Mining protein data from two-dimensional gels: tools for systematic post-planned analyses

Molecular classification of breast carcinoma in situ

Pleomorphic variant of invasive lobular carcinoma (PILC) is an aggressive variant of invasive lobular carcinoma (ILC). Its in situ counterpart, pleomorphic lobular carcinoma in situ (PLCIS) is a recently described entity. Morphologically it has the typical architectural pattern of LCIS, but the neoplastic cells resemble intermediate grade DCIS. Molecular signatures that distinguish PLCIS from DCIS and LCIS would provide additional tools to aid in the histopathologic classification of PLCIS as a lesion distinct from LCIS and DCIS. CIS lesions, obtained from a study cohort of 38 breast cancer patients, were divided into 18 DCIS, 14 PLCIS and 6 LCIS. DNA from microdissected archival tissue was interrogated for loss or gain of 112 breast-cancer-specific genes using the Multiplex Ligation-dependent Probe Amplification Assay (MLPA). Classification Regression Tree (CART) analysis was employed to develop a gene-based molecular classification to distinguish or separate out PLCIS from DCIS and LCIS. Molecular classification via CART, based on gene copy number, agreed with histopathology in 34/38 CIS cases. Loss of CASP1 was predictive of LCIS (n=4) with one misclassified PLCIS. Gain of RELA predicted only the LCIS classification (n=2 cases). STK15 and TNFRSF1B were predictive only for DCIS with no misclassifications. Gain of EHF and TNFRSF1B and loss of NCOA3 were predictive of PLCIS, but not without misclassification. Molecular reclassification by CART was accomplished in 4 CIS cases: 1 PLCIS was reclassified as LCIS, 1 LCIS reclassified as PLCIS, and 2 DCIS cases as PLCIS. This study provides additional rationale for molecular modeling strategies in the evaluation of CIS lesions. This diagnostic aid may serve to minimize misclassification between PLCIS and DCIS, and PLCIS and LCIS, aiding to increase accuracy in the differential diagnosis of CIS lesions.

Proteomics in Early Detection of Cancer

Early detection is critical in cancer control and prevention. Biomarkers help in this process by providing valuable information about a the status of a cell at any given point in time. As a cell transforms from nondiseased to neoplastic, distinct changes occur that could be potentially detected through the identification of the appropriate biomarkers. Biomarker research has benefited from advances in technology such as proteomics. We discuss here ongoing research in this field, focusing on proteomic technologies. The advances in two-dimensional electrophoresis and mass spectrometry are discussed in light of their contribution to biomarker research. Chip-based techniques, such as surface-enhanced laser desorption, and ionization and emerging methods, such as tissue and antibody arrays, are also discussed. The development of bioinformatic tools that have and are being developed in parallel to proteomics is also addressed. This report brings into focus the efforts of the Early Detection Research Network at the National Cancer Institute in harnessing scientific expertise from leading institutions to identify and validate biomarkers for early detection and risk assessment.

2D or not 2D. Two-dimensional gel electrophoresis

2D gel electrophoresis is the technology that everyone loves to hate-it requires manual dexterity and precision to reproduce precisely and is thus not well-suited as a high-throughput technology. Although almost everyone would like to replace it, the resolution and sensitivity it offers are exquisite and unsurpassed if one wants a global view of cellular activity. There have been several recent developments, for example, the detection of low abundance proteins, and the resolution possible with narrow-range IPG gels.

The microbial proteome database--an automated laboratory catalogue for monitoring protein expression in bacteria

Laboratories devoted to high-throughput characterisation of purified proteins arrayed via two-dimensional (2-D) gel electrophoresis face an arduous task in maintaining a centralised and constantly evolving record of information relating to the characterisation of proteins and their responses following biological challenges. The Microbial Proteome Database (MPD) has been conceived as an in-house resource for complementing the plethora of genomic databases available for such organisms. The database utilises commercially available software to provide an electronic 'lab book' of information obtained daily from 2-D electrophoresis gels, image analysis packages, protein characterisation methodologies, and biological experimentation. The MPD begins from a single 2-D gel image (a 2-D 'reference map') with clickable spots that link to a 'protein catalogue' (ProtCat) with spot information including protein identity, changes in expression determined under experimental conditions, cellular location, mass, and pI. The entry for each protein then contains further links to gel images corresponding to the presence of the particular protein within different subproteomes (as defined by the pH of narrow- and wide-range immobilised pH gradients or from differential extraction methods used to determine the location of the protein within a functional cell). The database currently contains information from strains of three microbial species (Escherichia coil, Pseudomonas aeruginosa and Staphylococcus aureus) and 32 master gel images. The rapid accessibility of information obtained from microbial proteomes is an essential step towards the integrated analysis of these organisms at the gene, transcript, protein and functional levels and will aid in reducing turnaround times between sample preparation and the discovery of molecules of biological significance.

Exploratory data analysis groupware for qualitative and quantitative electrophoretic gel analysis over the Internet-WebGel

Many scientists use quantitative measurements to compare the presence and amount, of various proteins and nucleotides among series of one- and two-dimensional (1-D and 2-D) electrophoretic gels. These gels are often scanned into digital image files. Gel spots are then quantified using stand-alone analysis software. However, as more research collaborations take place over the Internet, it has become useful to share intermediate quantitative data between researchers. This allows research group members to investigate their data and share their work in progress. We developed a World Wide Web group-accessible software system, WebGel, for interactively exploring qualitative and quantitative differences between electrophoretic gels. Such Internet databases are useful for publishing quantitative data and allow other researchers to explore the data with respect to their own research. Because intermediate results of one user may be shared with their collaborators using WebGel, this form of active data-sharing constitutes a groupware method for enhancing collaborative research. Quantitative and image gel data from a stand-alone gel image processing system are copied to a database accessible on the WebGel Web server. These data are then available for analysis by the WebGel database program residing on that server. Visualization is critical for better understanding of the data. WebGel helps organize labeled gel images into montages of corresponding spots as seen in these different gels. Various views of multiple gel images, including sets of spots, normalization spots, labeled spots, segmented gels, etc. may also be displayed. These displays are active and may be used for performing database operations directly on individual protein spots by simply clicking on them. Corresponding regions between sets of gels may be visually analyzed using Flicker-comparison (Electrophoresis 1997, 18, 122-140) as one of the WebGel methods for qualitative analysis. Quantitative exploratory data analysis can be performed by comparing protein concentration values between corresponding spots for multiple samples run in separate gels. These data are then used to generate reports on statistical differences between sets of gels (e.g., between different disease states such as benign or metastatic cancers, etc.). Using combined visual and quantitative methods, WebGel can help bridge the analysis of dissimilar gels which are difficult to analyze with stand-alone systems and can serve as a collaborative Internet tool in a groupware setting.