Dataset Information

Grade-dependent proteomics characterization of kidney cancer.

ABSTRACT: 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.

SUBMITTER: Perroud B

PROVIDER: S-EPMC2689781 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Grade-dependent proteomics characterization of kidney cancer.

Perroud Bertrand B Ishimaru Tatz T Borowsky Alexander D AD Weiss Robert H RH

Molecular & cellular proteomics : MCP 20090121 5

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 acqu ...[more]

PMID: 19164279

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Project description:BackgroundThe Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging.ResultsUsing healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions.ConclusionsWe developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

Project description:BackgroundRenal cell carcinoma (RCC) is the sixth leading cause of cancer death and is responsible for 11,000 deaths per year in the US. Approximately one-third of patients present with disease which is already metastatic and for which there is currently no adequate treatment, and no biofluid screening tests exist for RCC. In this study, we have undertaken a comprehensive proteomic analysis and subsequently a pathway and network approach to identify biological processes involved in clear cell RCC (ccRCC). We have used these data to investigate urinary markers of RCC which could be applied to high-risk patients, or to those being followed for recurrence, for early diagnosis and treatment, thereby substantially reducing mortality of this disease.ResultsUsing 2-dimensional electrophoresis and mass spectrometric analysis, we identified 31 proteins which were differentially expressed with a high degree of significance in ccRCC as compared to adjacent non-malignant tissue, and we confirmed some of these by immunoblotting, immunohistochemistry, and comparison to published transcriptomic data. When evaluated by several pathway and biological process analysis programs, these proteins are demonstrated to be involved with a high degree of confidence (p values < 2.0 E-05) in glycolysis, propanoate metabolism, pyruvate metabolism, urea cycle and arginine/proline metabolism, as well as in the non-metabolic p53 and FAS pathways. In a pilot study using random urine samples from both ccRCC and control patients, we performed metabolic profiling and found that only sorbitol, a component of an alternative glycolysis pathway, is significantly elevated at 5.4-fold in RCC patients as compared to controls.ConclusionExtensive pathway and network analysis allowed for the discovery of highly significant pathways from a set of clear cell RCC samples. Knowledge of activation of these processes will lead to novel assays identifying their proteomic and/or metabolomic signatures in biofluids of patient at high risk for this disease; we provide pilot data for such a urinary bioassay. Furthermore, we demonstrate how the knowledge of networks, processes, and pathways altered in kidney cancer may be used to influence the choice of optimal therapy.

Dataset Information

Grade-dependent proteomics characterization of kidney cancer.

Publications

Grade-dependent proteomics characterization of kidney cancer.

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