffin-embedded Alzheimer's disease brain tissue

The vast majority of human tissue specimens are formalin-fixed, paraffin embedded (FFPE) archival samples, making this type of tissue a potential gold mine for medical research. It is now accepted that proteomics can be done using FFPE tissue and can generate similar results as snap-frozen tissue. However, the current methodology requires a large amount of starting protein, limiting the questions that can be answered in these types of proteomics studies and making cell-type specific proteomics studies difficult. Cell-type specific proteomics has the potential to greatly enhance understanding of cell functioning in both normal and disease states. Therefore, here we describe a new method that allows localized proteomics on individual cell populations isolated from FFPE tissue sections using laser capture microdissection. To demonstrate this technique we microdissected neurons from archived tissue blocks of the temporal cortex from patients with Alzheimer’s disease. Using this method we identified over 400 proteins in microdissected neurons; on average 78% that were neuronal and 50% that were associated with Alzheimer’s disease. Therefore, this technique is able to provide accurate and meaningful data and has great potential for any future study that wishes to perform localized proteomics using very small amounts of archived FFPE tissue.

Proteomic analysis reveals differentially secreted proteins in the urine from patients with clear cell renal cell carcinoma

OBJECTIVE

The aim of this study was to evaluate the differentially secreted protein profile in the urine from patients with clear cell renal cell carcinoma (ccRCC) using mass spectrometry-based methods. Urine composition can reflect kidney physiology and can be used to detect markers for renal diseases. Moreover, characterization of the secretome is likely to assist in the investigation of new drugs for biological targets and diagnose the ccRCC at an early stage.

METHODS AND MATERIALS

Urine samples from patients were divided according to Fuhrman degree (FI-IV), which was associated with the cellular differentiation as good prognosis (GP) and poor prognosis (PP). Healthy individuals were used as the control group (CG). We used both qualitative and quantitative mass spectrometry-based analyses that involved the following approaches: 1-dimensional gel electrophoresis combined with liquid chromatography mass spectrometry in tandem (1DE LC-MS/MS), in-solution digestion combined with label-free 1-dimensional LC-MS(E) (1D LC-MS(E)), and bidimensional gel electrophoresis combined with matrix-assisted laser desorption/ionization time of flight in tandem (2DE MALDI-TOF/TOF) or combined with LC-MS/MS.

RESULTS

All the strategies allowed the identification of 354 proteins from the CG, GP, and PP groups. Qualitative experiments using 1DE LC-MS/MS analysis detected different protein profiles, and 224 proteins were identified in all groups. The label-free MS(E) quantitative analysis identified 113 proteins and generated novel information on secreted protein profiles, including 49 up-secreted proteins in the urine from patients with ccRCC and 40 down-secreted proteins related to the CG. Proteins such as kininogen-1, uromodulin, apolipoprotein D, polyubiquitin, and CD59 glycoprotein were down secreted according to the groups CG>GP>PP. In contrast, apolipoprotein A, fibrinogen, and haptoglobin were up secreted in patient groups. The same expression profile observed for kininogen-1, apolipoprotein D, fibrinogen, and haptoglobin was corroborated by 2DE LC-MS/MS or 2DE MALDI-TOF/TOF analyses. These 2 strategies also showed 13 differentially secreted proteins among the 3 groups.

CONCLUSIONS

The proteins kininogen-1, apolipoprotein D, fibrinogen, and haptoglobin presented similar quantitative protein profiles according to MS(E) and 2DE approaches. The latter proteins were up secreted and the former ones were down-regulated. The strategies used proved to be valuable in identifying proteins that were differentially secreted in urine from patients with RCC.

Quantitative proteomic analysis of formalin-fixed, paraffin-embedded clear cell renal cell carcinoma tissue using stable isotopic dimethylation of primary amines

Background

Formalin-fixed, paraffin-embedded (FFPE) tissues represent the most abundant resource of archived human specimens in pathology. Such tissue specimens are emerging as a highly valuable resource for translational proteomic studies. In quantitative proteomic analysis, reductive di-methylation of primary amines using stable isotopic formaldehyde variants is increasingly used due to its robustness and cost-effectiveness.

Results

In the present study we show for the first time that isotopic amine dimethylation can be used in a straightforward manner for the quantitative proteomic analysis of FFPE specimens without interference from formalin employed in the FFPE process. Isotopic amine dimethylation of FFPE specimens showed equal labeling efficiency as for cryopreserved specimens. For both FFPE and cryopreserved specimens, differential labeling of identical samples yielded highly similar ratio distributions within the expected range for dimethyl labeling. In an initial application, we profiled proteome changes in clear cell renal cell carcinoma (ccRCC) FFPE tissue specimens compared to adjacent non–malignant renal tissue. Our findings highlight increased levels of glyocolytic enzymes, annexins as well as ribosomal and proteasomal proteins.

Conclusion

Our study establishes isotopic amine dimethylation as a versatile tool for quantitative proteomic analysis of FFPE specimens and underlines proteome alterations in ccRCC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1768-x) contains supplementary material, which is available to authorized users.

Grade-Dependent Metabolic Reprogramming in Kidney Cancer Revealed by Combined Proteomics and Metabolomics Analysis

Kidney cancer [or renal cell carcinoma (RCC)] is known as "the internist's tumor" because it has protean systemic manifestations, suggesting that it utilizes complex, nonphysiologic metabolic pathways. Given the increasing incidence of this cancer and its lack of effective therapeutic targets, we undertook an extensive analysis of human RCC tissue employing combined grade-dependent proteomics and metabolomics analysis to determine how metabolic reprogramming occurring in this disease allows it to escape available therapeutic approaches. After validation experiments in RCC cell lines that were wild-type or mutant for the Von Hippel-Lindau tumor suppressor, in characterizing higher-grade tumors, we found that the Warburg effect is relatively more prominent at the expense of the tricarboxylic acid cycle and oxidative metabolism in general. Further, we found that the glutamine metabolism pathway acts to inhibit reactive oxygen species, as evidenced by an upregulated glutathione pathway, whereas the β-oxidation pathway is inhibited, leading to increased fatty acylcarnitines. In support of findings from previous urine metabolomics analyses, we also documented tryptophan catabolism associated with immune suppression, which was highly represented in RCC compared with other metabolic pathways. Together, our results offer a rationale to evaluate novel antimetabolic treatment strategies being developed in other disease settings as therapeutic strategies in RCC.

Common variation at 1q24.1 (ALDH9A1) is a potential risk factor for renal cancer

So far six susceptibility loci for renal cell carcinoma (RCC) have been discovered by genome-wide association studies (GWAS). To identify additional RCC common risk loci, we performed a meta-analysis of published GWAS (totalling 2,215 cases and 8,566 controls of Western-European background) with imputation using 1000 Genomes Project and UK10K Project data as reference panels and followed up the most significant association signals [22 single nucleotide polymorphisms (SNPs) and 3 indels in eight genomic regions] in 383 cases and 2,189 controls from The Cancer Genome Atlas (TCGA). A combined analysis identified a promising susceptibility locus mapping to 1q24.1 marked by the imputed SNP rs3845536 (Pcombined =2.30x10-8). Specifically, the signal maps to intron 4 of the ALDH9A1 gene (aldehyde dehydrogenase 9 family, member A1). We further evaluated this potential signal in 2,461 cases and 5,081 controls from the International Agency for Research on Cancer (IARC) GWAS of RCC cases and controls from multiple European regions. In contrast to earlier findings no association was shown in the IARC series (P=0.94; Pcombined =2.73x10-5). While variation at 1q24.1 represents a potential risk locus for RCC, future replication analyses are required to substantiate our observation.

Functional characterization of BC039389-GATM and KLK4-KRSP1 chimeric read-through transcripts which are up-regulated in renal cell cancer

Background

Chimeric read-through RNAs are transcripts originating from two directly adjacent genes (<10 kb) on the same DNA strand. Although they are found in next-generation whole transcriptome sequencing (RNA-Seq) data on a regular basis, investigating them further has usually been refrained from. Therefore, their expression patterns or functions in general, and in oncogenesis in particular, are poorly understood.

Results

We used paired-end RNA-Seq and a specifically designed computational data analysis pipeline (FusionSeq) to nominate read-through events in a small discovery set of renal cell carcinomas (RCC) and confirmed them in a larger validation cohort.

324 read-through events were called overall; 22/27 (81%) selected nominees passed validation with conventional PCR and were sequenced at the junction region. We frequently identified various isoforms of a given read-through event. 2/22 read-throughs were up-regulated: BC039389-GATM was higher expressed in RCC compared to benign adjacent kidney; KLK4-KRSP1 was expressed in 46/169 (27%) RCCs, but rarely in normal tissue. KLK4-KRSP1 expression was associated with worse clinical outcome in the patient cohort. In cell lines, both read-throughs influenced molecular mechanisms (i.e. target gene expression or migration/invasion) in a way that counteracted the effect of the respective parent transcript GATM or KLK4.

Conclusions

Our data suggests that the up-regulation of read-through RNA chimeras in tumors is not random but causes regulatory effects on cellular mechanisms and may impact patient survival.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1446-z) contains supplementary material, which is available to authorized users.

PPARα inhibition modulates multiple reprogrammed metabolic pathways in kidney cancer and attenuates tumor growth

Kidney cancer [renal cell carcinoma (RCC)] is the sixth-most-common cancer in the United States, and its incidence is increasing. The current progression-free survival for patients with advanced RCC rarely extends beyond 1-2 yr due to the development of therapeutic resistance. We previously identified peroxisome proliferator-activating receptor-α (PPARα) as a potential therapeutic target for this disease and showed that a specific PPARα antagonist, GW6471, induced apoptosis and cell cycle arrest at G0/G1 in RCC cell lines associated with attenuation of cell cycle regulatory proteins. We now extend that work and show that PPARα inhibition attenuates components of RCC metabolic reprogramming, capitalizing on the Warburg effect. The specific PPARα inhibitor GW6471, as well as a siRNA specific to PPARα, attenuates the enhanced fatty acid oxidation and oxidative phosphorylation associated with glycolysis inhibition, and PPARα antagonism also blocks the enhanced glycolysis that has been observed in RCC cells; this effect did not occur in normal human kidney epithelial cells. Such cell type-specific inhibition of glycolysis corresponds with changes in protein levels of the oncogene c-Myc and has promising clinical implications. Furthermore, we show that treatment with GW6471 results in RCC tumor growth attenuation in a xenograft mouse model, with minimal obvious toxicity, a finding associated with the expected on-target effects on c-Myc. These studies demonstrate that several pivotal cancer-relevant metabolic pathways are inhibited by PPARα antagonism. Our data support the concept that targeting PPARα, with or without concurrent inhibition of glycolysis, is a potential novel and effective therapeutic approach for RCC that targets metabolic reprogramming in this tumor.

Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer

Background

Despite the success of tamoxifen since its introduction, about one-third of patients with estrogen (ER) and/or progesterone receptor (PgR) - positive breast cancer (BC) do not benefit from therapy. Here, we aim to identify molecular mechanisms and protein biomarkers involved in tamoxifen resistance.

Results

Using iTRAQ and Immobilized pH gradient-isoelectric focusing (IPG-IEF) mass spectrometry based proteomics we compared tumors from 12 patients with early relapses (<2 years) and 12 responsive to therapy (relapse-free > 7 years). A panel of 13 proteins (TCEAL4, AZGP1, S100A10, ALDH6A1, AHNAK, FBP1, S100A4, HSP90AB1, PDXK, GFPT1, RAB21, MX1, CAPS) from the 3101 identified proteins, potentially separate relapse from non-relapse BC patients. The proteins in the panel are involved in processes such as calcium (Ca²⁺) signaling, metabolism, epithelial mesenchymal transition (EMT), metastasis and invasion. Validation of the highest expressed proteins in the relapse group identify high tumor levels of CAPS as predictive of tamoxifen response in a patient cohort receiving tamoxifen as only adjuvant therapy.

Conclusions

This data implicate CAPS in tamoxifen resistance and as a potential predictive marker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9080-y) contains supplementary material, which is available to authorized users.

Up-regulation of CNDP2 facilitates the proliferation of colon cancer

BACKGROUND

Cytosolic nonspecific dipetidase (CN2) belongs to the family of M20 metallopeptidases. It was stated in previous articles that higher expression levels of CN2 were observed in renal cell carcinoma and breast cancer. Our study explored the correlation between CN2 and colon carcinogenesis.

METHODS

We analysed the relationship between 183 patients clinicopathological characteristics and its CN2 expression. To detect the levels of CN2 in colon cancer cell lines and colon cancer tissues by western blot. To verify cell proliferation in colon cancer cells with knockdown of CNDP2 and explore the causes of these phenomena.

RESULTS

The expression levels of CN2 in clinical colon tumors and colon cancer cell lines were significantly higher than that in normal colon mucosa and colon cell lines. The difference in CN2 levels was associated with tumor location (right- and left-sided colon cancer), but there was no significant association with age, gender, tumor size, tumor grade, tumor stage or serum carcinoembryonic antigen (CEA). Knockdown of CNDP2 inhibited cell proliferation, blocked cell cycle progression and retarded carcinogenesis in an animal model. The signaling pathway through which knockdown of CNDP2 inhibited cell proliferation and tumorigenesis involved in EGFR, cyclin B1 and cyclin E.

CONCLUSIONS

Knockdown of CNDP2 can inhibit the proliferation of colon cancer in vitro and retarded carcinogenesis in vivo.

Underexpressed CNDP2 participates in gastric cancer growth inhibition through activating the MAPK signaling pathway

Increasing evidence suggests that cytosolic non-specific dipeptidase 2 (CNDP2) appears to do more than just perform an enzymatic activity; it is functionally important in cancers as well. Here, we show that the expression of CNDP2 is commonly down-regulated in gastric cancer tissues. The ectopic expression of CNDP2 resulted in significant inhibition of cell proliferation, induction of cell apoptosis and cell cycle arrest, and suppressed gastric tumor growth in nude mice. We further revealed that the reintroduction of CNDP2 transcriptionally upregulated p38 and activated c-Jun NH2-terminal kinase (JNK), whereas the loss of CNDP2 increased the phosphorylation of extracellular signal-related kinase (ERK). These results suggest that CNDP2 acts as a functional tumor suppressor in gastric cancer via activation of the mitogen-activated protein kinase (MAPK) pathway.

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling

Background:

Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.

Methods:

Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.

Results:

A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.

Conclusions:

Our study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.

Two-dimensional gel electrophoresis of gastric tissue in an alkaline pH range

2DE in combination with MS has facilitated the discovery of several proteins with altered abundance in gastric cancer. While acidic and wide pH ranges have been widely investigated, analysis in the alkaline pH range has not been specifically performed in gastric cancer to date. In the present study, we initially optimized the 2DE in alkaline pH range (pH 7-11) for gastric tissue samples. Using a modified lysis buffer, we analyzed pooled nontumor and tumor samples for proteins with altered abundance in gastric adenocarcinoma. We successfully identified 38 silver-stained spots as 24 different proteins. Four of these were chosen for investigation with immunoblotting on individual paired samples to determine whether the changes seen in 2DE represent the overall abundance of the protein or possibly only a single form. While mitochondrial trifunctional protein (MTP) subunits were decreased in 2DE gels, immunoblotting identified their overall abundance as being differently dysregulated: in the gastric tumor samples, the MTP-α subunit was decreased, and the MTP-β subunit was increased. On the other hand, heterogenous nuclear ribonucleoprotein M and galectin-4 were increased in the gastric tumor samples in both 2DE and immunoblotting.

Proteomic studies of formalin-fixed paraffin-embedded tissues

Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent a valuable informational resource of histologically characterized specimens for proteomic studies. In this article, the authors review the advancement performed in the field of FFPE proteomics focusing on formaldehyde treatment and on strategies addressed to obtain the best recovery in the protein/peptide extraction. A variety of approaches have been used to characterize protein tissue extracts, and many efforts have been performed demonstrating the comparability between fresh/frozen and FFPE proteomes. Finally, the authors report and discuss the large numbers of works aimed at developing new strategies and sophisticated platforms in the analysis of FFPE samples to validate known potential biomarkers and to discover new ones.

Inhibition of PPARα induces cell cycle arrest and apoptosis, and synergizes with glycolysis inhibition in kidney cancer cells

Renal cell carcinoma (RCC) is the sixth most common cancer in the US. While RCC is highly metastatic, there are few therapeutics options available for patients with metastatic RCC, and progression-free survival of patients even with the newest targeted therapeutics is only up to two years. Thus, novel therapeutic targets for this disease are desperately needed. Based on our previous metabolomics studies showing alteration of peroxisome proliferator-activated receptor α (PPARα) related events in both RCC patient and xenograft mice materials, this pathway was further examined in the current study in the setting of RCC. PPARα is a nuclear receptor protein that functions as a transcription factor for genes including those encoding enzymes involved in energy metabolism; while PPARα has been reported to regulate tumor growth in several cancers, it has not been evaluated in RCC. A specific PPARα antagonist, GW6471, induced both apoptosis and cell cycle arrest at G0/G1 in VHL(+) and VHL(-) RCC cell lines (786-O and Caki-1) associated with attenuation of the cell cycle regulatory proteins c-Myc, Cyclin D1, and CDK4; this data was confirmed as specific to PPARα antagonism by siRNA methods. Interestingly, when glycolysis was blocked by several methods, the cytotoxicity of GW6471 was synergistically increased, suggesting a switch to fatty acid oxidation from glycolysis and providing an entirely novel therapeutic approach for RCC.

Analysis of the formalin-fixed paraffin-embedded tissue proteome: pitfalls, challenges, and future prospectives

Formalin-fixed paraffin-embedded (FFPE) tissues are a real treasure for retrospective analysis considering the amount of samples present in hospital archives, combined with pathological, clinical, and outcome information available for every sample. Although unlocking the proteome of these tissues is still a challenge, new approaches are being developed. In this review, we summarize the different mass spectrometry platforms that are used in human clinical studies to unravel the FFPE proteome. The different ways of extracting crosslinked proteins and the analytical strategies are pointed out. Also, the pitfalls and challenges concerning the quality of FFPE proteomic approaches are depicted. We also evaluated the potential of these analytical methods for future clinical FFPE proteomics applications.

Potential biofluid markers and treatment targets for renal cell carcinoma

Renal cell carcinoma (RCC) is the 13th most common cancer in the world and one of the few cancers for which incidence is increasing. This disease is generally asymptomatic at an early stage and is highly metastatic. Frequently discovered by physicians in the process of working up other diseases such as acute kidney injury, RCC is often discovered in an advanced form and many patients have metastases at the time of diagnosis. Given that life expectancy with currently approved therapies for metastatic RCC is approximately 1-2 years, biomarkers for RCC that will enable early detection are urgently needed. Although it is unlikely that highly sensitive and specific biomarkers will be identified in the near future that are useful for screening the general population, a noninvasive marker or set of markers could soon be used in general medicine, nephrology, and urology clinics to screen patients at increased risk of RCC. In addition to the ongoing need for RCC biomarkers, the frequent resistance reported with currently available targeted therapies makes the identification of new therapeutic targets similarly important. Many promising leads for new targeted therapies have come to light; some of these therapies are in clinical trials and others are still being evaluated in the laboratory.

Proteomic analysis of formalin-fixed paraffin-embedded renal tissue samples by label-free MS: assessment of overall technical variability and the impact of block age

PURPOSE

Protein profiling of formalin-fixed paraffin-embedded (FFPE) tissues has enormous potential for the discovery and validation of disease biomarkers. The aim of this study was to systematically characterize the effect of length of time of storage of such tissue blocks in pathology archives on the quality of data produced using label-free MS.

EXPERIMENTAL DESIGN

Normal kidney and clear cell renal cell carcinoma tissues routinely collected up to 10 years prior to analysis were profiled using LC-MS/MS and the data analyzed using MaxQuant. Protein identities and quantification data were analyzed to examine differences between tissue blocks of different ages and assess the impact of technical and biological variability.

RESULTS

An average of over 2000 proteins was seen in each sample with good reproducibility in terms of proteins identified and quantification for normal kidney tissue, with no significant effect of block age. Greater biological variability was apparent in the renal cell carcinoma tissue, possibly reflecting disease heterogeneity, but again there was good correlation between technical replicates and no significant effect of block age.

CONCLUSIONS AND CLINICAL RELEVANCE

These results indicate that archival storage time does not have a detrimental effect on protein profiling of FFPE tissues, supporting the use of such tissues in biomarker discovery studies.

Comparability of differential proteomics data generated from paired archival fresh-frozen and formalin-fixed samples by GeLC-MS/MS and spectral counting

In this study, a Veterinary Department repository composed by paired formalin-fixed paraffin-embedded (FFPE) and fresh-frozen (FrFr) sets of the same tissues, routinely archived in the typical conditions of a clinical setting, was exploited to perform a comparative evaluation of the results generated by GeLC-MS/MS (1-DE followed by in-gel digestion and LC-MS/MS) and spectral counting with the two types of archival samples. Therefore, two parallel differential proteomic studies were performed using 3 canine mammary carcinomas and 3 normal controls in a paired fashion (6 FrFr and 6 FFPE in total). As a result, the FrFr and FFPE differential proteomic datasets exhibited fair consistency in differential expression trends, according to protein molecular function, cellular localization, networks, and pathways. However, FFPE samples were globally slightly less informative, especially concerning the high-MW subproteome. As a further investigation, new insights into the molecular aspects of protein fixation and retrieval were obtained. In conclusion, archival FFPE samples can be reliably used for differential proteomics studies employing a spectral counting GeLC-MS/MS approach, although some typical biases need to be taken into account, and FrFr specimens (when available) should still be considered as the gold standard for clinical proteomics.

Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

Metabolomics is increasingly being used in cancer biology for biomarker discovery and identification of potential novel therapeutic targets. However, a systematic metabolomics study of multiple biofluids to determine their interrelationships and to describe their use as tumor proxies is lacking. Using a mouse xenograft model of kidney cancer, characterized by subcapsular implantation of Caki-1 clear cell human kidney cancer cells, we examined tissue, serum, and urine all obtained simultaneously at baseline (urine) and at, or close to, animal sacrifice (urine, tissue, and plasma). Uniform metabolomics analysis of all three "matrices" was accomplished using gas chromatography- and liquid chromatography-mass spectrometry. Of all the metabolites identified (267 in tissue, 246 in serum, and 267 in urine), 89 were detected in all 3 matrices, and the majority was altered in the same direction. Heat maps of individual metabolites showed that alterations in serum were more closely related to tissue than was urine. Two metabolites, cinnamoylglycine and nicotinamide, were concordantly and significantly (when corrected for multiple testing) altered in tissue and serum, and cysteine-glutathione disulfide showed the highest change (232.4-fold in tissue) of any metabolite. On the basis of these and other considerations, three pathways were chosen for biologic validation of the metabolomic data, resulting in potential therapeutic target identification. These data show that serum metabolomics analysis is a more accurate proxy for tissue changes than urine and that tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in renal cell carcinoma, and support the concept that PPAR-α antagonism may be a potential therapeutic approach for this disease.

Metabolomics in the study of kidney diseases

Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.

Metabolomics in the study of kidney diseases

Metabolomics--the nontargeted measurement of all metabolites produced by the body--is beginning to show promise in both biomarker discovery and, in the form of pharmacometabolomics, in aiding the choice of therapy for patients with specific diseases. In its two basic forms (pattern recognition and metabolite identification), this developing field has been used to discover potential biomarkers in several renal diseases, including acute kidney injury (attributable to a variety of causes), autosomal dominant polycystic kidney disease and kidney cancer. NMR and gas chromatography or liquid chromatography, together with mass spectrometry, are generally used to separate and identify metabolites. Many hurdles need to be overcome in this field, such as achieving consistency in collection of biofluid samples, controlling for batch effects during the analysis and applying the most appropriate statistical analysis to extract the maximum amount of biological information from the data obtained. Pathway and network analyses have both been applied to metabolomic analysis, which vastly extends its clinical relevance and effects. In addition, pharmacometabolomics analyses, in which a metabolomic signature can be associated with a given therapeutic effect, are beginning to appear in the literature, which will lead to personalized therapies. Thus, metabolomics holds promise for early diagnosis, increased choice of therapy and the identification of new metabolic pathways that could potentially be targeted in kidney disease.

Classification of unknown primary tumors with a data-driven method based on a large microarray reference database

We present a new method to analyze cancer of unknown primary origin (CUP) samples. Our method achieves good results with classification accuracy (88% leave-one-out cross validation for primary tumors from 56 categories, 78% for CUP samples), and can also be used to study CUP samples on a gene-by-gene basis. It is not tied to any a priori defined gene set as many previous methods, and is adaptable to emerging new information.

Urinary acylcarnitines are altered in human kidney cancer

Kidney cancer often diagnosed at late stages when treatment options are severely limited. Thus, greater understanding of tumor metabolism leading ultimately to novel approaches to diagnosis is needed. Our laboratory has been utilizing metabolomics to evaluate compounds appearing in kidney cancer patients' biofluids at concentrations different from control patients. Here, we collected urine samples from kidney cancer patients and analyzed them by chromatography coupled to mass spectrometry. Once normalized to control for urinary concentration, samples were analyzed by two independent laboratories. After technical validation, we now show differential urinary concentrations of several acylcarnitines as a function of both cancer status and kidney cancer grade, with most acylcarnitines being increased in the urine of cancer patients and in those patients with high cancer grades. This finding was validated in a mouse xenograft model of human kidney cancer. Biological validation shows carbon chain length-dependent effects of the acylcarnitines on cytotoxicity in vitro, and higher chain length acylcarnitines demonstrated inhibitory effects on NF-κB activation, suggesting an immune modulatory effect of these compounds. Thus, acylcarnitines in the kidney cancer urine may reflect alterations in metabolism, cell component synthesis and/or immune surveillance, and may help explain the profound chemotherapy resistance seen with this cancer. This study shows for the first time the value of a novel class of metabolites which may lead to new therapeutic approaches for cancer and may prove useful in cancer biomarker studies. Furthermore, these findings open up a new area of investigation into the metabolic basis of kidney cancer.

Urine metabolomics for kidney cancer detection and biomarker discovery

Renal cell carcinoma (RCC) is one of the few human cancers whose incidence is increasing. The disease regularly progresses asymptomatically and is frequently metastatic upon presentation, thereby necessitating the development of an early method of detection. A metabolomic approach for biomarker detection using urine as a biofluid is appropriate since the tumor is located in close proximity to the urinary space. By comparing the composition of urine from individuals with RCC to control individuals, differences in metabolite composition of this biofluid can be identified, and these data can be utilized to create a clinically applicable and, possibly, bedside assay. Recent studies have shown that sample handling and processing greatly influences the variability seen in the urinary metabolome of both cancer and control patients. Once a standard method of collection is developed, identifying metabolic derangements associated with RCC will also lead to the investigation of novel targets for therapeutic intervention. The objective of this review is to discuss existing methods for sample collection, processing, data analysis, and recent findings in this emerging field.

A comprehensive map of the human urinary proteome

The study of the human urinary proteome has the potential to offer significant insights into normal physiology as well as disease pathology. The information obtained from such studies could be applied to the diagnosis of various diseases. The high sensitivity, resolution, and mass accuracy of the latest generation of mass spectrometers provides an opportunity to accurately catalog the proteins present in human urine, including those present at low levels. To this end, we carried out a comprehensive analysis of human urinary proteome from healthy individuals using high-resolution Fourier transform mass spectrometry. Importantly, we used the Orbitrap for detecting ions in both MS (resolution 60 000) and MS/MS (resolution 15 000) modes. To increase the depth of our analysis, we characterized both unfractionated as well as lectin-enriched proteins in our experiments. In all, we identified 1,823 proteins with less than 1% false discovery rate, of which 671 proteins have not previously been reported as constituents of human urine. This data set should serve as a comprehensive reference list for future studies aimed at identification and characterization of urinary biomarkers for various diseases.

Identification and characterization of human leukocyte antigen class I ligands in renal cell carcinoma cells

The presentation of tumor antigen-derived peptides by human leukocyte antigen (HLA) class I surface antigens on tumor cells is a key prerequisite to trigger effective T-cell responses in cancer patients. Multiple complementary strategies like cDNA and serological expression cloning, reverse immunology and different 'ome'-based methods have been employed to identify potential T-cell targets. This report focuses on a ligandomic profiling approach leading to the identification of 49 naturally processed HLA class I peptide ligands presented on the cell surface of renal cell carcinoma (RCC) cells. The source proteins of the defined HLA ligands are classified according to their biological function and subcellular localization. Previously established cDNA microarray data of paired tissue specimen of RCC and renal epithelium assessed the transcriptional regulation for 28 source proteins. In addition, HLA-A2-restricted, peptide-specific T cells directed against a HLA ligand derived from sulfiredoxin-1 (SRXN1) were generated, which were able to recognize and lyse ligand-presenting target cells in a HLA class I-restricted manner. Furthermore, tumor-infiltrating T cells isolated from a RCC patient were also able to kill SRXN1 expressing tumor cells. Thus, this experimental strategy might be suited to define potential candidate biomarkers and novel targets for T-cell-based immunotherapies of this disease.

Linkage of microRNA and proteome-based profiling data sets: a perspective for the priorization of candidate biomarkers in renal cell carcinoma?

Despite recent advances in the understanding of the biology of renal cell carcinoma (RCC) and the implementation of novel targeted therapies, the overall 5 years' survival rate for RCC patients remains disappointing. Late presentation, tumor heterogeneity and in particular the lack of molecular biomarkers for early detection and classification represent major obstacles. Global, untargeted comparative analysis of RCC vs tumor adjacent renal epithelium (NN) samples by high throughput analyses both at the transcriptome and proteome level have identified signatures, which might further clarify the molecular differences of RCC subtypes and might allow the identification of suitable therapeutic targets and diagnostic/prognostic biomarkers, but none thereof has yet been implemented in routine clinical use. The increasing knowledge regarding the functional role of noncoding microRNA (miR) in physiological, developmental, and pathophysiological processes by shaping the protein expression profile might provide an important link to improve the definition of disease-relevant regulatory networks. Taking into account that miR profiling of RCC and NN provides robust signatures discriminating between malignant and normal tissues, the concept of evaluating and scoring miR/protein pairs might represent a strategy for the selection and prioritization of potential biomarkers and their translation into practical use.

Urine metabolomic analysis identifies potential biomarkers and pathogenic pathways in kidney cancer

Kidney cancer is the seventh most common cancer in the Western world, its incidence is increasing, and it is frequently metastatic at presentation, at which stage patient survival statistics are grim. In addition, there are no useful biofluid markers for this disease, such that diagnosis is dependent on imaging techniques that are not generally used for screening. In the present study, we use metabolomics techniques to identify metabolites in kidney cancer patients' urine, which appear at different levels (when normalized to account for urine volume and concentration) from the same metabolites in nonkidney cancer patients. We found that quinolinate, 4-hydroxybenzoate, and gentisate are differentially expressed at a false discovery rate of 0.26, and these metabolites are involved in common pathways of specific amino acid and energetic metabolism, consistent with high tumor protein breakdown and utilization, and the Warburg effect. When added to four different (three kidney cancer-derived and one "normal") cell lines, several of the significantly altered metabolites, quinolinate, α-ketoglutarate, and gentisate, showed increased or unchanged cell proliferation that was cell line-dependent. Further evaluation of the global metabolomics analysis, as well as confirmation of the specific potential biomarkers using a larger sample size, will lead to new avenues of kidney cancer diagnosis and therapy.

Initial development and validation of a novel extraction method for quantitative mining of the formalin-fixed, paraffin-embedded tissue proteome for biomarker investigations

Annotated formalin-fixed, paraffin-embedded (FFPE) tissue archives constitute a valuable resource for retrospective biomarker discovery. However, proteomic exploration of archival tissue is impeded by extensive formalin-induced covalent cross-linking. Robust methodology enabling proteomic profiling of archival resources is urgently needed. Recent work is beginning to support the feasibility of biomarker discovery in archival tissues, but further developments in extraction methods which are compatible with quantitative approaches are urgently needed. We report a cost-effective extraction methodology permitting quantitative proteomic analyses of small amounts of FFPE tissue for biomarker investigation. This surfactant/heat-based approach results in effective and reproducible protein extraction in FFPE tissue blocks. In combination with a liquid chromatography-mass spectrometry-based label-free quantitative proteomics methodology, the protocol enables the robust representative and quantitative analyses of the archival proteome. Preliminary validation studies in renal cancer tissues have identified typically 250-300 proteins per 500 ng of tissue with 1D LC-MS/MS with comparable extraction in FFPE and fresh frozen tissue blocks and preservation of tumor/normal differential expression patterns (205 proteins, r = 0.682; p < 10(-15)). The initial methodology presented here provides a quantitative approach for assessing the potential suitability of the vast FFPE tissue archives as an alternate resource for biomarker discovery and will allow exploration of methods to increase depth of coverage and investigate the impact of preanalytical factors.

A metabolomics approach using juvenile cystic mice to identify urinary biomarkers and altered pathways in polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease and affects 1 in 1,000 individuals. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. There is accumulating evidence suggesting that there are common cellular and molecular mechanisms responsible for cystogenesis in human and murine PKD regardless of the genes mutated, and, in the case of complex metabolomic analysis, the use of a mouse model has distinct advantages for proof of principle over a human study. Therefore, in this study we utilized a urinary metabolomics-based investigation using gas chromatography-time of flight mass spectrometry to demonstrate that the cystic mouse can be discriminated from its wild-type counterpart by urine analysis alone. At day 26 of life, before there is serological evidence of kidney dysfunction, affected mice are distinguishable by urine metabolomic analysis; this finding persists through 45 days until 64 days, at which time body weight differences confound the results. Using functional score analysis and the KEGG pathway database, we identify several biologically relevant metabolic pathways which are altered very early in this disease, the most highly represented being the purine and galactose metabolism pathways. In addition, we identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components, once extended to human disease, may prove useful as a noninvasive means of diagnosing cystic kidney diseases and to suggest novel therapeutic approaches. Thus, urine metabolomics has great diagnostic potential for cystic renal disorders and deserves further study.

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.

In vivo multiplex quantitative analysis of 3 forms of alpha melanocyte stimulating hormone in pituitary of prolyl endopeptidase deficient mice

Background

In vitro reactions are useful to identify putative enzyme substrates, but in vivo validation is required to identify actual enzyme substrates that have biological meaning. To investigate in vivo effects of prolyl endopeptidase (PREP), a serine protease, on alpha melanocyte stimulating hormone (α-MSH), we developed a new mass spectrometry based technique to quantitate, in multiplex, the various forms of α-MSH.

Methods

Using Multiple Reaction Monitoring (MRM), we analyzed peptide transitions to quantify three different forms of α-MSH. Transitions were first confirmed using standard peptides. Samples were then analyzed by mass spectrometry using a triple quadrupole mass spectrometer, after elution from a reverse phase C18 column by a gradient of acetonitrile.

Results

We first demonstrate in vitro that PREP digests biological active alpha melanocyte stimulating hormone (α-MSH_1–13), by cleaving the terminal amidated valine and releasing a truncated alpha melanocyte stimulating hormone (α-MSH_1–12) product – the 12 residues α-MSH form. We then use the technique in vivo to analyze the MRM transitions of the three different forms of α-MSH: the deacetylated α-MSH_1–13, the acetylated α-MSH_1–13 and the truncated form α-MSH_1–12. For this experiment, we used a mouse model (PREP-GT) in which the serine protease, prolyl endopeptidase, is deficient due to a genetrap insertion. Here we report that the ratio between acetylated α-MSH_1–13 and α-MSH_1–12 is significantly increased (P-value = 0.015, N = 6) in the pituitaries of PREP-GT mice when compared to wild type littermates. In addition no significant changes were revealed in the relative level of α-MSH_1–13 versus the deacetylated α-MSH_1–13. These results combined with the demonstration that PREP digests α-MSH_1–13 in vitro, strongly suggest that α-MSH_1–13 is an in vivo substrate of PREP.

Conclusion

The multiplex targeted quantitative peptidomics technique we present in this study will be decidedly useful to monitor several neuropeptide enzymatic reactions in vivo under varying conditions.