Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

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

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      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.)

      Related collections

      Most cited references 58

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Mutational landscape and significance across 12 major cancer types

      The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known(forexample, mitogen-activatedprotein kinase, phosphatidylinositol-3-OH kinase,Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the numberof driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        ProteomeXchange provides globally co-ordinated proteomics data submission and dissemination

        To the Editor There is a growing trend towards public dissemination of proteomics data, which is facilitating the assessment, reuse, comparative analyses and extraction of new findings from published data 1, 2 . This process has been mainly driven by journal publication guidelines and funding agencies. However, there is a need for better integration of public repositories and coordinated sharing of all the pieces of information needed to represent a full mass spectrometry (MS)–based proteomics experiment. Your July 2009 editorial “Credit where credit is overdue” 3 exposed the situation in the proteomics field, where full data disclosure is still not common practise. Olsen and Mann 4 identified different levels of information in the typical experiment, starting from raw data and going through peptide identification and quantification, protein identifications and ratios and the resulting biological conclusions. All of these levels should be captured and properly annotated in public databases, using the existing MS proteomics repositories for the MS data (raw data, identification and quantification results) and metadata, whereas the resulting biological information should be integrated in protein knowledgebases, such as UniProt 5 . A recent editorial in Nature Methods 6 again highlighted the need for a stable repository for raw MS proteomics data. In this Correspondence, we report on the first implementation of the ProteomeXchange consortium, an integrated framework for submission and dissemination of MS-based proteomics data. Among the existing MS proteomics repositories with a broad target audience, the PRIDE (PRoteomics IDEntifications) database 7 (European Bioinformatics Institute, EBI, Cambridge, UK; http://www.ebi.ac.uk/pride) and PeptideAtlas 8 (Institute for Systems Biology, ISB, Seattle, USA; http://www.peptideatlas.org) are two of the most prominent. Both are mainly focused on tandem MS (MS/MS) data storage. Whereas PRIDE represents the information as originally analysed by the researcher (thus constituting a primary resource), data in PeptideAtlas are reprocessed through a common pipeline (the Trans-Proteomic Pipeline) to provide a uniformly analyzed view on the data with a focus on low protein false discovery rates (constituting a secondary resource). In addition, ISB has set up the first repository for SRM data, PASSEL 9 (PeptideAtlas SRM Experiment Library, http://www.peptideatlas.org/passel/). There are other resources dedicated to storing MS proteomics data, each of them with different focuses and functionalities, for instance GPMDB (where data are reprocessed using the search engine X!Tandem) 10 . At a higher abstraction level, resources like UniProt and neXtProt are integrating proteomics results into a wider context of functional annotation from many different sources, including antibody-based methods. Although most of the proteomics resources mentioned have existed for a long time, they have acted independently with limited coordination of their activities. As a result, data providers were unclear to which repository they should submit their dataset, and in what form, with choices ranging from full raw data to highly processed identifications and quantifications. In addition, no repository could store both raw data and results. Similar issues arose for data consumers, who could not always find the data supporting a protein modification in UniProt, or know whether a particular dataset from PRIDE had been integrated into PeptideAtlas. The ProteomeXchange (PX) consortium (http://www.proteomexchange.org) was formed in 2006 (ref. 11) to overcome these challenges, developing from a loose collaboration into an international consortium of major stakeholders in the domain, comprising, among others, primary (PRIDE, PASSEL) and secondary resources (PeptideAtlas, UniProt), proteomics bioinformaticians, investigators (including some involved in the HUPO Human Proteome Project), and representatives from journals regularly publishing proteomics data (Supplementary Notes, section 7). The aim of the ProteomeXchange consortium is to provide a common framework and infrastructure for the cooperation of proteomics resources by defining and implementing consistent, harmonised, user-friendly data deposition and exchange procedures among the major public proteomics repositories. ProteomeXchange provides unified data submission for multiple MS data types and delivers different ‘views’ of the deposited data, such as the raw data suitable for reprocessing, the author-generated identifications and highly filtered composite results in resources like UniProt, all linked by a universal shared identifier. Authors are able to cite the resulting ProteomeXchange accession number for datasets reported in their publications. As such, a dataset (with appropriate metadata) is becoming publishable per se and can be tracked if used by various consumers in different publications. Individual resources can join ProteomeXchange by implementing the ProteomeXchange data submission and dissemination guidelines, and metadata requirements. In the current version (http://www.proteomexchange.org/concept), the mandatory information comprises: (i) mass spectrometer output files (raw data, either in a binary format, or in a standard open format such as mzML); (ii) processed identification results (two submission modes are available, see below); and (iii) sufficient metadata to provide a suitable biological and technological background, including method information such as transition lists in the case of SRM data. Other types of information, such as peak list files (processed versions of mass spectra most often used in the identification process) and quantification results can also be provided. Two main MS proteomics workflows are now fully supported: tandem MS and SRM data (Fig. 1 and Supplementary Fig. 1). PRIDE acts as the initial submission point for MS/MS data, whereas PASSEL is the initial submission point for SRM data. It is expected that in most cases, one ProteomeXchange dataset will correspond to data from one publication, and it will be clearly linked to it. However, this concept is flexible and a mechanism for grouping different ProteomeXchange datasets is also available, for example for large-scale collaborative studies. At present, two different submission modes are available for MS/MS data: - ‘Complete submission’: this requires peptide and protein identification results to be fully supported and integrated in the receiving repository (PRIDE at present). The search engine output files (plus the associated spectra) must therefore first be converted to PRIDE XML or mzIdentML format (a process supported by several popular and user-friendly tools, Supplementary Notes, section 5). Complete submissions make the data fully available for querying, and thus maximise the potential for data re-use in MS. This in turn increases the visibility of the associated publication. A DOI (Digital Object Identifier) is assigned to each dataset, allowing formalized credit to be given to submitters and their principal investigators, through a citation index, as proposed in your editorial 3 . - ‘Partial submission’: For these submissions, peptide or protein identification results cannot be integrated in PRIDE because data converters and exporters to the supported formats are not yet available. In this case, search engine output files can be directly provided in their original format. Although partial submissions are searchable by their metadata, they are not fully searchable by results such as protein identifiers, and will not receive a DOI. However, partial submissions are important as they allow data from novel experimental approaches to be deposited into the ProteomeXchange resources, rather than having to reject these until the workflows have been mapped into a representation in PRIDE or another ProteomeXchange partner. For the submission of MS/MS datasets, a stand-alone, open-source Java tool has been made available, the ‘ProteomeXchange submission tool’ (http://www.proteomexchange.org/submission) (Supplementary Notes section 5, Supplementary Figs. 2–10). The tool allows interactive submission of small datasets as well as large- scale batch submissions. For SRM datasets, a web form (http://www.peptideatlas.org/submit) can be used for submission to PASSEL. Similar to the guidelines stated above for MS/MS datasets, PASSEL submissions require mass spectrometer output files, study metadata, peptide reagents, analysis result files and the actual SRM transition lists, the information that drives the instrument data acquisition. Once datasets are submitted, they are checked by a curator and then loaded into the main PASSEL database, which facilitates interactive exploration of the data and results. The submitted information and files can selectively be made available to journal editors and reviewers during manuscript peer review. Once the manuscript is accepted for publication or the submitter informs the receiving repository directly, the data will be publicly released (Fig. 1). At this point, the availability of the dataset, as well as basic metadata, will be disseminated through a public RSS feed (http://groups.google.com/group/proteomexchange/feed/rss_v2_0_msgs.xml). The RSS feed includes a link to an XML message (ProteomeXchange XML), which is created by the receiving repository (Supplementary Notes, section 3), and made available from ProteomeCentral, the portal for all public ProteomeXchange datasets (http://proteomecentral.proteomexchange.org) (Supplementary Notes, section 2). Repositories such as PeptideAtlas or GPMDB as well as any interested end users can subscribe to this RSS feed and trigger actions, including incorporation of the data into local resources, re-processing or biological analysis. This reprocessing is already occurring in practice. For example, two ProteomeXchange datasets (PXD000134 and PXD000157) have been used in the latest build of the human proteome in PeptideAtlas, and PXD000013 (ref. 12) was reprocessed and nominated as technical dataset of the year 2012 by GPMDB (http://www.thegpm.org/dsotw_2012.html - 201210071). ProteomeXchange started to accept regular submissions in June 2012. By the beginning of August 2013, 373 ProteomeXchange datasets have been submitted (consisting of 341 tandem MS and 32 SRM datasets, Fig. 2), a total of ~25 TB of data. The largest submission so far (currently still private) comprised 5 TB of data. For a current list of the publicly available datasets, see http://proteomecentral.proteomexchange.org/. In summary, ProteomeXchange provides an infrastructure for efficient and reliable public dissemination of proteomics data, supporting crucial validation, analysis and reuse. By providing and linking different interpretations of the data we aim to maximise dataset visibility as well as their potential benefit to different communities. Citability and traceability are addressed through the assignment of DOIs and a common identifier space. The consortium is open to the participation of additional resources (Supplementary Notes, Section 9). Although all repositories depend on continuous funding for continuous operation, the ProteomeXchange core repositories PRIDE and PeptideAtlas are well established, with first publications in 2005 (ref. 7,8), and have strong institutional backing (Supplementary Notes, section 8), ensuring that the data will remain reliably available for the foreseeable future. We are confident that the ProteomeXchange infrastructure will support the growing trend towards public availability of proteomics data, maximising its benefit to the scientific community through increased ease of access, greater ability to re-assess interpretations and extract further biological insight, and greater citation rates for the submitters. Supplementary Material 1
          Bookmark
          • Record: found
          • Abstract: found
          • Article: found

          COMPREHENSIVE MOLECULAR CHARACTERIZATION OF CLEAR CELL RENAL CELL CARCINOMA

          Genetic changes underlying clear cell renal cell carcinoma (ccRCC) include alterations in genes controlling cellular oxygen sensing (e.g. VHL) and the maintenance of chromatin states (e.g. PBRM1). We surveyed more than 400 tumors using different genomic platforms and identified 19 significantly mutated genes. The PI3K/Akt pathway was recurrently mutated, suggesting this pathway as a potential therapeutic target. Widespread DNA hypomethylation was associated with mutation of the H3K36 methyltransferase SETD2, and integrative analysis suggested that mutations involving the SWI/SNF chromatin remodeling complex (PBRM1, ARID1A, SMARCA4) could have far-reaching effects on other pathways. Aggressive cancers demonstrated evidence of a metabolic shift, involving down-regulation of genes involved in the TCA cycle, decreased AMPK and PTEN protein levels, up-regulation of the pentose phosphate pathway and the glutamine transporter genes, increased acetyl-CoA carboxylase protein, and altered promoter methylation of miR-21 and GRB10. Remodeling cellular metabolism thus constitutes a recurrent pattern in ccRCC that correlates with tumor stage and severity and offers new views on the opportunities for disease treatment.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, United States of America
            [2 ]Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia, United States of America
            [3 ]Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville, Florida, United States of America
            [4 ]Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
            [5 ]Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
            [6 ]Princess Margaret Cancer Center, Toronto, Ontario, Canada
            [7 ]INCOGEN, Inc., Williamsburg, Virginia, United States of America
            [8 ]Venebio Group, LLC, Richmond, Virginia, United States of America
            [9 ]Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, Virginia, United States of America
            University of Texas Health Science Center, UNITED STATES
            Author notes

            Competing Interests: The affiliation of authors HS and MS with INCOGEN, Inc, & Venebio Group, LLC, does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

            Conceived and designed the experiments: DM HS MS JON TK JAC RRD. Performed the experiments: CEW LAM YK AI JON TK JAC RRD. Analyzed the data: BAN AI TK DM LAM HS MS JAC RRD. Contributed reagents/materials/analysis tools: BAN AI DM HS MS JON TK JAC RRD. Wrote the paper: BAN RRD JAC AI TK LAM JON DM HS MS.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, CA USA )
            1932-6203
            29 April 2016
            2016
            : 11
            : 4
            27128972
            4851420
            10.1371/journal.pone.0154074
            PONE-D-16-08692
            (Editor)
            © 2016 Neely et al

            This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Counts
            Figures: 6, Tables: 0, Pages: 22
            Product
            Funding
            Funded by: funder-id http://dx.doi.org/10.13039/100000054, National Cancer Institute;
            Award ID: 2R44CA125807-02
            Award Recipient :
            This research was supported by the South Carolina SmartState Center of Economic Excellence in Proteomics and the MUSC Proteomics Center (BAN and RRD). TK is supported through the Canadian Research Chair program. This work was funded in part from NIH/NCI grants 2R44CA125807-02 (MS), R01CA104505 (JAC), R01CA136665 (JAC), and R01CA104505-05S1 (JAC); a generous gift from the David & Lois Stulberg Endowed Fund for Kidney Cancer Research (JAC); Mr. and Mrs. Ompal Chauhan Research Fund (JAC); Kidney Cancer Research at Mayo Clinic in Florida (JAC); Scheidel Foundation (JAC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. INCOGEN, Inc, & Venebio Group, LLC, provided support in the form of salaries for authors HS and MS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
            Categories
            Research Article
            Research and Analysis Methods
            Database and Informatics Methods
            Biological Databases
            Proteomic Databases
            Biology and Life Sciences
            Biochemistry
            Proteomics
            Proteomic Databases
            Biology and Life Sciences
            Computational Biology
            Genome Analysis
            Transcriptome Analysis
            Biology and Life Sciences
            Genetics
            Genomics
            Genome Analysis
            Transcriptome Analysis
            Biology and Life Sciences
            Biochemistry
            Proteomics
            Biology and Life Sciences
            Biochemistry
            Metabolism
            Protein Metabolism
            Biology and Life Sciences
            Genetics
            Gene Expression
            Biology and Life Sciences
            Biochemistry
            Proteomics
            Protein Abundance
            Medicine and Health Sciences
            Oncology
            Basic Cancer Research
            Metastasis
            Medicine and Health Sciences
            Oncology
            Cancers and Neoplasms
            Carcinomas
            Renal Cell Carcinoma
            Medicine and Health Sciences
            Oncology
            Cancers and Neoplasms
            Genitourinary Tract Tumors
            Renal Cell Carcinoma
            Custom metadata
            All data are fully available without restriction. Accession numbers for data deposited to MassIVE and PRIDE are available in the paper.

            Uncategorized

            Comments

            Comment on this article