In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 3997-3997
Abstract:
The discovery of novel biomarkers is essential for the early diagnosis of malignant diseases as well as for patient stratification to improve oncological therapies. Nevertheless, most biomarkers discovered so far have not satisfied the hopes laid upon them. Following a novel strategy we have started to systematically investigate different types of cells in different functional states to create a “protein-atlas”. We believe that only if we know the “normal” proteome of a cell at different functional states we will be able to efficiently deduct specific pathological changes. The associated proteome alterations may thus qualify as biomarkers. Based on this cell centered approach we created a novel proteomics database: the Griss Proteomics Database (GPDE). The GPDE is free, open-source software available at http://gpde.sourceforge.net. The GPDE is based on a standardized data format for proteomics experiments, the Proteomics Idenitification Database (PRIDE) data format, and thus allows the integration of proteomics experiments from different research groups. Here we present a new set of tools that was specifically developed to support the identification of biomarker candidates. The main feature of these new tools is the automated interpretation of any given protein list with respect to cells already represented in the GDPE. To ensure correct comparison results protein accessions are converted to the IPI accession system, updated to the currently suggested accession and cleared of duplicate entries caused by redundant accessions. In addition, biological knowledge is integrated into the GPDE and presented to the user to aid the interpretation of the results. With this tool it is, e.g., possible to analyze the proteome profile of hepatocytes isolated from a hepatocellular carcinoma (HCC) against other types of hepatocytes represented in the GPDE. By this comparison to multiple references the function now returns three main conclusions: (1) A quality assessment regarding reference protein profiles of this cell type. A wide coverage of proteins known to be present in the investigated cell type is an indicator for the reliability of the experiment. (2) Identification of specifically expressed proteins implies the presence of functional cell states in the investigated sample. Thus, functional states such as proliferation, occurrence of apoptosis, inflammatory activation or establishment of drug resistance can immediately be identified. (3) The most likely and plausible biomarkers are promptly highlighted by the automated identification of atypical, unexpected alterations which may be specific for the investigated disease. With the GPDE any researcher gets the possibility to quickly and easily assess, interpret and further use huge amounts of complex proteomics data. Thus the often experienced gap between the abstract results of basic research and their biological relevance is maybe a little bit closed. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3997.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM10-3997
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2010
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
