Laser capture microdissection and advanced molecular analysis of human breast cancer

Laser Capture Microdissection: A Gear for Pancreatic Cancer Research

The advancement in molecular techniques has been attributed to the quality and significance of cancer research. Pancreatic cancer (PC) is one of the rare cancers with aggressive behavior and a high mortality rate. The asymptomatic nature of the disease until its advanced stage has resulted in late diagnosis as well as poor prognosis. The heterogeneous character of PC has complicated cancer development and progression studies. The analysis of bulk tissues of the disease was insufficient to understand the disease, hence, the introduction of the single-cell separating technique aided researchers to decipher more about the specific cell population of tumors. This review gives an overview of the Laser Capture Microdissection (LCM) technique, one of the single-cell separation methods used in PC research.

Global Gene Expression Profiling: A Complement to Conventional Histopathologic Analysis of Neoplasia

Transcriptional profiling of entire tumors has yielded considerable insight into the molecular mechanisms of heterogeneous cell populations within different types of neoplasms. The data thus acquired can be further refined by microdissection methods that enable the analyses of subpopulations of neoplastic cells. Separation of the various components of a neoplasm (i.e., stromal cells, inflammatory infiltrates, and blood vessels) has been problematic, primarily because of a paucity of tools for accurate microdissection. The advent of laser capture microdissection combined with powerful tools of linear amplification of RNA and high-throughput microarray-based assays have allowed the transcriptional mapping of intricate and highly complex networks within pure populations of neoplastic cells. With this approach, specific “molecular signatures” can be assigned to tumors of distinct or even similar histomorphology, thereby aiding the desired objective of pattern recognition, tumor classification, and prognostication. This review highlights the potential benefits of global gene expression profiling of tumor cells as a complement to conventional histopathologic analyses.

Functional interrogation of breast cancer: from models to drugs

Functional genomics allows for the activity of the whole genome to be surveyed at once. Using this technology for the identification of novel targets and their validation in disease-specific contexts has profound implications for the future of drug discovery. Now researchers have the technological means to gather comprehensive data on basic biological phenomena and disease mechanisms, while monitoring the effect of drug candidates on a molecular level. Pathway analysis can facilitate the genetic profiling of patients and, in turn, predict individual responses to treatment regimes. Functional interrogation of a disease-specific phenotype at a whole genome level (through, for example, the use of whole genome RNAi libraries) allows for the identification of critical regulators in complex biological systems, and the detection of putative targets for future therapeutic intervention. The authors describe the applications of functional genomics in models of breast cancer and the integration of these disparate technologies, specifically in the context of the search for novel therapeutic targets.

Immunophenotypic identification and characterization of tumor cells and infiltrating cell populations in meningiomas

Meningiomas are primary tumors of the central nervous system composed of both neoplastic and other infiltrating cells. We determined the cellular composition of 51 meningioma samples by multiparameter flow cytometric (MFC) immunophenotyping and investigated the potential relationship between mRNA and protein expression levels of neoplastic cells. For immunophenotypic, morphologic, and cytogenetic characterization of individual cell populations, a large panel of markers was used together with phagocytic/endocytic functional assays and MFC sorting. Overall, our results revealed coexistence of CD45(-) neoplastic cells and CD45(+) immune infiltrating cells in all meningiomas. Infiltrating cells included tissue macrophages, with an HLA-DR(+)CD14(+)CD45(+)CD68(+)CD16(-/+)CD33(-/+) phenotype and high phagocytic/endocytic activity, and a small proportion of cytotoxic lymphocytes (mostly T CD8(+) and natural killer cells). Tumor cells expressed multiple cell adhesion proteins, tetraspanins, HLA-I/HLA-DR molecules, complement regulatory proteins, cell surface ectoenzymes, and growth factor receptors. Noteworthy, the relationship between mRNA and protein levels was variable, depending on the proteins evaluated and the level of infiltration by immune cells. In summary, our results indicate that MFC immunophenotyping provides a reliable tool for the characterization of the patterns of protein expression of different cell populations coexisting in meningioma samples, with a more accurate measure of gene expression profiles of tumor cells at the functional/protein level than conventional mRNA microarray, independently of the degree of infiltration of the tumor by immune cells.

Integrated proteomic, transcriptomic, and biological network analysis of breast carcinoma reveals molecular features of tumorigenesis and clinical relapse

Gene and protein expression changes observed with tumorigenesis are often interpreted independently of each other and out of context of biological networks. To address these limitations, this study examined several approaches to integrate transcriptomic and proteomic data with known protein-protein and signaling interactions in estrogen receptor positive (ER+) breast cancer tumors. An approach that built networks from differentially expressed proteins and identified among them networks enriched in differentially expressed genes yielded the greatest success. This method identified a set of genes and proteins linking pathways of cellular stress response, cancer metabolism, and tumor microenvironment. The proposed network underscores several biologically intriguing events not previously studied in the context of ER+ breast cancer, including the overexpression of p38 mitogen-activated protein kinase and the overexpression of poly(ADP-ribose) polymerase 1. A gene-based expression signature biomarker built from this network was significantly predictive of clinical relapse in multiple independent cohorts of ER+ breast cancer patients, even after correcting for standard clinicopathological variables. The results of this study demonstrate the utility and power of an integrated quantitative proteomic, transcriptomic, and network analysis approach to discover robust and clinically meaningful molecular changes in tumors.

Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia

Ductal carcinoma in situ (DCIS) is a precursor lesion that can gives rise to invasive breast cancer (IBC). It has been proposed that both the nature of the lesion and the tumor microenvironment play key roles in progression to IBC. Here, laser capture microdissected tissue from pure DCIS and pure IBC were employed to define key gene expression profiles in either the epithelial or stromal compartment associated with disease progression. Each tissue had distinct gene expression profiles, and a DCIS/IBC classifier accurately distinguished DCIS versus IBC in multiple independent data sets. However, contrary to other studies that profiled DCIS associated with invasive disease, we found that the most significant alterations in gene expression were observed in the epithelial compartment rather than in the stroma. In particular, genes associated with epithelial-to-mesenchymal transition and myoepithelial cell-specific genes were enriched in invasive cancer relative to pure DCIS. Such alterations in transcript levels were associated with all subtypes of breast cancer, but were particularly indicative of poor outcome in ER-negative breast cancer. Together, these studies indicate that lesion-specific differences in gene expression associated with invasive phenotype are particularly relevant in the progression of DCIS to invasive breast cancer.

Systematic bias in genomic classification due to contaminating non-neoplastic tissue in breast tumor samples

Background

Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification.

Methods

To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors.

Results

Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability.

Conclusions

Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

State of the art in tumor antigen and biomarker discovery

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology.

Maintaining RNA integrity in a homogeneous population of mammary epithelial cells isolated by Laser Capture Microdissection

BACKGROUND

Laser-capture microdissection (LCM) that enables the isolation of specific cell populations from complex tissues under morphological control is increasingly used for subsequent gene expression studies in cell biology by methods such as real-time quantitative PCR (qPCR), microarrays and most recently by RNA-sequencing. Challenges are i) to select precisely and efficiently cells of interest and ii) to maintain RNA integrity. The mammary gland which is a complex and heterogeneous tissue, consists of multiple cell types, changing in relative proportion during its development and thus hampering gene expression profiling comparison on whole tissue between physiological stages. During lactation, mammary epithelial cells (MEC) are predominant. However several other cell types, including myoepithelial (MMC) and immune cells are present, making it difficult to precisely determine the specificity of gene expression to the cell type of origin. In this work, an optimized reliable procedure for producing RNA from alveolar epithelial cells isolated from frozen histological sections of lactating goat, sheep and cow mammary glands using an infrared-laser based Arcturus Veritas LCM (Applied Biosystems®) system has been developed. The following steps of the microdissection workflow: cryosectioning, staining, dehydration and harvesting of microdissected cells have been carefully considered and designed to ensure cell capture efficiency without compromising RNA integrity.

RESULTS

The best results were obtained when staining 8 μm-thick sections with Cresyl violet® (Ambion, Applied Biosystems®) and capturing microdissected cells during less than 2 hours before RNA extraction. In addition, particular attention was paid to animal preparation before biopsies or slaughtering (milking) and freezing of tissue blocks which were embedded in a cryoprotective compound before being immersed in isopentane. The amount of RNA thus obtained from ca.150 to 250 acini (300,000 to 600,000 μm2) ranges between 5 to 10 ng. RNA integrity number (RIN) was ca. 8.0 and selectivity of this LCM protocol was demonstrated through qPCR analyses for several alveolar cell specific genes, including LALBA (α-lactalbumin) and CSN1S2 (αs2-casein), as well as Krt14 (cytokeratin 14), CD3e and CD68 which are specific markers of MMC, lymphocytes and macrophages, respectively.

CONCLUSIONS

RNAs isolated from MEC in this manner were of very good quality for subsequent linear amplification, thus making it possible to establish a referential gene expression profile of the healthy MEC, a useful platform for tumor biomarker discovery.

Laser capture microdissection of cervical human papillomavirus infections: copy number of the virus in cancerous and normal tissue and heterogeneous DNA methylation

Research on the pathogenicity of human papillomaviruses (HPVs) during cervical carcinogenesis often relies on the study of homogenized tissue or cultured cells. This approach does not detect molecular heterogeneities within the infected tissue. It is desirable to understand molecular properties in specific histological contexts. We asked whether laser capture microdissection (LCM) of archival cervical tumors in combination with real-time polymerase chain reaction and bisulfite sequencing permits (i) sensitive DNA diagnosis of small clusters of formalin-fixed cells, (ii) quantification of HPV DNA in neoplastic and normal cells, and (iii) analysis of HPV DNA methylation, a marker of tumor progression. We analyzed 26 tumors containing HPV-16 or 18. We prepared DNA from LCM dissected thin sections of 100 to 2000 cells, and analyzed aliquots corresponding to between nine and 70 cells. We detected nine to 630 HPV-16 genome copies and one to 111 HPV-18 genome copies per tumor cell, respectively. In 17 of the 26 samples, HPV DNA existed in histologically normal cells distant from the margins of the tumors, but at much lower concentrations than in the tumor, suggesting that HPVs can infect at low levels without pathogenic changes. Methylation of HPV DNA, a biomarker of integration of the virus into cellular DNA, could be measured only in few samples due to limited sensitivity, and indicated heterogeneous methylation patterns in small clusters of cancerous and normal cells. LCM is powerful to study molecular parameters of cervical HPV infections like copy number, latency and epigenetics.

Conventional and Proteomic Technologies for the Detection of Early Stage Malignancies: Markers for Ovarian Cancer

Our understanding of the tumor microenvironment continues to evolve and allows for the identification of biomarkers that should detect the presence of early stage malignancies. Recent advances in computational analysis and biomedical technologies have come together to elucidate signatures associated with cancer and that are capable of identifying unique tumor-specific proteins. Within the tumor microenvironment, we continue to characterize the proteophysiology of the different steps associated with tumor progression. The urgent need for biomarkers accurately detecting early-stage epithelial ovarian cancer has prompted us, and others, to engage in a search for specific peptide signatures that may discriminate transformed cells from those of the normal ovarian microenvironment. This endeavor also provides new insights into the biology of the disease, which may not only be applicable to detection but may also help to initiate new therapies and optimize patient care.

Review of gene-expression profiling and its clinical use in breast cancer

Despite advances in the treatment of early-stage breast cancer, physicians still lack the ability to accurately predict which individual patients will relapse and would benefit from adjuvant chemotherapy. Traditional clinicopathologic factors are important in helping to determine risk of relapse, but do not fully account for the biologic complexity of breast cancer. Gene-expression profiling has provided us with insight into the heterogeneity of breast cancer and led to the development of prognostic and predictive molecular gene signature models designed to aid in clinical decision-making. However, it remains to be determined how much refinement in prognosis genomic models provide over standard clinicopathologic features and whether these refinements translate into improvements in clinical practice. On-going large prospective multi-center clinical trials will provide us with information regarding the clinical utility of two of these assays, but for now, implementation of these models into widespread clinical practice remains limited.

Gene expression profiling of breast cancer

DNA microarray platforms for gene expression profiling were invented relatively recently, and breast cancer has been among the earliest and most intensely studied diseases using this technology. The molecular signatures so identified help reveal the biologic spectrum of breast cancers, provide diagnostic tools as well as prognostic and predictive gene signatures, and may identify new therapeutic targets. Data are best presented in an open access format to facilitate external validation, the most crucial step in identifying robust, reproducible gene signatures suitable for clinical translation. Clinically practical applications derived from full expression profile studies already in use include reduced versions of microarrays representing key discriminatory genes and therapeutic targets, quantitative polymerase chain reaction assays, or immunohistochemical surrogate panels (suitable for application to standard pathology blocks). Prospective trials are now underway to determine the value of such tools for clinical decision making in breast cancer; these efforts may serve as a model for using such approaches in other tumor types.

Proteome analysis of human gastric cardia adenocarcinoma by laser capture microdissection

Background

The incidence of gastric cardiac adenocarcinoma (GCA) has been increasing in the past two decades in China, but the molecular changes relating to carcinogenesis have not been well characterised.

Methods

In this study, we used a comparative proteomic approach to analyse the malignant and nonmalignant gastric cardia epithelial cells isolated by navigated laser capture microdissection (LCM) from paired surgical specimens of human GCA.

Results

Twenty-seven spots corresponding to 23 proteins were consistently differentially regulated. Fifteen proteins were shown to be up-regulated, while eight proteins were shown to be down-regulated in malignant cells compared with nonmalignant columnar epithelial cells. The identified proteins appeared to be involved in metabolism, chaperone, antioxidation, signal transduction, apoptosis, cell proliferation, and differentiation. In addition, expressions of HSP27, 60, and Prx-2 in GCA specimens were further confirmed by immunohistochemical and western blot analyses.

Conclusion

These data indicate that the combination of navigated LCM with 2-DE provides an effective strategy for discovering proteins that are differentially expressed in GCA. Such proteins may contribute in elucidating the molecular mechanisms of GCA carcinogenesis. Furthermore, the combination provides potential clinical biomarkers that aid in early detection and provide potential therapeutic targets.

The ongoing evolution of proteomics in malignancy

The complementary fields of genomics and proteomics offer insights into the molecular mechanisms of diseases. While genomics seeks to define our genetic substrate, proteomics explores the structure and function of proteins, which are the end effectors of our genes. Proteomics has been revolutionized in the past decade by the application of techniques such as protein arrays, two-dimensional gel electrophoresis, and mass spectrometry. These techniques have tremendous potential for biomarker development, target validation, diagnosis, prognosis, and optimization of treatment in medical care, especially in the field of clinical oncology. We will discuss innovations in proteomic technologies and highlight their prospective applications to patient care.

Contribution of laser microdissection-based technology to proteomic analysis in hepatocellular carcinoma developing on cirrhosis

Hepatocellular carcinoma (HCC) is a major cause of cancer worldwide. Proteomic studies provide opportunities to uncover targets for the diagnosis and treatment of this disease. However, in HCC developing in a setting of cirrhosis, the detection of proteome alterations may be hampered by the increased cellular heterogeneity of tissue when analysing global liver homogenates. The aim of this study was to evaluate whether the identification of proteome alterations in these HCC cases was improved when the differential protein profile between tumour and non-tumour areas of liver was determined using hepatocytes isolated by laser microdissection (LM). Differential profiles established with LM-hepatocytes and liver section homogenates using 2-DE and MS exhibited noticeable differences: 30% of the protein spots with deregulated expression in tumorous LM-samples did not display any modification in homogenates; conversely 15% of proteins altered in tumorous homogenates were not impaired in LM-hepatocytes. These alterations resulted from the presence in cirrhotic liver of fibrotic stroma which displayed a protein pattern different from that determined in LM-hepatocytes. In conclusion, our data demonstrate the interest of LM in distinguishing between fibrotic and hepatocyte proteome alterations and thus the benefit of LM to proteome studies of HCC developing in a context of cirrhosis.

Proteomics in nutrition research: principles, technologies and applications

The global profiling of the whole protein complement of the genome expressed in a particular cell or organ, or in plasma or serum, makes it possible to identify biomarkers that respond to alterations in diet or to treatment, and that may have predictive value for the modelling of biological processes. Proteomics has not yet been applied on a large scale in nutritional studies, yet it has advantages over transcriptome profiling techniques in that it directly assesses the entities that carry out the biological functions. The present review summarizes the different approaches in proteomics research, with special emphasis on the current technical ‘workhorses’: two-dimensional (2D)-PAGE with immobilized pH gradients and protein identification by MS. Using a work-flow approach, we provide information and advice on sample handling and preparation, protein solubilization and pre-fractionation, protein separation by 2D-PAGE, detection and quantification via computer-assisted analysis of gels, and protein identification and characterization techniques by means of MS. Examples from nutritional studies employing proteomics are provided to demonstrate not only the advantages but also the limitations of current proteome analysis platforms.

Exploring the molecular basis of tumor metastasis by microarray analysis

Over the past decade, microarray technology has become a powerful tool to provide a genome-wide view of genetic or epigenetic changes associated with tumor metastasis. To extract biologically meaningful information from the vast amounts of microarray data, it is crucial to choose suitable biological systems and have vigilant experimental design. In this review, I will discuss several experimental systems that are used to identify genes involved in tumor metastasis by microarray analysis. Also highlighted are the pros and cons for each system. In particular, I will describe our experience of using microarray technology to identify the transcription factor Twist as an essential player in tumor metastasis.

Laser Capture Microdissection and Laser Pressure Catapulting as Tools to Study Gene Expression in Individual Cells of a Complex Tissue

Laser capture microdissection (LCM) method allows the selection of individual or clustered cells from intact tissues. LCM enables to pick cells from tissues that are difficult to study individually, to sort the anatomical complexity of tissues, and to make the cells available for molecular analyses. This technology provides an opportunity to uncover the molecular control of cellular fate in the natural microenvironment. It is a difficult task to obtain cells from skeletal tissues, such as cartilage, periost, bone, and muscle, that are structured together and do not exist as individual organs. LCM allows isolation of desired cells from the native tissue environment for the analysis of gene expression. We earlier described the selection of cells from skeletal tissues that were analyzed for expression of transcription factors, receptors for cytokines, nuclear receptors, and functional genes such as alkaline phosphatase and structural proteins. Current results acquired using the LCM technology demonstrate expression of known genes that are in agreement with their reported in vivo and in vitro function in skeletal cells. The obtained knowledge will provide molecular information in the context of the cell and tissue biology. Such analysis will enable a reliable interpretation of function of known and novel genes expression in the skeletal tissues under various physiological conditions.

Proteomic Profiling of Primary Breast Cancer Predicts Axillary Lymph Node Metastasis

To determine if protein expression in primary breast cancers can predict axillary lymph node (ALN) metastasis, we assessed differences in protein expression between primary breast cancers with and without ALN metastasis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Laser capture microdissection was performed on invasive breast cancer frozen sections from 65 patients undergoing resection with sentinel lymph node (SLN) or level I and II ALN dissection. Isolated proteins from these tumors were applied to immobilized metal affinity capture (IMAC-3) ProteinChip arrays and analyzed by SELDI-TOF-MS to generate unique protein profiles. Correlations between unique protein peaks and histologically confirmed ALN status and other known clinicopathologic factors were examined using ANOVA and multivariate logistic regression. Two metal-binding polypeptides at 4,871 and 8,596 Da were identified as significant risk factors for nodal metastasis (P = 0.034 and 0.015, respectively) in a multivariate analysis. Lymphovascular invasion (LVI) was the only clinicopathologic factor predictive of ALN metastasis (P = 0.0038). In a logistic regression model combining the 4,871 and 8,596 Da peaks with LVI, the area under the receiver operating characteristic curve was 0.87. Compared with patients with negative ALN, those with > or =2 positive ALN or non-SLN metastases were significantly more likely to have an increased peak at 4,871 Da (P = 0.016 and 0.0083, respectively). ProteinChip array analysis identified differential protein peaks in primary breast cancers that predict the presence and number of ALN metastases and non-SLN status.

Computational expression deconvolution in a complex mammalian organ

Background

Microarray expression profiling has been widely used to identify differentially expressed genes in complex cellular systems. However, while such methods can be used to directly infer intracellular regulation within homogeneous cell populations, interpretation of in vivo gene expression data derived from complex organs composed of multiple cell types is more problematic. Specifically, observed changes in gene expression may be due either to changes in gene regulation within a given cell type or to changes in the relative abundance of expressing cell types. Consequently, bona fide changes in intrinsic gene regulation may be either mimicked or masked by changes in the relative proportion of different cell types. To date, few analytical approaches have addressed this problem.

Results

We have chosen to apply a computational method for deconvoluting gene expression profiles derived from intact tissues by using reference expression data for purified populations of the constituent cell types of the mammary gland. These data were used to estimate changes in the relative proportions of different cell types during murine mammary gland development and Ras-induced mammary tumorigenesis. These computational estimates of changing compartment sizes were then used to enrich lists of differentially expressed genes for transcripts that change as a function of intrinsic intracellular regulation rather than shifts in the relative abundance of expressing cell types. Using this approach, we have demonstrated that adjusting mammary gene expression profiles for changes in three principal compartments – epithelium, white adipose tissue, and brown adipose tissue – is sufficient both to reduce false-positive changes in gene expression due solely to changes in compartment sizes and to reduce false-negative changes by unmasking genuine alterations in gene expression that were otherwise obscured by changes in compartment sizes.

Conclusion

By adjusting gene expression values for changes in the sizes of cell type-specific compartments, this computational deconvolution method has the potential to increase both the sensitivity and specificity of differential gene expression experiments performed on complex tissues. Given the necessity for understanding complex biological processes such as development and carcinogenesis within the context of intact tissues, this approach offers substantial utility and should be broadly applicable to identifying gene expression changes in tissues composed of multiple cell types.

Cancer biomarkers: knowing the present and predicting the future

In recent years the discovery of cancer biomarkers has become a major focus of cancer research. The widespread use of prostate-specific antigen in prostate cancer screening has motivated researchers to identify suitable markers for screening different types of cancer. Biomarkers are also useful for diagnosis, monitoring disease progression, predicting disease recurrence and therapeutic treatment efficacy. With the advent of new and improved genomic and proteomic technologies such as DNA and tissue microarray, two-dimensional gel eletrophoresis, mass spectrometry and protein assays coupled with advanced bioinformatic tools, it is possible to develop biomarkers that are able to reliably and accurately predict outcomes during cancer management and treatment. In years to come, a serum or urine test for every phase of cancer may drive clinical decision making, supplementing or replacing currently existing invasive techniques.

Proteomics in cancer research: Methods and application of array-based protein profiling technologies

With the human genome sequence now determined, the field of molecular medicine is moving beyond genomics to proteomics, the large-scale analysis of proteins. It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods. Microarray technology is a new and efficient approach, for extracting relevant biomedical data and has a wide range of applications. It provides a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate and protein-drug interactions. This review paper will explore the key themes in proteomics and their application in clinical cancer research.

Laser-capture microdissection: refining estimates of the quantity and distribution of latent herpes simplex virus 1 and varicella-zoster virus DNA in human trigeminal Ganglia at the single-cell level

There remains uncertainty and some controversy about the percentages and types of cells in human sensory nerve ganglia that harbor latent herpes simplex virus 1 (HSV-1) and varicella-zoster virus (VZV) DNA. We developed and validated laser-capture microdissection and real-time PCR (LCM/PCR) assays for the presence and copy numbers of HSV-1 gG and VZV gene 62 sequences in single cells recovered from sections of human trigeminal ganglia (TG) obtained at autopsy. Among 970 individual sensory neurons from five subjects, 2.0 to 10.5% were positive for HSV-1 DNA, with a median of 11.3 copies/positive cell, compared with 0.2 to 1.5% of neurons found to be positive by in situ hybridization (ISH) for HSV-1 latency-associated transcripts (LAT), the classical surrogate marker for HSV latency. This indicates a more pervasive latent HSV-1 infection of human TG neurons than originally thought. Combined ISH/LCM/PCR assays revealed that the majority of the latently infected neurons do not accumulate LAT to detectable levels. We detected VZV DNA in 1.0 to 6.9% of individual neurons from 10 subjects. Of the total 1,722 neurons tested, 4.1% were VZV DNA positive, with a median of 6.9 viral genomes/positive cell. After removal by LCM of all visible neurons on a slide, all surrounding nonneuronal cells were harvested and assayed: 21 copies of HSV-1 DNA were detected in approximately 5,200 nonneuronal cells, while nine VZV genomes were detected in approximately 14,200 nonneuronal cells. These data indicate that both HSV-1 and VZV DNAs persist in human TG primarily, if not exclusively, in a moderate percentage of neuronal cells.

Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy

The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized "designer" therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.

Somatic mosaicism and cancer: a micro-genetic examination into the role of the androgen receptor gene in prostate cancer

Recent evidence has shown that the androgen receptor (AR) plays a major role in all prostate cancer stages, including both androgen-dependent and -independent tumors. A large number of studies have examined the possible effects of a functional polymorphism in the AR gene, a variable-length CAG repeat, on the development of prostate cancer, but the results to date have been inconclusive. We have considered the fact that the tissue heterogeneity present in almost all prostate cancer tumors has rarely been regarded as an indicator of AR genetic heterogeneity. To determine if genetic heterogeneity exists and is a significant event in prostate cancer development, we have examined prostate cancer tumors for somatic shortening of the AR gene CAG repeat. All 72 laser capture microdissected samples from archival prostate cancer tissues, as well as samples from freshly prepared prostate cancer tissues, showed some genetic heterogeneity (somatic mosaicism) for AR CAG repeat length. Cancerous tissues showed a much greater degree of genetic heterogeneity than adjacent benign tissues, as well as a very significant shortening of their CAG repeat lengths. However, CAG repeat length heterogeneity was not observed in normal prostate tissues. It is hypothesized that somatic mosaicism of the AR CAG repeat in prostate cancer tumors may be found to be an important genetic event in precancerous tissue, which may subsequently lead to the development of prostate cancer.

Laser capture microdissection and single-cell RT-PCR without RNA purification

Chronic infectious diseases of the central nervous system (CNS) are characterized by intrathecal synthesis of increased amounts of immunoglobulin G (IgG) directed against the agent that causes disease. In other inflammatory CNS diseases such as multiple sclerosis and CNS sarcoid, the targets of the humoral immune response are uncertain. To identify the IgGs expressed by individual CD38(+) plasma cells seen in human brain sections, we merged the techniques of laser capture microdissection (LCM) and single-cell RT-PCR. Frozen brain sections from a patient who died of subacute sclerosing panencephalitis (SSPE), were rapidly immunostained and examined by LCM to dissect individual CD38(+) cells. After cell lysis, we developed two techniques for reverse-transcription (RT) of unpurified total RNA in the cell lysates. The first method performed repeated and rapid freeze-thawing, followed by centrifugation of the cell lysate into tubes for subsequent RT. The second, more successful method performed RT in situ on detergent-solubilized cells directly on the cap surface; subsequent nested PCR identified heavy and light chain sequences expressed by two-thirds of individually isolated plasma cells. These techniques will streamline the identification of gene expression products in single cells from complex tissues and have the potential to identify IgGs expressed in the CNS of inflammatory diseases of unknown etiology.

Changes in gene expression during the development of mammary tumors in MMTV-Wnt-1 transgenic mice

cDNA microarray-derived expression profiles of MMTV-Wnt-1 and MMTV-Neu transgenic mice reveal several hundred genes to be differentially expressed at each stage of breast tumor development.

Background

In human breast cancer normal mammary cells typically develop into hyperplasia, ductal carcinoma in situ, invasive cancer, and metastasis. The changes in gene expression associated with this stepwise progression are unclear. Mice transgenic for mouse mammary tumor virus (MMTV)-Wnt-1 exhibit discrete steps of mammary tumorigenesis, including hyperplasia, invasive ductal carcinoma, and distant metastasis. These mice might therefore be useful models for discovering changes in gene expression during cancer development.

Results

We used cDNA microarrays to determine the expression profiles of five normal mammary glands, seven hyperplastic mammary glands and 23 mammary tumors from MMTV-Wnt-1 transgenic mice, and 12 mammary tumors from MMTV-Neu transgenic mice. Adipose tissues were used to control for fat cells in the vicinity of the mammary glands. In these analyses, we found that the progression of normal virgin mammary glands to hyperplastic tissues and to mammary tumors is accompanied by differences in the expression of several hundred genes at each step. Some of these differences appear to be unique to the effects of Wnt signaling; others seem to be common to tumors induced by both Neu and Wnt-1 oncogenes.

Conclusion

We described gene-expression patterns associated with breast-cancer development in mice, and identified genes that may be significant targets for oncogenic events. The expression data developed provide a resource for illuminating the molecular mechanisms involved in breast cancer development, especially through the identification of genes that are critical in cancer initiation and progression.

From signatures to models: understanding cancer using microarrays

Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

Analysis for Localization of Steroid Sulfatase in Human Tissues

Human steroid sulfatase (STS) is an enzyme that hydrolyzes several sulfated steroids, such as estrone sulfate, dehydroepiandrosterone sulfate, and cholesterol sulfate, and results in the production of active substances. STS has been demonstrated in human breast cancer tissues and is considered to be involved in intratumoral estrogen production. It is very important to analyze the cellular distribution of STS with accuracy in human tissues in order to obtain a better understanding of the biological significance of STS. Therefore, this chapter describes several morphological approaches used to study the localization of STS, including immunohistochemistry, mRNA in situ hybridization, and laser capture microdissection/reverse transcription-polymerase chain reaction, in human tissues.

Proteomic Analysis of Human Breast Cancer Tissue with Laser-Capture Microdissection and Reverse-Phase Protein Microarrays

Despite recent advances in breast cancer therapy, women with similar types of breast cancers may respond very differently to standard treatments. The emerging field of clinical proteomics has the potential to revolutionize breast cancer therapy. The ultimate goal of clinical proteomics is to characterize information flow through protein cascades for individual patients. After the protein networks have been elucidated, drug therapies may be specially designed for each patient. The following review describes the proteomic technologies of laser-capture microdissection (LCM) and reverse-phase protein arrays (RPPAs). These technologies allow scientists to analyze relative abundances of key cellular signaling proteins from pure cell populations. Cell survival and apoptotic protein pathways are currently being monitored with LCM and RPPAs at the National Institutes of Health, in phase II clinical trials of metastatic breast and ovarian cancers. Ultimately, proteomics will become an integral component of tracking and managing individualized breast cancer therapy.