Integrating proteomic and functional genomic technologies in discovery-driven translational breast cancer research

Big Data: the challenge for small research groups in the era of cancer genomics

In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.

Proteomic profiling of triple-negative breast carcinomas in combination with a three-tier orthogonal technology approach identifies Mage-A4 as potential therapeutic target in estrogen receptor negative breast cancer

Breast cancer is a very heterogeneous disease, encompassing several intrinsic subtypes with various morphological and molecular features, natural history and response to therapy. Currently, molecular targeted therapies are available for estrogen receptor (ER)(-) and human epidermal growth factor receptor 2 (Her2)-positive breast tumors. However, a significant proportion of primary breast cancers are negative for ER, progesterone receptor (PgR), and Her2, comprising the triple negative breast cancer (TNBC) group. Women with TNBC have a poor prognosis because of the aggressive nature of these tumors and current lack of suitable targeted therapies. As a consequence, the identification of novel relevant protein targets for this group of patients is of great importance. Using a systematic two dimensional (2D) gel-based proteomic profiling strategy, applied to the analysis of fresh TNBC tissue biopsies, in combination with a three-tier orthogonal technology (two dimensional PAGE/silver staining coupled with MS, two dimensional Western blotting, and immunohistochemistry) approach, we aimed to identify targetable protein markers that were present in a significant fraction of samples and that could define therapy-amenable sub-groups of TNBCs. We present here our results, including a large cumulative database of proteins based on the analysis of 78 TNBCs, and the identification and validation of one specific protein, Mage-A4, which was expressed in a significant fraction of TNBC and Her2-positive/ER negative lesions. The high level expression of Mage-A4 in the tumors studied allowed the detection of the protein in the tumor interstitial fluids as well as in sera. The existence of immunotherapeutics approaches specifically targeting this protein, or Mage-A protein family members, and the fact that we were able to detect its presence in serum suggest novel management options for TNBC and human epidermal growth factor receptor 2 positive/estrogen receptor negative patients bearing Mage-A4 positive tumors.

Immunoexpression analysis and prognostic value of BLCAP in breast cancer

Bladder Cancer Associated Protein (BLCAP, formerly Bc10), was identified by our laboratory as being down-regulated in bladder cancer with progression. BLCAP is ubiquitously expressed in different tissues, and several studies have found differential expression of BLCAP in various cancer types, such as cervical and renal cancer, as well as human tongue carcinoma and osteosarcoma. Here we report the first study of the expression patterns of BLCAP in breast tissue. We analyzed by immunohistochemistry tissue sections of normal and malignant specimens collected from 123 clinical high-risk breast cancer patients within the Danish Center for Translational Breast Cancer Research (DCTB) prospective study dataset. The staining pattern, the distribution of the immunostaining, and its intensity were studied in detail. We observed weak immunoreactivity for BLCAP in mammary epithelial cells, almost exclusively localizing to the cytoplasm and found that levels of expression of BLCAP were generally higher in malignant cells as compared to normal cells. Quantitative IHC analysis of BLCAP expression in breast tissues confirmed this differential BLCAP expression in tumor cells, and we could establish, in a 62-patient sample matched cohort, that immunostaining intensity for BLCAP was increased in tumors relative to normal tissue, in more than 45% of the cases examined, indicating that BLCAP may be of value as a marker for breast cancer. We also analyzed BLCAP expression and prognostic value using a set of tissue microarrays comprising an independent cohort of 2,197 breast cancer patients for which we had follow-up clinical information.

Microarray data analysis: comparing two population means

Scientists employing microarray profiling technology to compare sample sets generate data for a large number of endpoints. Assuming the experimental design minimized sources of bias, and the analytical technology was reliable, precise, and accurate, how does the experimentalist determine which endpoints are meaningfully different between the groups? Comparison of two population means for individual analysis measurements is the most common statistical problem associated with microarray data analysis. This chapter focuses on the hands-on procedures using SAS software to describe how to choose statistical methods to find the statistically significantly different endpoints between two groups of data generated from reverse phase protein microarrays. The four methods outlined are: (a) two-sample t-test, (b) Wilcoxon rank sum test, (c) one-sample t-test, and (d) Wilcoxon signed rank test. Two sample t-test is used for two independently normally distributed groups. One-sample t-test is used for a normally distributed difference of paired observations. Wilcoxon rank sum test is considered a nonparametric version of the two-sample t-test, and Wilcoxon signed rank test is considered a nonparametric version of the one-sample t-test.

Human cancer classification: a systems biology- based model integrating morphology, cancer stem cells, proteomics, and genomics

Human cancer classification is currently based on the idea of cell of origin, light and electron microscopic attributes of the cancer. What is not yet integrated into cancer classification are the functional attributes of these cancer cells. Recent innovative techniques in biology have provided a wealth of information on the genomic, transcriptomic and proteomic changes in cancer cells. The emergence of the concept of cancer stem cells needs to be included in a classification model to capture the known attributes of cancer stem cells and their potential contribution to treatment response, and metastases. The integrated model of cancer classification presented here incorporates all morphology, cancer stem cell contributions, genetic, and functional attributes of cancer. Integrated cancer classification models could eliminate the unclassifiable cancers as used in current classifications. Future cancer treatment may be advanced by using an integrated model of cancer classification.

Quantitative proteomic profiling of matched normal and tumor breast tissues

Proteomic analysis of breast cancer tissue has proven difficult due to its inherent histological complexity. This pilot study presents preliminary evidence for the ability to differentiate adenoma and invasive carcinoma by measuring changes in proteomic profile of matched normal and disease tissues. A dual lysis buffer method was used to maximize protein extraction from each biopsy, proteins digested with trypsin, and the resulting peptides iTRAQ labeled. After combining, the peptide mixtures they were separated using preparative IEF followed by RP nanoHPLC. Following MALDI MS/MS and database searching, identified proteins were combined into a nonredundant list of 481 proteins with associated normal/tumor iTRAQ ratios for each patient. Proteins were categorized by location as blood, extracellular, and cellular, and the iTRAQ ratios were normalized to enable comparison between patients. Of those proteins significantly changed (upper or lower quartile) between matched normal and disease tissues, those from two invasive carcinoma patients had >50% in common with each other but <22% in common with an adenoma patient. In invasive carcinoma patients, several cellular and extracellular proteins that were significantly increased (Periostin, Small breast epithelial mucin) or decreased (Kinectin) have previously been associated with breast cancer, thereby supporting this approach for a larger disease-stage characterization effort.

Proteomic profiling of mammary carcinomas identifies C7orf24, a gamma-glutamyl cyclotransferase, as a potential cancer biomarker

Breast cancer is the leading cause of cancer deaths in women today and is the most common cancer (excluding skin cancers) among women in the Western world. Although cancers detected by screening mammography are significantly smaller than nonscreening ones, noninvasive biomarkers for detection of breast cancer as early as possible are an urgent need as the risk of recurrence and subsequent death is closely related to the stage of the disease at the time of primary surgery. A set of 123 primary breast tumors and matched normal tissue was analyzed by two-dimensional (2D) gel electrophoresis, and a novel protein, C7orf24, was identified as being upregulated in cancer cells. Protein expression levels of C7orf24 were evaluated by immunohistochemical assays to qualify deregulation of this protein. Analysis of C7orf24 expression showed up-regulation in 36.4 and 23.4% of cases present in the discovery sample set (123 samples) and in an independent large TMA validation data set (2197 samples) of clinically annotated breast cancer specimens, respectively. Survival analysis showed that C7orf24 overexpression defines a subgroup of breast tumors with poor clinical outcome. Up-regulation of C7orf24 was also found in other cancer types. Four of these were investigated in greater detail, and we found that a proportion of tumors (58% in cervical, 38% in lung, 72% in colon, and 46% in breast cancer) expressed C7orf24 at levels exceeding those seen in normal samples. The observed overexpression of this protein in different types of cancer suggests deregulation of C7orf24 to be a general event in epithelial carcinogenesis, indicating that this protein may play an important role in cancer cell biology and thus constitute a novel therapeutic target. Furthermore, as C7orf24 is externalized to the tissue extracellular fluid and can be detected in serum, this protein also represents a potential serological marker.

Tissue proteomics of the human mammary gland: towards an abridged definition of the molecular phenotypes underlying epithelial normalcy

Our limited understanding of the biological impact of the whole spectrum of early breast lesions together with a lack of accurate molecular-based risk criteria for the diagnosis and assignment of prognostic significance to biopsy findings presents an important problem in the clinical management of patients harboring precancerous breast lesions. As a result, there is a need to identify biomarkers that can better determine the outcome of early breast lesions by identifying subpopulations of cells in breast premalignant disease that are at high-risk of progression to invasive disease. A first step towards achieving this goal will be to define the molecular phenotypes of the various cell types and precursors - generated by the stem cell hierarchy - that are present in normal and benign conditions of the breast. To date there have been very few systematic proteomic studies aimed at characterizing the phenotypes of the different cell subpopulations present in normal human mammary tissue, partly due to the formidable heterogeneity of mammary tissue, but also due to limitations of the current proteomic technologies. Work in our laboratories has attempted to address in a systematic fashion some of these limitations and here we present our efforts to search for biomarkers using normal fresh tissue from non-neoplastic breast samples. From the data generated by the 2D gel-based proteomic profiling we were able to compile a protein database of normal human breast epithelial tissue that was used to support the biomarker discovery program. We review and present new data on the putative cell-progenitor marker cytokeratin 15 (CK15), and describe a novel marker, dihydropyriminidase-related protein 3 (DRP3) that in combination with CK15 and other well known proteins were used to define molecular phenotypes of normal human breast epithelial cells and their progenitors in resting acini, lactating alveoli, and large collecting ducts of the nipple. Preliminary results are also presented concerning DRP3 positive usual ductal hyperplasias (UDHs) and on single cell layer columnar cells (CCCs). At least two bona fide biomarkers of undifferentiated ERα/PgR negative luminal cells emerged from these studies, CK15 and c-KIT, which in combination with transformation markers may lead to the establishment of a protein signature able to identify breast precancerous at risk of progressing to invasive disease.

Uncovering packaging features of co-regulated modules based on human protein interaction and transcriptional regulatory networks

Background

Network co-regulated modules are believed to have the functionality of packaging multiple biological entities, and can thus be assumed to coordinate many biological functions in their network neighbouring regions.

Results

Here, we weighted edges of a human protein interaction network and a transcriptional regulatory network to construct an integrated network, and introduce a probabilistic model and a bipartite graph framework to exploit human co-regulated modules and uncover their specific features in packaging different biological entities (genes, protein complexes or metabolic pathways). Finally, we identified 96 human co-regulated modules based on this method, and evaluate its effectiveness by comparing it with four other methods.

Conclusions

Dysfunctions in co-regulated interactions often occur in the development of cancer. Therefore, we focussed on an example co-regulated module and found that it could integrate a number of cancer-related genes. This was extended to causal dysfunctions of some complexes maintained by several physically interacting proteins, thus coordinating several metabolic pathways that directly underlie cancer.

Quantitative proteomic analysis of formalin-fixed and paraffin-embedded nasopharyngeal carcinoma using iTRAQ labeling, two-dimensional liquid chromatography, and tandem mass spectrometry

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens represent a potentially valuable resource for protein biomarker investigations. In this study, proteins were extracted by a heat-induced antigen retrieval technique combined with a retrieval solution containing 2% SDS from FFPE tissues of normal nasopharyngeal epithelial tissues (NNET) and three histological types of nasopharyngeal carcinoma (NPC) with diverse differentiation degrees. Then two-dimensional liquid chromatography-tandem mass spectrometry coupled with isobaric tags for relative and absolute quantification (iTRAQ) labeling was employed to quantitatively identify the differentially expressed proteins among the types of NPC FFPE tissues. Our study resulted in the identification of 730 unique proteins, the distributions of subcellular localizations and molecular functions of which were similar to those of the proteomic database of human NPC and NNET that we had set up based on the frozen tissues. Additionally, the relative expression levels of cathepsin D, keratin8, SFN, and stathmin1 identified and quantified in this report were consistent with the immunohistochemistry results acquired in our previous study. In conclusion, we have developed an effective approach to identifying protein changes in FFPE NPC tissues utilizing iTRAQ technology in conjunction with an economical and easily accessible sample preparation method.

DNA copy number aberrations in breast cancer by array comparative genomic hybridization

Array comparative genomic hybridization (CGH) has been popularly used for analyzing DNA copy number variations in diseases like cancer. In this study, we investigated 82 sporadic samples from 49 breast cancer patients using 1-Mb resolution bacterial artificial chromosome CGH arrays. A number of highly frequent genomic aberrations were discovered, which may act as “drivers” of tumor progression. Meanwhile, the genomic profiles of four “normal” breast tissue samples taken at least 2 cm away from the primary tumor sites were also found to have some genomic aberrations that recurred with high frequency in the primary tumors, which may have important implications for clinical therapy. Additionally, we performed class comparison and class prediction for various clinicopathological parameters, and a list of characteristic genomic aberrations associated with different clinicopathological phenotypes was compiled. Our study provides clues for further investigations of the underlying mechanisms of breast carcinogenesis.

Omics-based profiling of carcinoma of the breast and matched regional lymph node metastasis

Axillary lymph node (ALN) status is currently used as an important clinical indicator of breast cancer prognosis. However, the molecular mechanisms underlying lymph node metastasis are poorly understood and the relationship between ALN metastasis and the primary tumor remains unclear. In an effort to reveal structural changes in the genome and related protein responses that may drive regional metastatic progression we have analyzed matched pairs of primary breast tumors and ALN metastases both at the genomic and proteomic levels using comparative genomic hybridization (CGH) array, quantitative high-resolution 2-D PAGE in combination with MS, and immunohistochemistry (IHC). Array CGH revealed a remarkable similarity in genomic aberration profiles between the matched primary tumors and the ALN metastases. Quantitative profiling of 135 known proteins also revealed striking similarities in their overall expression patterns, although we observed distinct changes in the levels of individual proteins in some sample pairs. The remarkable similarities of the overall genomic and proteomic profiles between primary tumors and matched ALN metastases are taken to suggest that, in general, key biological characteristics of the primary breast tumor are maintained in the corresponding lymph node metastases. Given that the omics-based technologies are oblivious to changes that only occur in minor cellular subsets we validated the proteomic data using IHC, which provides protein expression information with a valuable topological component. Besides confirming the omics-derived data, the IHC analysis revealed that in two cases the ALN metastases may have been derived from a distinct minor cell subpopulation present in the primary tumor rather than from the bulk of it.

A single lysis solution for the analysis of tissue samples by different proteomic technologies

Cancer, being a major healthcare concern worldwide, is one of the main targets for the application of emerging proteomic technologies and these tools promise to revolutionize the way cancer will be diagnosed and treated in the near future. Today, as a result of the unprecedented advances that have taken place in molecular biology, cell biology and genomics there is a pressing need to accelerate the translation of basic discoveries into clinical applications. This need, compounded by mounting evidence that cellular model systems are unable to fully recapitulate all biological aspects of human dissease, is driving scientists to increasingly use clinically relevant samples for biomarker and target discovery. Tissues are heterogeneous and as a result optimization of sample preparation is critical for generating accurate, representative, and highly reproducible quantitative data. Although a large number of protocols for preparation of tissue lysates has been published, so far no single recipe is able to provide a "one-size fits all" solubilization procedure that can be used to analyse the same lysate using different proteomics technologies. Here we present evidence showing that cell lysis buffer 1 (CLB1), a lysis solution commercialized by Zeptosens [a division of Bayer (Schweiz) AG], provides excellent sample solubilization and very high 2D PAGE protein resolution both when using carrier ampholytes and immobilized pH gradient strips. Moreover, this buffer can also be used for array-based proteomics (reverse-phase lysate arrays or direct antibody arrays), allowing the direct comparison of qualitative and quantitative data yielded by these technologies when applied to the same samples. The usefulness of the CLB1 solution for gel-based proteomics was further established by 2D PAGE analysis of a number of technically demanding specimens such as breast carcinoma core needle biopsies and problematic tissues such as brain cortex, cerebellum, skeletal muscle, kidney cortex and tongue. This solution when combined with a specific sample preparation technique - cryostat sectioning of frozen specimens - simplifies tissue sample preparation and solves most of the difficulties associated with the integration of data generated by different proteomic technologies.

Proteomic strategies in bladder cancer: From tissue to fluid and back

We have applied protein expression profiling technologies in combination with immunohistochemistry, using fresh tissue and urine samples, to assess bladder cancer heterogeneity and prognosis as well as to generate protein markers for tumor progression and early diagnosis of the disease. Here, we review some selected lines of investigation and approaches undertaken by our laboratory, drawing on more than 15 years of experience in bladder cancer proteomics, to highlight a number of issues that may be useful for researchers entering the field. In particular, we address the identification of markers for bladder cancer progression and exemplify the potential of gel-based proteomic profiling of urine samples for the early detection of urothelial carcinomas. In addition, we provide a brief description of a novel and highly promising source of biomarkers, the tumor interstitial fluid (TIF) that perfuses the tumor microenvironment.

Expression of S100A4 by a variety of cell types present in the tumor microenvironment of human breast cancer

The S100A4 protein, which is involved in the metastasis process, is a member of the S100 superfamily of Ca-binding proteins. Members of this family are multifunctional signaling proteins with dual extra and intracellular functions involved in the regulation of diverse cellular processes. Several studies have established a correlation between S100A4 protein expression and worse prognosis for patients with various malignancies including breast cancer. In this article, we have used specific antibodies in combination with immunohistochemistry (IHC) to identify the cell types that express S100A4 in human breast cancer biopsies obtained from high-risk patients. IHC analysis of 68 tumor biopsies showed that the protein is expressed preferentially by various cell types present in the tumor microenvironment (macrophages, fibroblasts, activated lymphocytes), rather than by the tumor cells themselves. Moreover, we show that the protein is externalized by the stroma cells to the fluid that bathes the tumor microenvironment, where it is found in several forms that most likely correspond to charge variants. Using a specific ELISA test, we detected a significant higher concentration of S100A4 in the tumor interstitial fluid (TIF) as compared to their corresponding normal counterparts (NIF).

The needle in the haystack: application of breast fine-needle aspirate samples to quantitative protein microarray technology

BACKGROUND

There is an unmet clinical need for economic, minimally invasive procedures that use a limited number of cells for the molecular profiling of tumors in individual patients. Reverse-phase protein microarray (RPPM) technology has been applied successfully to the quantitative analysis of breast, ovarian, prostate, and colorectal cancers using frozen surgical specimens.

METHODS

For this report, the authors investigated the novel use of RPPM technology for the analysis of both archival cytology aspirate smears and frozen fine-needle aspiration (FNA) samples. RPPMs were printed with 63 breast FNA samples that were obtained before, during, and after treatment from 21 patients who were enrolled in a Phase II trial of neoadjuvant capecitabine and docetaxel therapy for breast cancer.

RESULTS

Based on an MCF7 cell line model of breast adenocarcinoma, the sensitivity of the RPPM detection method was in the femtomolar range with a coefficient of variance <13.5% for the most dilute sample. Assay linearity was noted from 1.0 microg/microL to 7.8 ng/microL total protein/array spot (R(2) = 0.9887) for a membrane receptor protein (epidermal growth factor receptor; R(2) = 0.9935).

CONCLUSIONS

The results from this study indicated that low-abundance analytes and phosphorylated and nonphosphorylated proteins in specimens that consist of a few thousand cells obtained through FNA can be quantified with RPPM technology. The ability to monitor the in vivo state of cell-signaling proteins before and after treatment potentially will augment the ability to design individualized therapy regimens through the mapping of aberrant cell-signaling phenotypes. The mapping of these protein pathways will further the development of rational drug targets.

Characterization of breast precancerous lesions and myoepithelial hyperplasia in sclerosing adenosis with apocrine metaplasia

The identification as well as the molecular characterization of breast precancerous lesions in terms of increased risk of progression and/or recurrence is becoming a critical issue today as improved non-surgical procedures are detecting cancer at an earlier stage. The strategy we have been pursuing to identify early apocrine breast lesions is based on the postulate that invasive apocrine carcinomas evolve from epithelial cells in terminal duct lobular units (TDLUs) in a stepwise manner that involves apocrine metaplasia of normal breast epithelia, hyperplasia, atypia, and apocrine carcinoma in situ. First, we identify specific protein biomarkers for benign apocrine metaplasia and thereafter we search for biomarkers that are highly overexpressed by pure invasive apocrine carcinomas. Here we present studies in which we have used antibodies against components of a benign apocrine signature that includes 15-prostaglandin dehydrogenase (15-PGDH), a protein that is expressed by all benign apocrine lesions, and markers that are highly overexpressed by pure invasive apocrine carcinomas such as MRP14 (S100A9), psoriasin (S100A7), and p53 to identify precancerous lesions in sclerosing adenosis (SA) with apocrine metaplasia. The latter is a benign proliferative lesion of the breast that exhibits an increase in the size of the TDLUs and characterized by retained two-cell lining, and myoepithelial (ME) and stromal hyperplasia. SA with apocrine metaplasia, i.e. apocrine adenosis (AA), presents with a higher degree of atypical apocrine hyperplasia, and these lesions are believed to be precursors of apocrine carcinoma, in situ and invasive. Analysis of 24 selected SA samples with apocrine metaplasia revealed non-obligate putative apocrine precancerous lesions that displayed some, or in same cases all the three markers associated with pure invasive apocrine carcinomas. These studies also revealed p53 positive, non-apocrine putative precancerous lesions as well as novel phenotypes for ME and some luminal cells characterized by the expression of cytokeratin 15.

Breast cancer proteomics: clinical perspectives

Breast cancer is the one of leading causes of cancer-related deaths in women within economically developed regions of the world. A major focus of present research into this malignancy is the identification of new biomarkers and drug targets to improve detection and treatment. Proteomics represents one of the latest technological developments in this context. It aims to analyse the complex circuitry of the breast cancer proteome. Here, the authors review how breast cancer proteomics has progressed so far, with emphasis on its potential application to clinically relevant scenarios.

Proteomic analysis of MCF-7 breast cancer cell line exposed to mitogenic concentration of 17β-estradiol

Estrogens are powerful mitogens that play a critical role in the onset of breast cancer and its progression. About two-thirds of all breast cancers are estrogen receptor (ER)+ at the time of diagnosis, and the ER expression is the determinant of a tumor phenotype associated with hormone responsiveness. The molecular basis of the relationship between ER expression, (anti)hormonal responsiveness, and breast cancer prognosis is still unknown. To identify the proteins affected by the presence of the hormone we used 2-D-PAGE-based bottom-up proteomics for the study of the proteome of MCF-7 cells of estrogen-responsive breast carcinoma exposed to a mitogenic concentration of 17beta-estradiol (E2) for 12, 18, 24, and 30 h. Differential expression analysis showed significant changes for 12 proteins. These include ezrin-radixin-moesin-binding phosphoprotein of 50 kDa which was previously shown to be directly regulated by E2. Expression profiles of other proteins already implicated in the progression of breast cancer, such as stathmin, calreticulin, heat shock 71 kDa, alpha-enolase are also described. Moreover, it is observed that different unexpected proteins, translation factors, and energetic metabolism enzymes are also influenced by the presence of the hormone.

Tissue detection of biomolecular predictors in breast cancer

One of the promises of modern biotechnology is to improve medical care by providing accurate diagnosis and targeted treatment to patients who will derive the maximum benefit. Delivery of this promise in the 21st century is the result of major advances in biotechnology over the past 20 years. Sequencing of the human genome and other high-volume data discovery has become possible, owing to relatively inexpensive computation power and automation. The same forces that drove the human genome project are now being focused on cataloging various disease processes at the DNA, RNA and protein levels. As these high-throughput technologies are entering the clinical care environment, the major task at hand is to integrate the complex data and derive clinically useful information. In spite of major breakthroughs in molecular approaches to the diagnosis and prognostication of cancer, there remain significant obstacles in applying these technologies to clinical samples. The time-honored conventional histopathology, for example, is still the backbone of tumor diagnosis and prognostication. The traditional fixation and processing methods are, however, rapidly losing ground, as they do not protect important tissue macromolecules. Formalin, the common universal fixative, is losing its place in histopathology. In addition to its toxicity, it alters macromolecules and renders the tissue unfit for most advanced molecular studies. This has prompted the use of fresh or fresh-frozen biopsy material for most biomolecular discoveries and clinical assays. This of course is impractical, or even impossible, in most clinical settings, particularly since tumors are being detected earlier and smaller. Also, many preneoplastic conditions are impossible to triage for freezing since their accurate diagnosis requires the use of the entire sample for detailed microscopic examination. The focus in this report is on breast cancer, where the value of the innovative approaches of the tissue detection of biomolecular predictors is examined. To this end, novel tissue handling platforms are introduced that are not only suitable for histological diagnosis, but allow the detection of tumor proteome and expression profiles on the same biopsy sample.

Proteomic analysis in cancer research: potential application in clinical use

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. The novel technologies allows researchers to facilitate the comprehensive analyses of genomes, transcriptomes, and proteomes in health and disease. The information that is expected from such technologies may soon exert a dramatic change in cancer research and impact dramatically on the care of cancer patients. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The localization of gene products, which is often difficult to deduce from the sequence, can be determined experimentally. Mechanisms, such as regulation of protein function by proteolysis, recycling, and isolation in cell compartments, affect gene products, not genes. Finally, protein-protein interactions and the molecular composition of cellular structures can be determined only at the protein level. The biological variability among patient samples as well as the great dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we have tried to offer a wide perspective about the current possibilities.

Development of novel cancer chemopreventive agents in Europe--neglected Cinderella or rising phoenix? A critical commentary. ESF Workshop on Cancer Chemoprevention, DKFZ, Heidelberg, September 18-20, 2005

Agents that prevent cancer, delay its onset, or revert premalignant conditions could have dramatic beneficial impacts on human health. Although there is an urgent need to develop cancer chemopreventive agents, researchers in the field suspect that this area of scientific endeavour in Europe leads a Cinderella existence, both in terms of perception of importance and research funding. In order to review current activities in this prevention field and to seek a consensus position, an exploratory workshop was held in September 2005 at the German Cancer Research Center (DKFZ) in Heidelberg, Germany, sponsored mainly by the European Science Foundation (ESF), and also supported by the European Association for Cancer Research (EACR) and the German Cancer Society (DKG). The 35 experts from European countries and the United States of America assessed state-of-the-art cancer chemoprevention research in Europe. The aims that the workshop organizers had pre-defined were: i) assessment of the usefulness of animal models for agent identification; ii) review of ongoing preclinical and clinical work on novel agents; iii) discussion of potential biomarkers predictive for cancer preventive efficacy; and finally iv) the potential role that European pharmaceutical industries could play in furthering chemopreventive agent development. Overall the workshop aimed at raising awareness among European clinical and laboratory researchers of the importance of the development of novel, efficacious and safe cancer preventive agents.

Integrated analysis of genetic, genomic and proteomic data

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.

Molecular pathology of breast apocrine carcinomas: A protein expression signature specific for benign apocrine metaplasia

Breast cancer is a heterogeneous disease that encompasses a wide range of histopathological types including: invasive ductal carcinoma, lobular carcinoma, medullary carcinoma, mucinous carcinoma, tubular carcinoma, and apocrine carcinoma among others. Pure apocrine carcinomas represent about 0.5% of all invasive breast cancers according to the Danish Breast Cancer Cooperative Group Registry, and despite the fact that they are morphologically distinct from other breast lesions, there are at present no standard molecular criteria available for their diagnosis. In addition, the relationship between benign apocrine changes and breast carcinoma is unclear and has been a matter of discussion for many years. Recent proteome expression profiling studies of breast apocrine macrocysts, normal breast tissue, and breast tumours have identified specific apocrine biomarkers [15-hydroxyprostaglandin dehydrogenase (15-PGDH) and hydroxymethylglutaryl coenzyme A reductase (HMG-CoA reductase)] present in early and advanced apocrine lesions. These biomarkers in combination with proteins found to be characteristically upregulated in pure apocrine carcinomas (psoriasin, S100A9, and p53) provide a protein expression signature distinctive for benign apocrine metaplasias and apocrine cystic lesions. These studies have also presented compelling evidence for a direct link, through the expression of the prostaglandin degrading enzyme 15-PGDH, between early apocrine lesions and pure apocrine carcinomas. Moreover, specific antibodies against the components of the expression signature have identified precursor lesions in the linear histological progression to apocrine carcinoma. Finally, the identification of proteins that characterize the early stages of mammary apocrine differentiation such as 15-PGDH, HMG-CoA reductase, and cyclooxygenase 2 (COX-2) has opened a window of opportunity for pharmacological intervention, not only in a therapeutic manner but also in a chemopreventive setting. Here we review published and recent results in the context of the current state of research on breast apocrine cancer.

SELDI-TOF-MS proteomics of breast cancer

The detection, diagnosis, and management of breast cancer rely on an integrated approach using clinical history, physical examination, imaging, and histopathology. The discovery and validation of novel biomarkers will aid the physician in more effectively achieving this integration. This review discusses efforts in surface-enhanced laser desorption/ionization (SELDI)-based proteomics to address various clinical questions surrounding breast cancer, including diagnosis, monitoring, and stratification for treatment. Emphasis is placed on examining how study design and execution influence the discovery and validation process, which is critical to the proper development of potential clinical tests.

Apocrine cysts of the breast: biomarkers, origin, enlargement, and relation with cancer phenotype

Up to one-third of women aged 30-50 years have cysts in their breasts and are presumed to be at increased risk of developing breast cancer. Here we present an extensive proteomic and immunohistochemistry (IHC) study of breast apocrine cystic lesions aimed at generating specific biomarkers and elucidating the relationship, if existent, of apocrine cysts with cancer phenotype. To this end we compared the expression profiles of apocrine macrocysts obtained from mastectomies from high risk cancer patients with those of cancerous and non-malignant mammary tissue biopsies collected from the same patients. We identified two biomarkers, 15-hydroxyprostaglandin dehydrogenase and 3-hydroxymethylglutaryl-CoA reductase, that were expressed specifically by apocrine type I cysts as well as by apocrine metaplastic cells in type II microcysts, terminal ducts, and intraductal papillary lesions. No expression of these markers was observed in non-malignant terminal ductal lobular units, type II flat cysts, stroma cells, or fat tissue as judged by IHC analysis of matched non-malignant tissue samples collected from 93 high risk patients enrolled in our cancer program. IHC analysis of the corresponding 93 primary tumors indicated that most apocrine changes have little intrinsic malignant potential, although some may progress to invasive apocrine cancer. None of the apocrine lesions examined, however, seemed to be a precursor of invasive ductal carcinomas, which accounted for 81% of the tumors analyzed. Our studies also provided some insight into the origin, development, and enlargement of apocrine cysts in mammary tissue. The successful identification of differentially expressed proteins that characterize specific steps in the progression from early benign lesions to apocrine cancer opens a window of opportunity for designing and testing new approaches for pharmacological intervention, not only in a therapeutic setting but also for chemoprevention, to inhibit cyst development as both 15-hydroxyprostaglandin dehydrogenase and 3-hydroxymethylglutaryl-CoA reductase are currently being targeted for chemoprevention strategies in various malignancies.

Method optimisation for peptide profiling of microdissected breast carcinoma tissue by matrix-assisted laser desorption/ionisation-time of flight and matrix-assisted laser desorption/ionisation-time of flight/time of flight-mass spectrometry

Appropriate methods for the analysis of microdissected solid tumour tissues by matrix-assisted laser desorption/ionisation-time of flight-mass spectrometry (MALDI-TOF MS) are not yet well established. Optimisation of sample preparation was performed first on undissected tissue slices, representing approximately 200 000 cells, which were solubilised either in urea containing buffer, trifluoroethanol/NH4HCO3, 0.1% sodium dodecyl sulphate (SDS) or in 0.1% RapiGest solution, then trypsin digested and analysed by MALDI-TOF MS. Solubilisation in 0.1% SDS resulted in detection of the highest number of sample specific peak signals. Interestingly, there was little overlap in detectable peaks using the different buffers, implying that they can be used complementarily to each other. Additionally, we fractionated tryptic digests on a monolithic high-performance liquid chromatography column. Fractionation of tryptic digest from whole tissue sections resulted in a four-fold increase in the total number of peaks detected. To prove this principle, we used 0.1% SDS to generate peptide patterns from 2000 microdissected tumour and stromal cells from five different breast carcinoma tumours. The tumour and stroma specific peaks could be detected upon comparison of the peptide profiles. Identification of differentially expressed peaks by MALDI-TOF/TOF MS was performed on fractionated tryptic digests derived from a whole tissue slice. In conclusion, we describe a method that is suitable for direct peptide profiling on small amounts of microdissected cells obtained from breast cancer tissues.

Two-dimensional electrophoresis database of fluorescence-labeled proteins of colon cancer cells

We constructed a novel database of the proteome of DLD-1 colon cancer cells by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of fluorescence-labeled proteins followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) analysis. The database consists of 258 functionally categorized proteins corresponding to 314 protein spots. The majority of the proteins are oxidoreductases, cytoskeletal proteins and nucleic acid binding proteins. Phosphatase treatment showed that 28% of the protein spots on the gel are phosphorylated, and mass spectrometric analysis identified 21 of them. Proteins of DLD-1 cells and of laser-microdissected colon cancer tissues showed similar distribution on 2D gels, suggesting the utility of our database for clinical proteomics.

Designing the future shape of breast cancer diagnosis, prognosis and treatment

Translational research is intended to efficiently and accurately validate the most promising preclinical biomarkers and ensure that their diagnostic or prognostic potential is rapidly adopted in the clinic. Translational research therefore needs to take into account the biological complexity that is inherent in all cellular pathways, such as the estrogen and progesterone receptor pathways, and yet produce conclusions that will have sufficient statistical power to define and shape clinical practice. This will be achieved through the incorporation of new concepts of trial design, including neoadjuvant or preoperative hypothesis-generating studies, and the increasing use of high-throughput proteomic and genomic approaches. Clinical trials will be increasingly designed to incorporate biomarker analyses, and ongoing trials may employ the retrospective collection of archival tissues to allow the use of prospectively planned biomarker analyses. This approach is being utilized in the TA01 program, which is based upon the 'Arimidex', tamoxifen, alone or in combination (ATAC) trial. The resulting incorporation of new biomarkers and revised trial designs should promote the future development of more precise and increasingly active interventions directed towards the treatment of breast cancer.

Identification of extracellular and intracellular signaling components of the mammary adipose tissue and its interstitial fluid in high risk breast cancer patients: toward dissecting the molecular circuitry of epithelial-adipocyte stromal cell interactions

It has become clear that growth and progression of breast tumor cells not only depend on their malignant potential but also on factors present in the tumor microenvironment. Of the cell types that constitute the mammary stroma, the adipocytes are perhaps the least well studied despite the fact that they represent one of the most prominent cell types surrounding the breast tumor cells. There is compelling evidence demonstrating a role for the mammary fat pad in mammary gland development, and some studies have revealed the ability of fat tissue to augment the growth and ability to metastasize of mammary carcinoma cells. Very little is known, however, about which factors adipocytes produce that may orchestrate these actions and how this may come about. In an effort to shed some light on these questions, we present here a detailed proteomic analysis, using two-dimensional gel-based technology, mass spectrometry, immunoblotting, and antibody arrays, of adipose cells and interstitial fluid of fresh fat tissue samples collected from sites topologically distant from the tumors of high risk breast cancer patients that underwent mastectomy and that were not treated prior to surgery. A total of 359 unique proteins were identified, including numerous signaling molecules, hormones, cytokines, and growth factors, involved in a variety of biological processes such as signal transduction and cell communication; energy metabolism; protein metabolism; cell growth and/or maintenance; immune response; transport; regulation of nucleobase, nucleoside, and nucleic acid metabolism; and apoptosis. Apart from providing a comprehensive overview of the mammary fat proteome and its interstitial fluid, the results offer some insight as to the role of adipocytes in the breast tumor microenvironment and provide a first glance of their molecular cellular circuitry. In addition, the results open new possibilities to the study of obesity, which has a strong association with type 2 diabetes, hypertension, and coronary heart disease.

Down-regulation of the tumor suppressor protein 14-3-3sigma is a sporadic event in cancer of the breast

14-3-3 proteins comprise a family of highly conserved and broadly expressed multifunctional regulatory proteins that are involved in various cellular processes such as cell cycle progression, cell growth, differentiation, and apoptosis. Transcriptional expression of the sigma isoform of 14-3-3 is frequently impaired in human cancers, including carcinomas of the breast, which has led to the suggestion that this protein might be involved in the neoplastic transformation of breast epithelial cells. Here we report on the analysis of 14-3-3sigma expression in primary breast tumors using a proteomic approach complemented by immunohistochemical analysis by means of specific antibodies against this isoform. We show that the levels of expression of 14-3-3sigma were similar in non-malignant breast epithelial tissue and matched malignant tissue with only sporadic loss of expression observed in 3 of the 68 tumors examined. Moreover we show that 14-3-3sigma immunoreactivity was restricted to epithelial cells and significantly stronger in the myoepithelial cells that line the mammary ducts and lobules. The lack of expression of 14-3-3sigma in the three breast carcinomas was not associated with high levels of expression of the dominant-negative transcriptional regulator DeltaNp63 or with increased expression of estrogen-responsive finger protein, a ubiquitin-protein ligase (E3) that targets 14-3-3sigma for proteolysis. Validation of the results was performed retrospectively on an independent clinical tumor sample set using a tissue microarray containing 65 primary tumors. Our data suggest that, contrary to what was previously thought, loss of expression of 14-3-3sigma protein is not a frequent event in breast tumorigenesis.

Towards discovery-driven translational research in breast cancer

Discovery-driven translational research in breast cancer is moving steadily from the study of cell lines to the analysis of clinically relevant samples that, together with the ever increasing number of novel and powerful technologies available within genomics, proteomics and functional genomics, promise to have a major impact on the way breast cancer will be diagnosed, treated and monitored in the future. Here we present a brief report on long-term ongoing strategies at the Danish Centre for Translational Breast Cancer Research to search for markers for early detection and targets for therapeutic intervention, to identify signalling pathways affected in individual tumours, as well as to integrate multiplatform 'omic' data sets collected from tissue samples obtained from individual patients. The ultimate goal of this initiative is to coalesce knowledge-based complementary procedures into a systems biology approach to fight breast cancer.

Interplay of transcriptomics and proteomics

Despite the obvious attractions of parallel profiling of transcripts and proteins on a global 'omic' scale, there are practical and biological differences involved in their application. Transcriptomics is now a robust, high-throughput, cost-effective technology capable of simultaneously quantifying tens of thousands of defined mRNA species in a miniaturized, automated format. Conversely, proteomic analysis is currently much more limited in breadth and depth of coverage owing to variations in protein abundance, hydrophobicity, stability, size and charge. Nevertheless, transcriptomic and proteomic data can be compared and contrasted provided the studies are carefully designed and interpreted. Differential splicing, post-translational modifications and data integration are among some of the future challenges to tackle.

Impact of proteomics on bladder cancer research

Detecting bladder cancer at an early stage and predicting how a tumor will behave and act in response to therapy, as well as the identification of new targets for therapeutic intervention, are among the main areas of research that will benefit from the current explosion in the number of powerful technologies emerging within proteomics. The purpose of this article is to briefly review what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily clinical practice.

Clinical Infrastructures to Support Proteomic Studies of Tissue and Fluids in Breast Cancer

The Danish Breast Cancer Cooperative Group (DBCG) was established in 1977 with the aim to ensure optimal breast cancer diagnostics and therapeutic modalities on a nationwide basis. DBCG was organized in such a way so it represents a broad interdisciplinary collaboration with established clinical databases and biobanks. This review summarizes the infrastructures, such as those of the DBCG, that are required to facilitate translational research studies aiming at further diagnostic and therapeutic improvements through interactions directed at prevention, early diagnosis, and treatment of primary breast cancer.

Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery

Clinical cancer proteomics aims at the identification of markers for early detection and predictive purposes, as well as to provide novel targets for drug discovery and therapeutic intervention. Proteomics-based analysis of traditional sources of biomarkers, such as serum, plasma, or tissue lyzates, has resulted in a wealth of information and the finding of several potential tumor biomarkers. However, many of these markers have shown limited usefulness in a clinical setting, underscoring the need for new clinically relevant sources. Here we present a novel and highly promising source of biomarkers, the tumor interstitial fluid (TIF) that perfuses the breast tumor microenvironment. We collected TIFs from small pieces of freshly dissected invasive breast carcinomas and analyzed them by two-dimensional polyacrylamide gel electrophoresis in combination with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, Western immunoblotting, as well as by cytokine-specific antibody arrays. This approach provided for the first time a snapshot of the protein components of the TIF, which we show consists of more than one thousand proteins--either secreted, shed by membrane vesicles, or externalized due to cell death--produced by the complex network of cell types that make up the tumor microenvironment. So far, we have identified 267 primary translation products including, but not limited to, proteins involved in cell proliferation, invasion, angiogenesis, metastasis, inflammation, protein synthesis, energy metabolism, oxidative stress, the actin cytoskeleton assembly, protein folding, and transport. As expected, the TIF contained several classical serum proteins. Considering that the protein composition of the TIF reflects the physiological and pathological state of the tissue, it should provide a new and potentially rich resource for diagnostic biomarker discovery and for identifying more selective targets for therapeutic intervention.

Estrogen and progesterone receptor isoforms: clinical significance in breast cancer

The identification and exploitation of biomarkers that may predict response to anti-cancer treatments has the capacity to revolutionize the way that patients with cancer are treated. In breast cancer, the estrogen receptor (ER) and the progesterone receptor (PgR) are known to have a significant predictive value in determining sensitivity to endocrine therapies. Tumor expression of ER or PgR is known to affect clinical outcome and this information is often used to determine a patient's optimal treatment regimen. However, the measurement of ER and PgR alone is more complex than originally thought and the impact of the recently identified isoforms of ER (ERalpha and ERbeta) and PgR (PgRA and PgRB), as well as several variant and mutant forms, upon the choice of treatment remains unclear. Therefore, ER and PgR expression alone are unlikely to determine a patient's optimal treatment regimen, particularly when the amount of 'cross-talk' between different pathways, such as the epidermal growth factor receptor pathway, is considered. In order to account for the complex cell-signaling environment that occurs in breast cancer, multifactorial techniques are needed to analyze tumor biomarker expression. The recent advances in genomic- or proteomic-based approaches has enabled molecular portraits of breast cancers to be painted, allowing biomarkers of response and prognosis to be identified and characterized more accurately than before. In the future, patients could be treated according to the molecular portrait of their tumor biomarker expression, maximizing the therapeutic benefit that each patient receives.

Comparison of proteomic and genomic analyses of the human breast cancer cell line T47D and the antiestrogen-resistant derivative T47D-r

In search of novel mechanisms leading to the development of antiestrogen-resistance in human breast tumors, we analyzed differences in the gene and protein expression pattern of the human breast carcinoma cell line T47D and its derivative T47D-r, which is resistant toward the pure antiestrogen ZM 182780 (Faslodex trade mark, fulvestrant). Affymetrix DNA chip hybridizations on the commercially available HuGeneFL and Hu95A arrays were carried out in parallel to the proteomics analysis where the total cellular protein content of T47D or T47D-r was separated on two-dimensional gels. Thirty-eight proteins were found to be reproducibly up- or down-regulated more than 2-fold in T47D-r versus T47D in the proteomics analysis. Comparison with differential mRNA analysis revealed that 19 of these were up- or down-regulated in parallel with the corresponding mRNA molecules, among which are the protease cathepsin D, the GTPases Rab11a and MxA, and the secreted protein hAG-2. For 11 proteins, the corresponding mRNA was not found to be differentially expressed, and for eight proteins an inverse regulation was found at the mRNA level. In summary, mRNA expression data, when combined with proteomic information, provide a more detailed picture of how breast cancer cells are altered in their antiestrogen-resistant compared with the antiestrogen-sensitive state.