Integrating Proteomic and Functional Genomic Technologies in Discovery-driven Translational Breast Cancer Research*

Julio E. Celisa,b,{ddagger}, Pavel Gromova,b, Irina Gromovaa,b, José M. A. Moreiraa,b, Teresa Cabezóna,b, Noona Ambartsumiana,c, Mariam Grigoriana,c, Eugene Lukanidina,c, Per thor Stratena,d, Per Guldberga,e, Jirina Bartkovaa,f, Jiri Barteka,f, Jiri Lukasa,f, Claudia Lukasa,f, Anne Lykkesfeldta,g, Marja Jäätteläa,h, Peter Roepstorffa,i, Lars Bolunda,j, Torben Ørntofta,k, Nils Brünnera,l, Jens Overgaarda,m, Kerstin Sandelina,n, Mogens Blichert-Tofta,o, Henning Mouridsena,p and Fritz E. Ranka,q

From a The Danish Centre for Translational Breast Cancer Research, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, b Department of Proteomics in Cancer, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, c Department of Molecular Cancer Biology, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, d Tumor Immunology Group, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, e Laboratory of Cancer Genomics, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, f Department of Cell Cycle and Cancer, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, g Department of Tumor Endocrinology, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, h Department of Apoptosis, Institute of Cancer Biology, The Danish Cancer Society, Strandboulevarden 49, DK-2100 Copenhagen, Denmark, i Department of Biochemistry and Molecular Biology, Danish Biotechnology Instrument Centre, University of Southern Denmark, Campusvej 55, DK-5230 Odense, Denmark, j Department of Human Genetics, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus, Denmark, k Department of Clinical Biochemistry, Aarhus University, Nordre Ringgade 1, DK-8000 Aarhus, Denmark, l Department of Pharmacology and Pathobiology, Royal Veterinary and Agricultural University, Rolighedsvej 30, DK-1958 Frederiksberg, Denmark, m Department of Experimental Clinical Oncology, Aarhus University Hospital, Nørrebrogade 44, DK-8000 Aarhus, Denmark, n Department of Breast and Endocrine Surgery, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark, o Danish Breast Cancer Cooperative Group, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark, p Department of Oncology, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark, and q Department of Pathology, The Centre of Diagnostic Investigations, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen, Denmark


    ABSTRACT
 TOP
 ABSTRACT
 BREAST CANCER
 IMPACT OF PROTEOMIC AND...
 DANISH CENTRE FOR TRANSLATIONAL...
 STRATEGIC ISSUES ADDRESSED IN...
 CHALLENGES POSED BY BIOPSY...
 REFERENCES
 
The application of state-of-the-art proteomics and functional genomics technologies to the study of cancer is rapidly shifting toward the analysis of clinically relevant samples derived from patients, as the ultimate aim of translational research is to bring basic discoveries closer to the bedside. Here we describe the essence of a long-term initiative undertaken by The Danish Centre for Translational Breast Cancer Research and currently underway for cancer biomarker discovery using fresh tissue biopsies and bio-fluids. The Centre is a virtual hub that brings together scientists working in various areas of basic cancer research such as cell cycle control, invasion and micro-environmental alterations, apoptosis, cell signaling, and immunology, with clinicians (oncologists, surgeons), pathologists, and epidemiologists, with the aim of understanding the molecular mechanisms underlying breast cancer progression and ultimately of improving patient survival and quality of life. The unifying concept behind our approach is the use of various experimental paradigms for the prospective analysis of clinically relevant samples obtained from the same patient, along with the systematic integration of the biological and clinical data.



    BREAST CANCER
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 ABSTRACT
 BREAST CANCER
 IMPACT OF PROTEOMIC AND...
 DANISH CENTRE FOR TRANSLATIONAL...
 STRATEGIC ISSUES ADDRESSED IN...
 CHALLENGES POSED BY BIOPSY...
 REFERENCES
 
Breast cancer is the most common malignancy among women in the Western world and constitutes 18% of all cancers in women (1). In Denmark, ~3800 women develop breast cancer per annum, and an estimated 1200 die from the disease (2).

At present, routine mammography is the most widely used tool for the early detection of breast cancer, and Service-screening programs reduce breast cancer-related mortality (3,4). To be detected, however, a tumor should be at least a few millimeters in size, a situation that potentially influences the odds of survival and cure. Parameters such as axillary lymph node status, tumor size, histological grade, and age, in combination with predictive factors such as estrogen and progesterone receptors, are currently used for selecting the appropriate systemic therapy (5).

Patients with primary breast cancer are offered a combination of treatment options such as surgery, often followed by adjuvant irradiation, chemotherapy, and/or endocrine therapy. These treatments have proven effective; however, despite adjuvant systemic therapy, ~60% of patients with lymph node-positive disease will experience a recurrence, and most of them will die from disseminated breast cancer (6). For patients with lymph node-negative disease, the 5-year recurrence rate is ~25%, suggesting that the risk of relapse and subsequent death is closely related to the stage of the disease at the time of primary surgery. A reasonable assumption would therefore be that the survival rate of breast cancer could be improved if the number of patients being diagnosed with early-stage disease, i.e. node-negative disease, was increased. In this context it would be important to develop new diagnostic tools to detect breast cancer at a very early stage, as this will provide one way to minimize disease-related mortality.

Today, adjuvant systemic therapy (chemotherapy and/or endocrine therapy) is offered to patients of different risks of recurrence and death, i.e. to a prognostically heterogeneous group with risks ranging from 10 to 80%. This group is characterized according to classical prognostic factors (nodal status (positive), size of the primary tumor (>=20 mm), malignancy grade (II-III), steroid receptor status (negative), age (<35 years)) and constitutes about 70% of all new breast cancer patients (6). It is a well-established fact that 30–40% of the expected deaths can be avoided if adjuvant systemic therapy is offered to this patient group. However, in absolute terms the mortality reduction amounts to only a few percent (i.e. from 5 to 3%) in the low-risk group and to ~25% in the high-risk group (i.e. from 80 to 50%). Thus, although adjuvant systemic therapy has led to a significant improvement of the prognosis of the breast cancer population, it also carries the significant adverse effect of overtreatment (7, 8).

It is well known from the treatment of advanced breast cancer that patients nonresponsive to one specific type of chemotherapy, or endocrine therapy, may react positively to another type of each of the two modalities, indicating that response to a specific treatment may relate to specific characteristics (predictive factors) of the tumor. Thus, there is a pressing need to develop new independent prognostic and predictive indicators or signatures in primary breast cancer to improve the selection of patients for specific, ideally tailored treatments (914 and references therein).


    IMPACT OF PROTEOMIC AND FUNCTIONAL GENOMIC TECHNOLOGIES IN TRANSLATIONAL CANCER RESEARCH
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 ABSTRACT
 BREAST CANCER
 IMPACT OF PROTEOMIC AND...
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 STRATEGIC ISSUES ADDRESSED IN...
 CHALLENGES POSED BY BIOPSY...
 REFERENCES
 
The sequencing of the human genome together with that of model organisms has paved the way to the revolution in biology and medicine that we are experiencing today. In particular, the explosive growth in the number of new and powerful technologies within proteomics and functional genomics (9, 11, 1530, and references therein), in combination with bioinformatics, promises to accelerate the application of basic discoveries into daily clinical practice (Fig. 1).



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FIG. 1. Technologies and resources available in functional genomics.

 
Cancer, being a complex multifactorial disease group that affects a significant proportion of the population worldwide, is a prime target for focused multidisciplinary efforts using these novel and powerful technologies (19, 3134). Indeed, tools for the rapid and efficient analysis of genes and their products are expected to hasten the translation of basic research findings into clinical applications by improving drug development and clinical trial methodologies, as well as by providing biomarkers for diagnosis, prognosis, early detection, and novel therapies. In particular, array and proteomic technologies are expected to play a key role in the study and treatment of human cancers as they provide invaluable resources to define and characterize regulatory and functional networks of genes and proteins within cells. In addition, proteomics provide tools to investigate the precise molecular defect(s) in cancer tissues and may help develop specific reagents to better understand different stages of the pathology. For drug discovery, proteomics provides tools for identifying new clinically relevant drug targets, as well as functional insight for drug development (3537).

Presently, the application of state-of-the-art technologies from proteomics and functional genomics to the study of cancer is rapidly shifting to the analysis of clinically relevant samples such as biopsy specimens (11, 33, 3852 and references therein) and bio-fluids (5356), as the ultimate aim of translational research is to bring basic discoveries closer to the bedside (31, 33). The complexity of the biological samples, however, is daunting and represents one of the most important hurdles we face today for implementing the new technologies. In addition, issues related to sample collection, handling, storage, and preparation are crucial and must be carefully addressed.


    DANISH CENTRE FOR TRANSLATIONAL RESEARCH IN BREAST CANCER: A COMPREHENSIVE RESEARCH PROGRAM FOR DISCOVERY-DRIVEN TRANSLATIONAL BREAST CANCER RESEARCH
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 BREAST CANCER
 IMPACT OF PROTEOMIC AND...
 DANISH CENTRE FOR TRANSLATIONAL...
 STRATEGIC ISSUES ADDRESSED IN...
 CHALLENGES POSED BY BIOPSY...
 REFERENCES
 
At the present time, no single laboratory or research institution has the critical mass or the resources necessary to take on a complex biological problem such as cancer on its own, and it is becoming increasingly clear that we must use all of the enabling technologies, resources, and expertise available in order to make a substantial impact on the disease. With these concerns in mind, the Danish Cancer Society recently catalyzed the creation of the Danish Centre for Translational Research in Breast Cancer (DCTB),1 a virtual hub that brings together scientists working in various areas of basic cancer research (cell cycle control, invasion and micro-environmental alterations, apoptosis, cell signaling, and immunology), with oncologists, surgeons, pathologists, and epidemiologists, in an integrated, mission-oriented environment. The ultimate aim is to understand the molecular mechanisms underlying the disease and to improve breast cancer patient survival and quality of life. Main long-term objectives of DCTB are:

To achieve these goals, we focus on the prospective analysis of fresh tissue biopsies obtained from the same patient (Fig. 2, A and B) using a plethora of technologies from genomics, proteomics, functional genomics, cell biology, and bioinformatics. Briefly, biopsies dissected from the same tumor, axillary nodal metastasis, or nonmalignant breast epithelium are distributed to members of DCTB who apply various experimental paradigms (Fig. 2C) in a well-defined clinical and pathological framework. In due course, these studies will be complemented by proteomic analysis of plasma obtained from the same patient. Data will be integrated and shared through a large data base supported by the Image Informatics Platform-SIMS (Scimagix, San Mateo, California), a Web-based system that allows management, mining, and integration of image data generated across experiments and research sites. For retrospective studies, the Centre has the possibility of accessing samples stored at the Danish Breast Cancer Cooperative Group tumor repository bank, which contains frozen tissue from ~10,000 breast cancer patients, for which a full clinical follow-up is available. What follows below is a brief discussion of issues and strategies related to sample collection, handling, storage, and standardization of sample preparation, in particular for gel-based proteomics, which were recently addressed in a pilot study that involved 26 high-risk2 breast cancer patients.



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FIG. 2. Mastectomy from a high-risk patient. Small biopsy specimens from nonmalignant epithelial tissue (A), tumor (A), and axillary node metastasis (B) are distributed to members of the DCTB, who applied various experimental paradigms (as exemplified in C).

 

    STRATEGIC ISSUES ADDRESSED IN THE PILOT PROJECT
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 BREAST CANCER
 IMPACT OF PROTEOMIC AND...
 DANISH CENTRE FOR TRANSLATIONAL...
 STRATEGIC ISSUES ADDRESSED IN...
 CHALLENGES POSED BY BIOPSY...
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Patient Selection—
Access to large tumors and abundance of patient-derived breast nonmalignant epithelial tissue was deemed essential, as the chosen technological approach required the application of several enabling technologies to the same tissue biopsy. Consequently, women with primary operable high-risk invasive breast cancer were selected for the study. All 26 patients had no previous surgery to the breast and did not receive preoperative treatment. They presented a unifocal tumor of an estimated size of more than 20 mm, and all patients, except one, had mastectomy including axillary dissection (Fig. 2). Tumors studied were mostly of ductal type (22 patients), but also lobular (2 patients), medullar (1 patient), and mucinous lesions (1 patient) were included in this preliminary study.

Sample Collection and Handling—
Tissue biopsies (nonmalignant epithelial tissue, tumor, and axillary nodal metastasis) were collected from the Pathology Department at Righshospitalet 20–30 min after surgery. Samples for RNA preparation were cleaned with the aid of a scalpel directly at the Pathology Department and were snap frozen in liquid nitrogen prior to transport to the Institute of Cancer Biology. Other biopsy specimens were transported on ice, the whole process taking about 15 min. Samples were dissected at the Institute of Cancer Biology, distributed to members of the DCTB, or processed directly for gel-based proteomics and immunohistochemistry (IHC). A fraction of each sample was stored as archival material to be used for future needs. In general, the collection procedure worked extremely well thanks to the efficient and rapid handling of tissue specimens by the Pathology Department.

Core Enabling Technologies—
Because biomarker discovery is a centerpiece within the program, an important issue in the planning process was to identify technologies, particularly for revealing differential gene expression, that could be applied to the analysis of complex tissue biopsies in a prospective manner. In a biomarker discovery environment, high priority lies in the analysis of proteins, as ultimately these are responsible for orchestrating most cellular functions and are the most likely to reflect changes in gene expression. Within the proteomic technologies, two-dimensional (2D) PAGE (17, 5761), often referred as gel-based proteomics, multidimensional chromatography, and protein biochips, in combination with mass spectrometry (62, 63 and references therein) are among the tools that can be used for biomarker and drug target discovery. 2D PAGE reveals global patterns of protein expression in which every single protein can be quantitated in relation to the others and is perhaps the only technology readily usable for the analysis of complex tissue biopsies in a prospective manner. Even though it suffers from several limitations (17), we choose this technique in combination with mass spectrometry, given our experience in generating 2D PAGE protein data bases of various cell types (proteomics.cancer.dk) (15, 25, 64),3 and because protein markers for different cell types are readily available. The latter facilitates the interpretation of protein profiles where more than one cell type contributes to the overall pattern and ameliorates in part the problem of tissue and cancer heterogeneity. In addition, the use of specific antibodies against a differentially expressed marker in immunofluorescence or IHC greatly facilitates validation of the results (33). 2D PAGE data bases can be readily annotated and provide a comprehensive framework in which to store information gathered using different technologies. It should be stressed that all cell types may share as many as 80–90% of the proteins detected (25, 64), a fact that will facilitate exchange of information.

Among the other core technologies, high-throughput DNA microarray-based gene expression profiling (9, 18, 19, 65) is complementary to proteomic tools and was deemed as an essential component of the arsenal of technologies that was required, as gel-based proteomics often misses the low-abundance proteins. In addition, we considered technologies for mutation analysis (PCR in combination with denaturing gradient gel electrophoresis) (66), DNA methylation analysis (melting analysis of bisulfite-treated DNA) (67), cell enrichment (laser-capture microdissection) (68), three-dimensional cell culturing (69, 70), IHC (71), as well as for the isolation of tumor-infiltrating lymphocytes and peripheral blood lymphocytes with the potential to recognize specific immune markers as well as antigens specifically expressed by breast malignant cells that could constitute putative vaccination targets (72).


    CHALLENGES POSED BY BIOPSY SPECIMENS: SAMPLE PREPARATION
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 BREAST CANCER
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A major challenge that must be faced when applying enabling technologies to the analysis of complex tissue biopsies is the highly heterogeneous nature of tissues in terms of cell types and pathology. This is particularly burdensome for protein and RNA expression profile analysis as often the cell composition of the samples as well as the underlying pathology cannot be well defined. These shortcomings do not only affect sample preparation, but also the interpretation of the results.

Sample Preparation for Gel-based Proteomics—
Technically, tissues are much more difficult to handle than cultured cell lines and therefore standardization of sample preparation procedures is mandatory before scaling-up in a long-term translational program involving hundreds of patients. Sample preparation for gel-based proteomics proved to be demanding, as it required the analysis of hundreds of 2D gels. For tumors and lymph node metastasis, care was taken to clean the biopsies from clots and other contaminant tissue, and only small pieces of tissue (a few mm3 in size) were homogenized in lysis solution with the aid of a hand glass homogenizer to maximize the amount of dissolved material. Carrier ampholytes were carefully titrated to provide the best possible resolution, and only small amounts of protein sample, determined after trial runs, were applied to the first-dimension gel in order to avoid overloading and streaking. Fig. 3 shows representative isoelectrofocussing (IEF, Fig. 3A) and nonequilibrium pH gradient electrophoresis (Fig. 3B) 2D gels of whole extracts of tumor proteins stained with silver nitrate. The quality of the separation as well as the number of proteins resolved is very similar to that achieved with cultured cells lines, although several serum proteins, in particular albumin, are still present. Similar results have been obtained in the case of lymph node metastasis. An even larger number of proteins could be detected when tumors or axillary nodal metastasis were labeled overnight with [35S] methionine as depicted in the autoradiogram shown in Fig. 3C. These radioactive gels could be subjected to phosphorimager analysis in order to derive quantitative data. Metabolic labeling, however, proved to be rather variable from tumor to tumor, and additional work will be required to standardize this procedure. Proteins could be readily identified from silver-stained gels using mass spectrometry (73) and immunoblotting (74), and both the availability of 2D PAGE data bases of various cell types (proteomics.cancer.dk) (15, 25, 64),4 as well as of specific antibodies are instrumental in interpreting the protein profiles (33).



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FIG. 3. Representative 2D PAGE gels of whole tumor extracts and of extracellular tumor proteins. A, IEF gel of whole extracts from a tumor stained with silver nitrate. B, nonequilibrium pH gradient electrophoresis gel of whole extracts from a tumor stained with silver nitrate as in A. Several proteins both in A and B have been identified by means of mass spectrometry. C, IEF gel of whole protein extract from a tumor labeled with [35S] methionine for 14 h and revealed by autoradiography. D, IEF silver-stained 2D gel of extracellular tumor proteins. In D, proteins indicated with a cross have been identified by mass spectrometry (a) and in a few cases by immunoblotting (b). Proteins indicated with red correspond to serum proteins.

 
As far as nonmalignant breast epithelia was concerned, sample preparation proved more difficult as the ratio of glands to connective tissue varied between patients, as well as between different locations within the breast of the same patient. Reasonably good protein profiles (Fig. 4B) were obtained in those cases where the ratio was favorable (Fig. 4A), although they were contaminated to some extent with connective tissue and serum proteins. The latter, however, could be readily deducted from the profiles by comparison with serum and connective tissue patterns generated during the pilot study. In addition, to address the problem of tissue heterogeneity we took a two-sided alternative approach for the enrichment of breast epithelial cells, namely laser-capture microdissection (67) and cell culturing (68, 69). For laser-capture microdissection, the number of cells required to obtain a protein profile similar to that depicted in Fig. 4A is in the order of 50,000 cells or more, a fact that hindered the use of this technology on a routine basis. Even if a smaller number of cells would be required thanks to more sensitive protein detection procedures (60), one would still need to address the problem of cellular heterogeneity, as IHC with a single antibody marker can often detect heterogeneity even in ducts that are composed of a small number of cells (Fig. 4D). We also pursued measures to establish cultures of pure cell populations using reconstituted basal membrane material for three-dimensional (Fig. 4E) and monolayer cultures (Fig. 4F) (69, 70). Application of gene expression profiling technologies and IHC to these samples should reveal how close they mimic the tissue microenvironment (75).



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FIG. 4. A, hematoxylin stained section of nonmalignant breast epithelial tissue. B, IEF 2D PAGE of silver-stained proteins from nonmalignant epithelial tissue as illustrated in A. A few proteins are indicated for reference. C, laser-capture microdissection of a duct. D, IHC of a similar duct stained with a keratin 15-peptide antibody prepared by Eurogentec. E, three-dimensional culture of nonmalignant breast epithelial tissue. F, monolayer.

 
Sample Preparation for Other Technologies—
The isolation of high-quality DNA as well as RNA from tumors and nonmalignant epithelial breast tissue proved to be the most problematic aspect of the nucleic acid-based technologies. We found, however, that published protocols could be adopted with minor adjustments (50, 66, 67).

Integration of histopathological techniques into the project posed few problems with standard laboratory protocols being mostly used (71). Two key technical aspects of sample acquisition for IHC should, however, be emphasized here. First, in order to avoid structural and/or functional alterations, samples for IHC must be placed in formalin fixative or frozen in liquid nitrogen immediately in the operating theater. Second, the size of the tissue blocks for formalin fixation should be as large as possible in two dimensions (contain the largest possible amount of representative tissue), but only 3 mm in the third dimension to allow proper penetration of the fixative. After asserting routine proficiency in the procedures involving paraffin-embedded as well as cryo-preserved tissue, only optimization of conditions for specific antibodies can present difficulties meriting some additional considerations.

Cell-based technologies (cell culturing, lymphocytes responses, etc.) require that slightly modified sampling conditions and a set of prerequisites (sterility, cell viability) is in place in order for these techniques to be successfully employed. Efforts to establish standard procedures for sample acquisition and handling are currently underway with encouraging preliminary results.

Extracellular Proteins
The search for markers for early breast cancer detection will be greatly facilitated by the systematic identification of overexpressed proteins that are secreted by tumors. Some of these will be specifically expressed by mammary tissue and may thus represent potential candidates for a rapid blood-based screening test for early detection of breast cancer. Such a systematic search will require a simple procedure for collecting reasonable levels of these proteins for profile comparisons, followed by protein identification and sequencing using mass spectrometry.

As part of the pilot project, we explored several possibilities for obtaining extracellular proteins and the simplest procedure involved placing small pieces of tumors directly in serum-free buffers overnight at 37 °C followed by centrifugation. Fig. 3D shows silver-stained IEF 2D gels of proteins recovered from one such preparation. As expected, there is some contamination with serum proteins (marked with red), but most of the proteins identified so far by mass spectrometry (indicated with blue crosses in Fig. 3D, insert a) and/or immunoblotting (Fig. 3D, insert b) are either derived from direct secretion or are present in vesicles that are shed to the extracellular fluid. Protein profiles are very similar from tumor to tumor, although interesting differences have already been observed. The procedure can also be applied to lymph node metastasis and nonmalignant epithelial tissue. Studies intended to determine the levels of some of these proteins in the serum of the same patients are currently underway. In addition, we are testing whether these sera contain antibodies against any of the extracellular proteins.

Lessons Learned from the Pilot Project and Future Perspectives
One of the major outcomes of the pilot study was the realization that we had grossly underestimated the amount of information such a small study could generate. During the initial phase we produced a very large number of images (proteomics, genomics, histology, immunohistochemistry, etc.), a fact that raised the need for providing all those that contributed to the project with integrated access to the collected information, as well as with tools to store and mine image, numerical, and textual information.

To address this problem, we are in the process of implementing the Image Informatics Platform-SIMS, in partnership with Scimagix. This image informatics infrastructure has the capability to store and integrate experimental images with annotations into a common large data base that can accommodate thousands of images a day. Using this single image data repository, which will be centrally managed, researchers at DCTB will be able to maximize data sharing and mine image data generated across experiments and research sites distributed across the virtual Centre. The systematic integration of the data will enhance our understanding of the molecular mechanisms underlying breast cancer and will lead us to systems biology. Ultimately, the information will be integrated with clinical data to generate a prognostic index and molecular profile for each patient as well as to derive markers for early detection, prognosis, and response to treatment. Targets for drug discovery and therapeutic intervention are also expected to arise from these studies. Through a strategic partnership with Scimagix it will be possible to develop the image informatics system in a way that it can be adapted to future developments

Even though the pilot study highlighted the limitations of the current technologies when applied to complex and heterogeneous tissue samples, the overall outcome was positive as it proved feasible to orchestrate and manage a multidisciplinary research environment devoted to the study of breast cancer. The DCTB has achieved sufficient critical mass of resources and expertise to attract international networking and is strategically well poised for industrial partnership. Currently, a 5-year project involving 500 high-risk patients is underway in which both prospective and retrospective studies are planned. The latter takes advantage of the Danish Breast Cancer Cooperative Group tumor repository bank, which contains frozen tissue from about 10,000 breast cancer patients, for which a full clinical follow-up is available. The program will be extended to include the systematic analysis of plasma and will embrace additional technologies, in particular for subproteome analysis as well as for the study of protein-protein interactions. These studies will be enriched by the wealth of breast cancer research data currently available in the literature.


    ACKNOWLEDGMENTS
 
We are indebted to The Danish Cancer Society for having supported the initiative to establish the Centre. We would also like to acknowledge the support and collaboration with Eurogentec and the partnership with Scimagix. We are indebted to Signe Trentemøller, Dorrit Lützhøft, Hanne Nors, Kitt Christensen, Gitte Lindberg Stott, Michael Radich Johansen, and Vibeke Ahrenkiel for expert technical assistance and Laila Fischer, Susan Nicole Fayl, Ninna Gade, and Camilla Lauritzen for planning and administration support.


    FOOTNOTES
 
Received, June 23, 2003, and in revised form, June 25, 2003.

Published, MCP Papers in Press, June 25, 2003, DOI 10.1074/mcp.R300007-MCP200

1 The abbreviations used are: DCTB, Danish Centre for Translational Research in Breast Cancer; IHC, immunohistochemistry; 2D, two-dimensional; IEF, isoelectrofocussing. Back

2 The criteria for high-risk cancer applied by DCGB are age below 35 years old, and/or tumor diameter of more than 20 mm, and/or histological malignancy 2 or 3, and/or, negative estrogen and progesterone receptor status and/or positive axillary status. Back

3 J. E. Celis, I. Gromova, and P. Gromov, unpublished observations. Back

4 J. E. Celis, I. Gromova, and P. Gromov, unpublished observations. Back

* This work was supported by the Danish Cancer Society through the budget of the Institute of Cancer Biology, and by grants from the Danish Medical Research Council and the John and Birthe Meyer Foundation. The project was approved by the Scientific and Ethical Committee of the Copenhagen and Frederiksberg Municipalities. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

{ddagger} To whom correspondence should be addressed: The Danish Centre for Translational Breast Cancer Research, Strandboulevarden 49, DK-2100 Copenhagen, Denmark. Tel.: 45-35257363; Fax: 45- 35257375; E-mail: jec{at}cancer.dk


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 BREAST CANCER
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 CHALLENGES POSED BY BIOPSY...
 REFERENCES
 

  1. Parkin, D. M. (2001) Global cancer statistics in the year 2000. Lancet Oncol. 2, 533 –543[CrossRef][Medline]

  2. Sundhedsstyrelsen (1997) Vitalstatistik, København

  3. Nystrom, L., Andersson, I., Bjurstam, N., Frisell, J., Nordenskjold, B., and Rutqvist, L. E. (2002) Long-term effects of mammography screening: updated overview of the Swedish randomised trials. Lancet 359, 909 –919[CrossRef][Medline]

  4. Lynge, E., Olsen, A. H., Fracheboud, J., and Patnick, J. (2003) Reporting of performance indicators of mammography screening in Europe. Eur. J. Cancer Prev. 12, 213 –222[CrossRef][Medline]

  5. Vainio, H., and Biamchini, F. (eds) (2002) IARC Handbooks of Cancer Prevention. Volume 7. Breast Cancer Screening. IARC Press, Lyon

  6. Goldhirsch, A., Wood, W. C., Gelber, R. D., Coates, A. S., Thurlimann, B., and Senn, H. J. (2003) Meeting highlights: Updated international expert consensus on the primary therapy of early breast cancer. J. Clin. Oncol., in press

  7. Early Breast Cancer Trialists’ Collaborative Group (1998) Polychemotherapy for early breast cancer: An overview of the randomised trials. Lancet 352, 930 –942[CrossRef][Medline]

  8. Early Breast Cancer Trialists’ Collaborative Group (1998) Polychemotherapy for early breast cancer: An overview of the randomised trials. Lancet 351, 1451 –1467[CrossRef][Medline]

  9. van de Vijver, M. J., He, Y. D., van’t Veer, L. J., Dai, H., Hart, A. A., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E. T., Friend, S. H., and Bernards, R. (2002) A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999 –2009[Abstract/Free Full Text]

  10. Krishnamurthy, S., and Sneige, N. (2002) Molecular and biological markers of premalignant lesions of human breast. Adv. Anat. Pathol. 9, 185 –197[CrossRef][Medline]

  11. Ma, X. J., Salunga, R., Tuggle, J. T., Gaudet, J., Enrigh, t E., McQuary, P., Payette, T., Pistone, M., Stecker, K., Zhang, B. M., Zhou, Y. X., Varnholt, H., Smith, B., Gadd, M., Chatfield, E., Kessler, J., Baer, T. M., Erlander, M. G., and Sgroi, D. C. (2003) Gene expression profiles of human breast cancer progression. Proc. Natl. Acad. Sci. U. S. A. 100, 5974 –5979[Abstract/Free Full Text]

  12. Ramaswamy, S., and Perou, C. M. (2003) DNA microarrays in breast cancer: the promise of personalised medicine. Lancet 361, 1576 –1577[CrossRef][Medline]

  13. Porter, D., Lahti-Domenic, i J., Keshaviah, A., Bae, Y. K., Argani, P., Marks, J., Richardson, A., Cooper, A., Strausberg, R., Riggins, G. J., Schnitt, S., Gabrielson, E., Gelman, R., and Polyak, K. (2003) Molecular markers in ductal carcinoma in situ of the breast. Mol. Cancer Res. 1, 362 –375[Abstract/Free Full Text]

  14. Schrohl, A.-A., Holten-Andersen, M., Sweep, F., Schmitt, M., Harbeck, N., Foekens, J., and N. Brünner, on behalf of the European Organisation for Research and Treatment of Cancer (EORTIC) Receptor and Biomarker Group (2003) Tumor markers: From laboratory to clinical utility. Mol. Cell. Proteomics 2, 378 –387[Free Full Text]

  15. Celis, J. E., Rasmussen, H. H., Leffers, H., Madsen, P., Honore, B., Gesser, B., Dejgaard, K., and Vandekerckhove, J. (1991) Human cellular protein patterns and their link to genome DNA sequence data: Usefulness of two-dimensional gel electrophoresis and microsequencing. FASEB J. 5, 2200 –2208[Abstract/Free Full Text]

  16. Forozan, F., Karhu, R., Kallioniemi, A., and Kallioniemi, O. P. (1997) Genome screening by comparative genomic hybridization. Trends Genet. 13, 405 –409[CrossRef][Medline]

  17. Celis J. E., and Gromov, P. (1999) 2D protein electrophoresis: Can it be perfected? Curr. Opin. Biotechnol. 10, 16 –21[CrossRef][Medline]

  18. Ferea, T. L., and Brown, P. O. (1999) Observing the living genome. Curr. Opin. Genet. Dev. 9, 715 –722[CrossRef][Medline]

  19. Celis, J. E., Kruhoffer, M., Gromova, I., Frederiksen, C., Ostergaard, M., Thykjaer, T., Gromov, P., Yu, J., Palsdottir, H., Magnusson, N., and Orntoft, T. F. (2000) Gene expression profiling: Monitoring transcription and translation products using DNA microarrays and proteomics. FEBS Lett. 480, 2 –16[CrossRef][Medline]

  20. Kallioniemi, O. P. (2001) Biochip technologies in cancer research. Ann. Med. 33, 142 –147[Medline]

  21. Pollack, J. R., Van de Rijn, M., and Botstein D. (2002) Challenging in developing a molecular characterization of cancer. Semin. Oncol. 29, 280 –285[CrossRef][Medline]

  22. Chuaqui, R. F., Bonner, R. F., Best, C. J., Gillespie, J. W., Flaig, M. J., Hewitt, S. M., Phillips, J. L., Krizman, D. B., Tangrea, M. A., Ahram, M., Linehan, W. M., Knezevic, V., and Emmert-Buck, M. R. (2002) Post-analysis follow-up and validation of microarray experiments. Nat. Genet.32 (suppl.)509 –514[CrossRef][Medline]

  23. McDonald, W. H., and Yates, J. R, 3rd (2002) Shotgun proteomics and biomarker discovery. Dis. Markers 18, 99 –105[Medline]

  24. Figeys, D. (2002) Functional proteomics: mapping protein-protein interactions and pathways. Curr. Opin. Mol Ther. 4, 210 –215[Medline]

  25. Gromov, P., Ostergaard, M., Gromova, I, and Celis, J. E. (2002) Human proteomic databases: A powerful resource for functional genomics in health and disease. Prog. Biophys. Mol. Biol. 80, 3 –22[CrossRef][Medline]

  26. MacBeath, G. (2002) Protein microarrays and proteomics. Nat. Genet.32 (suppl.) ,526 –532[CrossRef][Medline]

  27. Fung, E. T., and Enderwick, C. (2002) ProteinChip clinical proteomics: Computation challenges and solutions. BioTechniques34 (Suppl. 8) ,40 –41

  28. Valle, R. P., and Jendoubi, M. (2003) Antibody-based technologies for target discovery. Curr. Opin. Drug Discov. Devel. 6, 197 –203[Medline]

  29. Aebersold, R., and Mann, M. (2003) Mass spectrometry-based proteomics. Nature 422, 198 –207[CrossRef][Medline]

  30. Baak, J. P. A., Path, F. R. C., Hermsen, M. A. J. A., Meijer, G., Schmidt, J., and Janssen, E. A. M. (2003) Genomics and proteomics in cancer. Eur. J. Cancer 39, 1199 –1215[CrossRef][Medline]

  31. Celis, J. E. (2002) A new start in Madrid: Symposium on basic and translational cancer research. EMBO Rep. 3, 718 –723[Free Full Text]

  32. Chen, G., Gharib, T. G., Huang, C. C., Taylor, J. M., Misek, D. E., Kardia, S. L., Giordano, T. J., Iannettoni, M. D., Orringer, M. B., Hanash, S. M., and Beer, D. G. (2002) Discordant protein and mRNA expression in lung adenocarcinomas. Mol. Cell Proteomics 1, 304 –313[Abstract/Free Full Text]

  33. Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Towards an integrated approach. Cancer Cell 3, 9 –15[CrossRef][Medline]

  34. Michener, C. M., Ardekani, A. M., Petricoin, E. F. 3rd, Liotta, L. A., and Kohn, E. C. (2002) Genomics and proteomics: Application of novel technology to early detection and prevention of cancer. Cancer Detect Prev. 26, 249 –255[CrossRef][Medline]

  35. Burbaum, J., and Tobal, G. M. (2002) Proteomics in drug discovery. Curr. Opin. Chem. Biol. 6, 427 –433[CrossRef][Medline]

  36. Witzmann, F. A., and Grant, R. A. (2003) Pharmacoproteomics in drug development. Pharmacogenomics J. 3, 69 –76[CrossRef][Medline]

  37. Bleicher, K. H., Bohm, H. J., Muller, K., and Alanine, A. I. (2003) Hit and lead generation: Beyond high-throughput screening. Nat. Rev. Drug. Discov. 2, 369 –378[CrossRef][Medline]

  38. Celis, J. E., Ostergaard, M., Basse, B., Celis, A., Lauridsen, J. B., Ratz, G. P., Andersen, I., Hein, B., Wolf, H., Orntoft, T. F., Rasmussen, H. H. (1996) Loss of adipocyte-type fatty acid binding protein and other protein biomarkers is associated with progression of human bladder transitional cell carcinomas. Cancer Res. 56, 4782 –4790[Abstract]

  39. Celis, J. E., Celis, P., Ostergaard, M., Basse, B., Lauridsen, J. B., Ratz, G., Rasmussen, H. H., Orntoft, T. F., Hein, B., Wolf, H., Celis A. (1999) Proteomics and immunohistochemistry define some of the steps involved in the squamous differentiation of the bladder transitional epithelium: a novel strategy for identifying metaplastic lesions. Cancer Res. 59, 3003 –3009[Abstract/Free Full Text]

  40. Emmert-Buck, M. R., Strausberg, R. L., Krizman, D. B., Bonaldo, M. F., Bonner, R. F., Bostwick, D. G., Brown, M. R., Buetow, K. H., Chuaqui, R. F., Cole, K. A., Duray, P. H., Englert, C. R., Gillespie, J. W., Greenhut, S., Grouse, L., Hillier, L. W., Katz, K. S., Klausner, R. D., Kuznetzov, V., Lash, A. E., Lennon, G., Linehan, W. M., Liotta, L. A., Marra, M. A., Munson, P. J., Ornstein, D. K., Prabhu, V. V., Prang, C., Schuler, G. D., Soares, M. B., Tolstoshev, C. M., Vocke, C. D., and Waterston, R. H. (2000) Molecular profiling of clinical tissues specimens: feasibility and applications. J. Mol. Diagn. 2, 60 –66[Free Full Text]

  41. Wulfkuhle, J. D., McLean, K. C, Paweletz, C. P., Sgroi, D. C., Trock, B. J., Steeg, P. S., and Petricoin, E. F. 3rd. (2001) New approaches to proteomic analysis of breast cancer. Proteomics 1, 1205 –1215[CrossRef][Medline]

  42. Lawrie, L. C., Curran, S., McLeod, H. L., Fothergill, J. E., and Murray, G. I. (2001) Application of laser capture microdissection and proteomics in colon cancer. Mol. Pathol. 54, 253 –258[Abstract/Free Full Text]

  43. Alaiya, A. A., Franzen, B., Hagman, A., Dysvik, B., Roblick, U. J., Becker, S., Moberger, B., Auer, G., and Linder, S. (2002) Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles. Int. J. Cancer 98, 895 –899[CrossRef][Medline]

  44. Ahram, M., Best, C. J., Flaig, M. J., Gillespie, J. W., Leiva, I. M., Chuaqui, R. F., Zhou, G., Shu, H., Duray, P. H., Linehan, W. M., Raffeld, M., Ornstein, D. K., Zhao, Y., Petricoin, E. F. 3rd, and Emmert-Buck, M. R. (2002) Proteomic analysis of human prostate cancer. Mol. Carcinog. 33, 9 –15[CrossRef][Medline]

  45. Chen, G., Gharib, T. G., Huang, C. C., Thomas, D. G., Shedden, K. A., Taylor, J. M., Kardia, S. L., Misek, D. E., Giordano, T. J., Iannettoni, M. D., Orringer, M. B., Hanash, S. M., and Beer, D. G. (2002) Proteomic analysis of lung adenocarcinoma: identification of a highly expressed set of proteins in tumors. Clin. Cancer Res. 8, 2298 –2305[Abstract/Free Full Text]

  46. Celis, J. E., Celis, P., Palsdottir, H., Ostergaard, M., Gromov, P., Primdahl, H., Orntoft, T. F., Wolf, H., Celis, A., and Gromova, I. (2002) Proteomic strategies to reveal tumor heterogeneity among urothelial papillomas. Mol. Cell. Proteomics 1, 269 –279[Abstract/Free Full Text]

  47. Meehan, K. L., Holland, J. W., and Dawkins, H. J. (2002) Proteomic analysis of normal and malignant prostate tissue to identify novel proteins lost in cancer. Prostate 50, 54 –63[CrossRef][Medline]

  48. Orntoft, T. F., Thykjaer, T., Waldman, F. M., Wolf, H., and Celis, J. E. (2002) Genome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas. Mol. Cell. Proteomics 1, 37 –45[Abstract/Free Full Text]

  49. Symmans, W. F., Ayers, M., Clark, E. A., Stec, J., Hess, K. R., Sneige, N., Buchholz, T. A., Krishnamurthy, S., Ibrahim, N. K., Buzdar, A. U., Theriault, R. L., Rosales, M. F., Thomas, E. S., Gwyn, K. M., Green, M. C., Syed, A. R., Hortobagyi, G. N., and Pusztai L. (2003) Total RNA yield and microarray gene expression profiles from fine-needle aspiration biopsy and core-needle biopsy samples of breast carcinoma. Cancer 97, 2960 –2971[CrossRef][Medline]

  50. Dyrskjot,. L., Thykjaer, T., Kruhoffer, M., Jensen, J. L., Marcussen, N., Hamilton-Dutoit, S., Wolf, H., and Orntoft, T. F. (2003) Identifying distinct classes of bladder carcinoma using microarrays. Nat. Genet. 33, 90 –96[CrossRef][Medline]

  51. Pusztai, L., Ayers, M., Stec, J., and Hortobagyi, G. N. (2003) Clinical application of cDNA microarrays in oncology. Oncologist 8, 252 –258[Abstract/Free Full Text]

  52. Staudt, L. M. (2003) Molecular diagnosis of the hematologic cancers. N. Engl. J. Med. 348, 1777 –1785[Free Full Text]

  53. Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., and Liotta, L. A. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572 –577[CrossRef][Medline]

  54. Petricoin, E. F. 3rd, Ornstein, D. K., Paweletz, C. P., Ardekani, A., Hackett, P. S., Hitt, B. A., Velassco, A., Trucco, C., Wiegand, L., Wood, K., Simone, C. B., Levine, P. J., Linehan, W. M., Emmert-Buck, M. R., Steinberg, S. M., Kohn, E. C., and Liotta, L. A. (2002) Serum proteomic patterns for detection of prostate cancer. J. Natl. Cancer Inst. 94, 1576 –1578[Abstract/Free Full Text]

  55. Petricoin, E. F., Zoon, K. C., Kohn, E. C., Barrett, J. C., and Liotta, L. A. (2003) Clinical proteomics: translating benchside promise into bedside reality. Nat. Rev. Drug Discov. 1, 683 –695[CrossRef]

  56. Wulfkuhle, J. D., Liotta, L. A., and Petricoin, E. F. (2003) Proteomic applications for the early detection of cancer. Nat. Rev. Cancer 3, 267 –275[CrossRef][Medline]

  57. O’Farrell, P., H. (1975) High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250, 4007 –4021[Abstract]

  58. Celis, J. E., Ratz, G., Basse, B., Lauridsen, J. B., Celis, A., Jensen N. A., and Gromov, P. (1997) High resolution two-dimensional gel electrophoresis of proteins: Isoelectric focusing (IEF) and nonequilibrium pH gradient electrophoresis (NEPHGE). Applications to the cultured cells and mouse knockouts, in Cell Biology. A Laboratory Handbook, Vol. 4 (J. E. Celis, N. Carter, T. Hunter, D. Shotton, K. Simons, and J. V. Small, eds) pp.375 –386, Academic Press, San Diego

  59. Herbert, B. R., Harry, J. L., Packer, N. H., Gooley, A. A., Pedersen, S. K., and Williams, K. L. (2001) What place for polyacrylamide in proteomics? Trends Biotechnol.19 (Suppl 10) ,S3 –S9[CrossRef][Medline]

  60. Lilley, K. S., Razzaq, A., and Dupree, P. (2002) Two-dimensional gel electrophoresis: Recent advances in sample preparation, detection and quantitation. Curr. Opin. Chem. Biol. 6, 46 –50[CrossRef][Medline]

  61. Görg, A., Obermaier, C., Boguth, G., Harder, A., Scheibe, B., Wildgruber, R., and Weiss, W. (2000) The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis 21, 1037 –1053[CrossRef][Medline]

  62. Yip, T. T., and Lomas, L. (2002) SELDI ProteinChip array in oncoproteomic research. Tech. Cancer Res. Treatment 1, 273 –274

  63. Wu, C. C., and McCoss, M. J. (2002) Shotgun proteomics: Tools for the analysis of complex biological systems. Curr. Opin. Mol. Ther. 4, 242 –250[Medline]

  64. Celis, J. E., Rasmussen, H. H., Gromov, P., Olsen, E., Madsen, P., Leffers, H., Honore, B., Dejgaard, K., Vorum, H., Kristensen, D. B., Ostergaard, M., Haunso, A., Jensen, N. A., Celis, A., Basse, B., Lauridsen, J. B., Ratz, G. P., Anderson, A. H., Walbum E., Kjaergaard, I., Andersen, I., Puype M., Van Damme, J., and Vanderkerckhove J. (1995) The human keratinocyte two-dimensional gel protein database (update 1995): Mapping components of signal transduction pathways. Electrophoresis 16, 2177 –2240[Medline]

  65. Brown, P. O., and Botstein, D. (1999) Exploring the new world of the genome with DNA microarrays. Nat. Genet.21 (Suppl.)33 –37[CrossRef][Medline]

  66. Abrams, E. S., and Stanton V. P. (1992) Use of denaturing gradient gel electrophoresis to study conformational transitions in nucleic acids. Methods Enzymol. 212, 71 –104[Medline]

  67. Guldberg, P., Worms, J., and Grønbæk, K. (2002) Profiling DNA methylation by melting analysis. Methods 27, 121 –127[CrossRef][Medline]

  68. Emmert-Buck, M. R., Bonner, R. F., Smith, P. D., Chuaqui, R. F., Zhuang, Z., Goldstein, S. R., Weiss, R. A., and Liotta, L. A. (1996) Laser capture microdissection. Science 274, 998 –1001[Abstract/Free Full Text]

  69. Hammond, S. L., Ham, R. G., and Stampfe, R. M. R. (1984) Serum-free growth of human mammary epithelial cells: Rapid clonal growth in defined medium and extended serial passage with pituitary extract. Proc. Natl. Acad. Sci. U. S. A. 81, 5435 –5439[Abstract]

  70. Weaver, V. M., Fischer, A. H., Peterson, O. W., and Bissell, M. J. (1996) The importance of the microenvironment in breast cancer progression: Recapitulation of mammary tumorigenesis using a unique human mammary epithelial cell model and a three-dimensional culture assay. Biochem. Cell Biol. 74, 833 –851[Medline]

  71. Osborn, M. (1997) Immunofluorescence microscopy of cultured cells, in Cell Biology. A Laboratory Handbook, Vol. 2 (J. E. Celis, N. Carter, T. Hunter, D. Shotton, K. Simons, and J. V. Small, eds) pp.462 –468, Academic Press, San Diego

  72. Andersen, M. H., Pedersen, L. O., Capeller, B., Brocker, E. B., Becker, J. C., and thor Straten, P. (2001) Spontaneous cytotoxic T-cell responses against survivin-derived MHC class I-restricted T-cell epitopes in situ as well as ex vivo in cancer patients. Cancer Res. 61, 5964 –5968[Abstract/Free Full Text]

  73. Celis, J. E., Gromova, I., Rank, F., and Gromov, P. (2003) Proteomics in bladder cancer, in Oncogenomics: Molecular Approaches to Cancer. Life & Medical Sciences. J. Wiley & Sons, Inc., New York, in press

  74. Celis, J. E., and Gromov, P. (2000) High-resolution two-dimensional gel electrophoresis and protein identification using western blotting and ECL detection. EXS 88, 55 –67[Medline]

  75. Celis, A., Rasmussen, H. H., Celis, P., Basse, B., Lauridsen, J. B., Ratz, G., Hein, B., Østergaard, M., Wolf, H., Ørntoft, T., and Celis, J. E. (1999) Short-term culturing of low-grade superficial bladder transitional cell carcinomas leads to changes in the expression levels of several proteins involved in key cellular activities. Electrophoresis 20, 355 –361[CrossRef][Medline]