Molecular profiling of human cancer

Nanotheranostics for personalized medicine

Nanotheranostics, the integration of diagnostic and therapeutic function in one system using the benefits of nanotechnology, is extremely attractive for personalized medicine. Because treating cancer is not a one-size-fits-all scenario, it requires therapy to be adapted to the patient's specific biomolecules. Personalized and precision medicine (PM) does just that. It identifies biomarkers to gain an understanding of the diagnosis and in turn treating the specific disorder based on the precise diagnosis. By predominantly utilizing the unique properties of nanoparticles to achieve biomarker identification and drug delivery, nanotheranostics can be applied to noninvasively discover and target image biomarkers and further deliver treatment based on the biomarker distribution. This is a large and hopeful role theranostics must fill. However, as described in this expert opinion, current nanotechnology-based theranostics systems engineered for PM applications are not yet sufficient. PM is an ever-growing field that will be a driving force for future discoveries in biomedicine, especially cancer theranostics. In this article, the authors dissect the requirements for successful nanotheranostics-based PM.

Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction

Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, to directly address the effects of variable normal tissue contamination in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer.

Utility, reliability and reproducibility of immunoassay multiplex kits

Multiplex technologies are becoming increasingly important in biomarker studies as they enable patterns of biomolecules to be examined, which provide a more comprehensive depiction of disease than individual biomarkers. They are crucial in deciphering these patterns, but it is essential that they are endorsed for reliability, reproducibility and precision. Here we outline the theoretical basis of a variety of multiplex technologies: Bead-based multiplex immunoassays (i.e. Cytometric Bead Arrays, Luminex™ and Bio-Plex Pro™), microtitre plate-based arrays (i.e. Mesoscale Discovery (MSD) and Quantsys BioSciences QPlex), Slide-based Arrays (i.e. FastQuant™) and reverse phase protein arrays. Their utility, reliability and reproducibility are discussed.

Discovery of stage-related proteins in esophageal squamous cell carcinoma using proteomic analysis

Esophageal squamous cell carcinoma (ESCC) is the major subtype of esophageal cancers in China, and characterized with high morbidity and mortality. So far, the diagnosis of ESCC is mainly dependent on the alterations in esophageal histology, but most cases of ESCC with low stage do not display visible histological abnormalities. Therefore, a deep understanding of the mechanism of ESCC progression and seeking stage-specific molecules might improve the diagnosis and therapy for ESCC. In this study, we used proteomics to analyze ESCC tissues with classification by TNM stage, and determined the proteomic features correlated with ESCC progression (from stages I to III). Proteins that exhibited significantly different expression patterns between ESCC and corresponding normal esophageal tissues were identified using MS. The identified proteins with differentiated expression mainly fell into three protein categories (i.e. cytoskeleton system-associated proteins, metabolism enzymes, and heat shock proteins). In addition, real-time PCR highlighted some molecules that were associated with tumor stages at the mRNA level, such as enolase 1, chromosome 1 ORF 10, elastase inhibitor, α B crystalline, stress-induced phosphoprotein 1, and squamous cell carcinoma antigen 1. Altogether, these data provided further information on ESCC progression and potential drug targets for ESCC clinical therapy.

Epigenetics advancing personalized nanomedicine in cancer therapy

Personalized medicine aims to deliver the right drug to a right patient at the right time. It offers unique opportunities to integrate new technologies and concepts to disease prognosis, diagnosis and therapeutics. While selective personalized therapies are conceptually impressive, the majority of cancer therapies have dismal outcome. Such therapeutic failure could result from no response, drug resistance, disease relapse or severe side effect from improper drug delivery. Nanomedicine, the application of nanotechnology in medicine, has a potential to advance the identification of diagnostic and prognostic biomarkers and the delivery of right drug to disease sites. Epigenetic aberrations dynamically contribute to cancer pathogenesis. Given the individualized traits of epigenetic biomarkers, epigenetic considerations would significantly refine personalized nanomedicine. This review aims to dissect the interface of personalized medicine with nanomedicine and epigenetics. I will outline the progress and highlight challenges and areas that can be further explored perfecting the personalized health care.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

Superficial spreading and nodular melanoma are distinct biological entities: a challenge to the linear progression model

The classification of melanoma subtypes into prognostically relevant and therapeutically insightful categories has been a challenge since the first description of melanoma in the 1800s. One limitation has been the assumption that the two most common histological subtypes of melanoma, superficial spreading and nodular, evolve according to a linear model of progression, as malignant melanocytes spread radially and then invade vertically. However, recent clinical, pathological, and molecular data indicate that these two histological subtypes might evolve as distinct entities. Here, we review the published data that support distinct molecular characterization of superficial spreading and nodular melanoma, the clinical significance of this distinction including prognostic relevance and the therapeutic implications.

Dasatinib synergizes with both cytotoxic and signal transduction inhibitors in heterogeneous breast cancer cell lines--lessons for design of combination targeted therapy

Molecularly targeted therapies have emerged as the leading theme in cancer therapeutics. Multi-cytotoxic drug regimens have been highly successful, yet many studies in targeted therapeutics have centered on a single agent. We investigated whether the Src/Abl kinase inhibitor dasatinib displays synergy with other agents in molecularly heterogeneous breast cancer cell lines. MCF-7, SKBR-3, and MDA-MB-231 display different signaling and gene signatures profiles due to expression of the estrogen receptor, ErbB2, or neither. Cell proliferation was measured following treatment with dasatinib±cytotoxic (paclitaxel, ixabepilone) or molecularly targeted agents (tamoxifen, rapamycin, sorafenib, pan PI3K inhibitor LY294002, and MEK/ERK inhibitor U0126). Dose-responses for single or combination drugs were calculated and analyzed by the Chou-Talalay method. The drugs with the greatest level of synergy with dasatinib were rapamycin, ixabepilone, and sorafenib, for the MDA-MB-231, MCF-7, and SK-BR-3 cell lines respectively. However, dasatinib synergized with both cytotoxic and molecularly targeted agents in all three molecularly heterogeneous breast cancer cell lines. These results suggest that effectiveness of rationally designed therapies may not entirely rest on precise identification of gene signatures or molecular profiling. Since a systems analysis that reveals emergent properties cannot be easily performed for each cancer case, multi-drug regimens in the near future will still involve empirical design.

Association of copy number loss of CDKN2B and PTCH1 with poor overall survival in patients with pulmonary squamous cell carcinoma

BACKGROUND AND PURPOSE

Although lung cancer is the leading cause of cancer deaths worldwide, reliable markers allowing prediction of patient survival at the time of initial diagnosis are still lacking. Copy number alterations (CNAs) in tumor tissue DNA have been associated with tumorigenesis and malignant progression. We aimed at identification of gene-level CNAs with prognostic value for survival in pulmonary squamous cell carcinoma (SCC).

METHODS

The CNA status of a panel of 44 genes was analyzed by high-resolution array comparative genomic hybridization (CGH) in 49 SCC samples. Overall survival information (median follow-up, 40 months) for the patients was collected and used to assess outcome correlations with gene CNAs.

RESULTS

Survival analysis showed that both CDKN2B loss and PTCH1 loss were associated with poor survival (both P < .001, log-rank test). Multivariate Cox analysis, including CDKN2B loss and PTCH1 loss as well as age, sex, cigarette smoking status, tumor size, tumor differentiation, and TNM stage showed that CDKN2B loss (hazard ratio [HR], 17.88; 95% confidence interval [CI], 4.40-72.67; P < .001) and PTCH1 loss (HR, 10.81; 95% CI, 1.92-60.98; P = .007) were independent prognostic factors for poor survival. In addition the PTCH1 loss was more frequently found in moderately or poorly differentiated tumors than in well-differentiated tumors (P = .007).

CONCLUSION

These findings suggest that 2 genes of loss, CDKN2B and PTCH1, are associated with poor overall survival in patients with SCC of the lung and may be useful as prognostic markers.

Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples

Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently identifies the globally optimal solution while preserving non-negativity of the fraction of the cells. We applied our method to various existing platforms to estimate proportions of different pure cell or tissue types and gene expression profilings of distinct phenotypes, with a focus on complex samples collected in clinical trials. We tested our methods on several well controlled benchmark data sets with known mixing fractions of pure cell or tissue types and mRNA expression profiling data from samples collected in a clinical trial. Accurate agreement between predicted and actual mixing fractions was observed. In addition, our method was able to predict mixing fractions for more than ten species of circulating cells and to provide accurate estimates for relatively rare cell types (<10% total population). Furthermore, accurate changes in leukocyte trafficking associated with Fingolomid (FTY720) treatment were identified that were consistent with previous results generated by both cell counts and flow cytometry. These data suggest that our method can solve one of the open questions regarding the analysis of complex transcriptional data: namely, how to identify the optimal mixing fractions in a given experiment.

Microarray-based cancer prediction using single genes

Background

Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the models and ease of translation to other assay platforms. We explored the use of single genes to construct classification models. We first identified the genes with the most powerful univariate class discrimination ability and then constructed simple classification rules for class prediction using the single genes.

Results

We applied our model development algorithm to eleven cancer gene expression datasets and compared classification accuracy to that for standard methods including Diagonal Linear Discriminant Analysis, k-Nearest Neighbor, Support Vector Machine and Random Forest. The single gene classifiers provided classification accuracy comparable to or better than those obtained by existing methods in most cases. We analyzed the factors that determined when simple single gene classification is effective and when more complex modeling is warranted.

Conclusions

For most of the datasets examined, the single-gene classification methods appear to work as well as more standard methods, suggesting that simple models could perform well in microarray-based cancer prediction.

Oncogenic K-Ras decouples glucose and glutamine metabolism to support cancer cell growth

A systems approach using 13C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling was applied to investigate the role of oncogenic K-Ras in metabolic transformation.

K-Ras transformed cells exhibit an increased glycolytic rate and lower flux through the oxidative tricarboxylic acid (TCA) cycle.

K-Ras transformed cells show a relative increase in glutamine anaplerosis and reductive TCA metabolism.

Transcriptional changes driven by oncogenic K-Ras suggest control nodes associated with the metabolic reprogramming of cancer cells.

The ras and myc oncogenes drive pleiotropic changes in cell signaling, nutrient uptake, and intracellular metabolism (Chiaradonna et al, 2006b; Yuneva et al, 2007; Wise et al, 2008; Vander Heiden et al, 2009). Mutated ras proteins, identified in 25% of human cancers (Bos, 1989; Downward, 2003), correlate with an increased rate of glucose consumption, lactate accumulation, altered expression of mitochondrial genes, increased ROS production, and reduced mitochondrial activity (Bos, 1989; Downward, 2003; Vizan et al, 2005; Chiaradonna et al, 2006a; Yun et al, 2009; Baracca et al, 2010; Weinberg et al, 2010). Furthermore, K-Ras transformed cancer cells are dependent upon glucose and glutamine availability, since their withdrawal induces apoptosis and cell-cycle arrest, respectively (Ramanathan et al, 2005; Telang et al, 2006; Yun et al, 2009). However, the precise metabolic effects downstream of oncogenic Ras signaling as well as the mechanisms by which intracellular glucose and glutamine metabolism change have not been completely elucidated.

In this report, we have investigated the reprogramming of central carbon metabolism in cancer cells and its regulation by the K-ras oncogene, applying a systems level approach using ¹³C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD), and transcriptional profiling. These data reveal a coordinated decoupling of glycolysis and the tricarboxylic acid (TCA) cycle. K-Ras transformed mouse and human cells exhibited a high glucose to lactate flux and relatively lower oxidative metabolism of pyruvate. Such changes were supported by increased expression of glycolytic genes as well as several pyruvate dehydrogenase kinases. In contrast to glucose, the contribution of glutamine carbon to TCA cycle intermediates through both oxidative and reductive metabolism was significantly increased upon K-Ras transformation. Despite this increase in glutamine anaplerosis, oxidative TCA flux was significantly decreased. Additionally, we observed elevated levels of glutamine-derived nitrogen in various biosynthetic metabolites in transformed cells, including amino acids, 5-oxoproline, and the nucleobase adenine. Consistent with these changes, we detected increased transcription of genes associated with glutamine metabolism and nucleotide biosynthesis in cells expressing oncogenic K-Ras.

Taken together, these findings indicate an important role of oncogenic K-Ras in cancer cell metabolism. The observed decoupling of glucose and glutamine metabolism enables the efficient utilization of both carbon and nitrogen from glutamine for biosynthetic processes. In accord with these alterations, oncogenic K-Ras induces gene expression changes that may drive this metabolic reprogramming. Finally, these results may enable the identification of metabolic and transcriptional targets throughout the network and allow more effective cancer therapies.

Oncogenes such as K-ras mediate cellular and metabolic transformation during tumorigenesis. To analyze K-Ras-dependent metabolic alterations, we employed ¹³C metabolic flux analysis (MFA), non-targeted tracer fate detection (NTFD) of ¹⁵N-labeled glutamine, and transcriptomic profiling in mouse fibroblast and human carcinoma cell lines. Stable isotope-labeled glucose and glutamine tracers and computational determination of intracellular fluxes indicated that cells expressing oncogenic K-Ras exhibited enhanced glycolytic activity, decreased oxidative flux through the tricarboxylic acid (TCA) cycle, and increased utilization of glutamine for anabolic synthesis. Surprisingly, a non-canonical labeling of TCA cycle-associated metabolites was detected in both transformed cell lines. Transcriptional profiling detected elevated expression of several genes associated with glycolysis, glutamine metabolism, and nucleotide biosynthesis upon transformation with oncogenic K-Ras. Chemical perturbation of enzymes along these pathways further supports the decoupling of glycolysis and TCA metabolism, with glutamine supplying increased carbon to drive the TCA cycle. These results provide evidence for a role of oncogenic K-Ras in the metabolic reprogramming of cancer cells.

Nanodevices in diagnostics

The real-time, personalized and highly sensitive early-stage diagnosis of disease remains an important challenge in modern medicine. With the ability to interact with matter at the nanoscale, the development of nanotechnology architectures and materials could potentially extend subcellular and molecular detection beyond the limits of conventional diagnostic modalities. At the very least, nanotechnology should be able to dramatically accelerate biomarker discovery, as well as facilitate disease monitoring, especially of maladies presenting a high degree of molecular and compositional heterogeneity. This article gives an overview of several of the most promising nanodevices and nanomaterials along with their applications in clinical practice. Significant work to adapt nanoscale materials and devices to clinical applications involving large interdisciplinary collaborations is already underway with the potential for nanotechnology to become an important enabling diagnostic technology.

Molecular diagnosis and prognosis with DNA microarrays

Microarray analysis makes it possible to determine thousands of gene expression values simultaneously. Changes in gene expression, as a response to diseases, can be detected allowing a better understanding and differentiation of diseases at a molecular level. By comparing different kinds of tissue, for example healthy tissue and cancer tissue, the microarray analysis indicates induced gene activity, repressed gene activity or when there is no change in the gene activity level. Fundamental patterns in gene expression are extracted by several clustering and machine learning algorithms. Certain kinds of cancer can be divided into subtypes, with different clinical outcomes, by their specific gene expression patterns. This enables a better diagnosis and tailoring of individual patient treatments.

Use of formalin-fixed and paraffin-embedded tissues for diagnosis and therapy in routine clinical settings

Formalin-fixed and paraffin-embedded (FFPE) tissues are used routinely everyday in hospitals world-wide for histopathological diagnosis of diseases like cancer. Due to formalin-induced cross-linking of proteins, FFPE tissues present a particular challenge for proteomic analysis. Nevertheless, there has been recent progress for extraction-based protein analysis in these tissues. Novel tools developed in the last few years are urgently needed because precise protein biomarker quantification in clinical FFPE tissues will be crucial for treatment decisions and to assess success or failure of current and future personalized molecular therapies. Furthermore, they will help to conceive why only a subset of patients responds to individualized treatments. Reverse phase protein array (RPPA) is a very promising new technology for quick and simultaneous analysis of many patient samples allowing relative and absolute protein quantifications. In this chapter, we show how protein extraction from FFPE tissues might facilitate the implementation of RPPA for therapy decisions and discuss challenges for application of RPPA in clinical trials and routine settings.

Effect of Oxymatrine Combined with Low Dose 5-FU on Lymphatic Vessel and Microvascular Endothelial Cell Growth of Gastric Cancer in a Severe Combined Immunodeficient Mouse Orthotopic Implantation Model

In this study, we explored the effect of Oxymatrine combined with low dose 5-Fu on lymphatic vessel and vascular endothelial growth factor of orthotopic implantated gastric cancer in severe combined immunodeficient (SCID) nude mice. Human gastric cancer cell line SGC-7901 was orthotopically implanted into the gastric tract of nude mice. Nude mice were treated with normal saline (control group), low dose 5-Fu, oxymatrine, oxymatrine combined with low dose 5-Fu using intraperitoneal injection. The expression of LVD, VEGF-C, VEGF-D, VEGF-R-3 and their Ct were analyzed in a severe combined immunodeficient mouse orthotopic implantatation gastric cancer model. We found that oxymatrine combined with low dose 5-Fu could decrease LVD and inhibit VEGF expression by a synergistic effect in SCID nude mouse orthotopic implantatation gastric cancer model.

Reverse phase protein microarrays advance to use in clinical trials

Individualizing cancer therapy for molecular targeted inhibitors requires a new class of molecular profiling technology that can map the functional state of the cancer cell signal pathways containing the drug targets. Reverse phase protein microarrays (RPMA) are a technology platform designed for quantitative, multiplexed analysis of specific phosphorylated, cleaved, or total (phosphorylated and non-phosphorylated) forms of cellular proteins from a limited amount of sample. This class of microarray can be used to interrogate tissue samples, cells, serum, or body fluids. RPMA were previously a research tool; now this technology has graduated to use in research clinical trials with clinical grade sensitivity and precision. In this review we describe the application of RPMA for multiplexed signal pathway analysis in therapeutic monitoring, biomarker discovery, and evaluation of pharmaceutical targets, and conclude with a summary of the technical aspects of RPMA construction and analysis.

Multiplexed detection and characterization of rare tumor cells in Hodgkin's lymphoma with multicolor quantum dots

The multicolor and multiplexing capabilities of semiconductor quantum dots (QDs) are most promising for improving the sensitivity and specificity of in vitro molecular and cellular diagnostics. Here, we report the use of multiplexed QDs and wavelength-resolved imaging to detect and characterize a class of low-abundant tumor cells in Hodgkin's lymphoma. Known as the Hodgkin's and Reed-Sternberg (HRS) cells, this class of malignant cells is a pathological hallmark in clinical diagnosis, but it comprises only about 1% of the heterogeneous infiltrating cells in lymph node tissues. To overcome this cellular heterogeneity and rarity problem, we have developed multicolor QD-antibody conjugates to simultaneously detect a panel of four protein biomarkers (CD15, CD30, CD45, and Pax5) directly on human tissue biopsies. This multiplexing approach allows rapid detection and differentiation of rare HRS cells from infiltrating immune cells such as T and B lymphocytes. We have also carried out clinical translation studies involving six confirmed Hodgkin's lymphoma patients, two suspicious lymphoma cases, and two patients with reactive lymph nodes (but not lymphoma). The results indicate that a distinct QD staining pattern (CD15 positive, CD30 positive, CD45 negative, and Pax5 positive) can be used to not only detect Hodgkin's lymphoma but also differentiate it from benign lymphoid hyperplasia.

The role of gene expression in ecological speciation

Ecological speciation is the process by which barriers to gene flow between populations evolve due to adaptive divergence via natural selection. A relatively unexplored area in ecological speciation is the role of gene expression. Gene expression may be associated with ecologically important phenotypes not evident from morphology and play a role during colonization of new environments. Here we review two potential roles of gene expression in ecological speciation: (1) its indirect role in facilitating population persistence and (2) its direct role in contributing to genetically based reproductive isolation. We find indirect evidence that gene expression facilitates population persistence, but direct tests are lacking. We also find clear examples of gene expression having effects on phenotypic traits and adaptive genetic divergence, but links to the evolution of reproductive isolation itself remain indirect. Gene expression during adaptive divergence seems to often involve complex genetic architectures controlled by gene networks, regulatory regions, and “eQTL hotspots.” Nonetheless, we review how approaches for isolating the functional mutations contributing to adaptive divergence are proving to be successful. The study of gene expression has promise for increasing our understanding ecological speciation, particularly when integrative approaches are applied.

Gene selection for multiclass prediction by weighted Fisher criterion

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gene subsets for accurate classification of multiclass phenotypes. In the first step, individually discriminatory genes (IDGs) are identified by using one-dimensional weighted Fisher criterion (wFC). In the second step, jointly discriminatory genes (JDGs) are selected by sequential search methods, based on their joint class separability measured by multidimensional weighted Fisher criterion (wFC). The performance of the selected gene subsets for multiclass prediction is evaluated by artificial neural networks (ANNs) and/or support vector machines (SVMs). By applying the proposed IDG/JDG approach to two microarray studies, that is, small round blue cell tumors (SRBCTs) and muscular dystrophies (MDs), we successfully identified a much smaller yet efficient set of JDGs for diagnosing SRBCTs and MDs with high prediction accuracies (96.9% for SRBCTs and 92.3% for MDs, resp.). These experimental results demonstrated that the two-step gene selection method is able to identify a subset of highly discriminative genes for improved multiclass prediction.

Genomic alterations of chromosome region 11p as predictive marker by array comparative genomic hybridization in lung adenocarcinoma patients

Array comparative genomic hybridization (aCGH) provides a method to quantitatively measure the changes of DNA copy number with an extremely high resolution and to map them directly onto the complete linear genome sequences. In this study, we used aCGH to compare genomic alterations in fresh-frozen lung cancer tissues of 21 adenocarcinomas (AdCCs) (11 early relapse and 10 nonrelapse) and identified genomic alterations that showed significant by different frequency between early relapse and nonrelapse AdCCs. Twelve clones were identified by the false discovery rate (FDR) test, and Kaplan-Meier analyses were selected as predictive markers. The significant gain clones were found in 11p (11p15.4, 11p15.1, and 11p13). When the cutoff value was 2, study of the association between candidate clones and relapse prediction revealed that early relapse and nonrelapse groups were most effectively separated. To further validate the gain of chromosome 11p region that was identified by array CGH, fluorescence in situ hybridization (FISH) was performed. To further confirm the results of aCGH, copy number changes of cancer-related candidate genes in AdCC patients were compared by real-time quantitative polymerase chain reaction. Array CGH and real-time quantitative polymerase chain reaction data were found to correspond to delineated DNA copy number changes. Genomic alterations of chromosome 11p region in AdCC patients were observed with aCGH, and a relapsable marker was identified in the nonrelapse group. This marker could be useful in stratifying patient groups according to likelihood of relapse for adjuvant treatment after surgical resection.

Analysis of multiple sarcoma expression datasets: implications for classification, oncogenic pathway activation and chemotherapy resistance

Background

Diagnosis of soft tissue sarcomas (STS) is challenging. Many remain unclassified (not-otherwise-specified, NOS) or grouped in controversial categories such as malignant fibrous histiocytoma (MFH), with unclear therapeutic value. We analyzed several independent microarray datasets, to identify a predictor, use it to classify unclassifiable sarcomas, and assess oncogenic pathway activation and chemotherapy response.

Methodology/Principal Findings

We analyzed 5 independent datasets (325 tumor arrays). We developed and validated a predictor, which was used to reclassify MFH and NOS sarcomas. The molecular “match” between MFH and their predicted subtypes was assessed using genome-wide hierarchical clustering and Subclass-Mapping. Findings were validated in 15 paraffin samples profiled on the DASL platform. Bayesian models of oncogenic pathway activation and chemotherapy response were applied to individual STS samples. A 170-gene predictor was developed and independently validated (80-85% accuracy in all datasets). Most MFH and NOS tumors were reclassified as leiomyosarcomas, liposarcomas and fibrosarcomas. “Molecular match” between MFH and their predicted STS subtypes was confirmed both within and across datasets. This classification revealed previously unrecognized tissue differentiation lines (adipocyte, fibroblastic, smooth-muscle) and was reproduced in paraffin specimens. Different sarcoma subtypes demonstrated distinct oncogenic pathway activation patterns, and reclassified MFH tumors shared oncogenic pathway activation patterns with their predicted subtypes. These patterns were associated with predicted resistance to chemotherapeutic agents commonly used in sarcomas.

Conclusions/Significance

STS profiling can aid in diagnosis through a predictor tracking distinct tissue differentiation in unclassified tumors, and in therapeutic management via oncogenic pathway activation and chemotherapy response assessment.

Molecular profiling of signalling pathways in formalin-fixed and paraffin-embedded cancer tissues

In most hospitals word-wide, histopathological cancer diagnosis is currently based on formalin-fixed and paraffin-embedded (FFPE) tissues. In the last few years new approaches and developments in patient-tailored cancer therapy have raised the need to select more precisely those patients, who will respond to personalised treatments. The most efficient way for optimal therapy and patient selection is probably to provide a tumour-specific protein network portrait prior to treatment. The discovery and characterisation of deregulated signalling molecules (e.g. human epidermal growth factor receptor 2, mitogen-activated protein kinases) are very promising candidates for the identification of new suitable therapy targets and for the selection of those patients who will receive the greatest benefit from individualised treatments. The reverse phase protein array (RPPA) is a promising new technology that allows quick, precise and simultaneous analysis of many components of a network. Importantly it requires only limited amounts of routine clinical material (e.g. FFPE biopsies) and can be used for absolute protein measurements. We and other research groups have described successful protein extraction from routine FFPE tissues. In this manuscript we show how these recent developments might facilitate the implementation of RPPA in clinical trials and routine settings.

Alterations of the extracellular matrix in ovarian cancer studied by Second Harmonic Generation imaging microscopy

Background

Remodeling of the extracellular matrix (ECM) has been implicated in ovarian cancer, and we hypothesize that these alterations may provide a better optical marker of early disease than currently available imaging/screening methods and that understanding their physical manifestations will provide insight into invasion.

Methods

For this investigation we use Second Harmonic Generation (SHG) imaging microcopy to study changes in the structure of the ovarian ECM in human normal and malignant ex vivo biopsies. This method directly visualizes the type I collagen in the ECM and provides quantitative metrics of the fibrillar assembly. To quantify these changes in collagen morphology we utilized an integrated approach combining 3D SHG imaging measurements and bulk optical parameter measurements in conjunction with Monte Carlo simulations of the experimental data to extract tissue structural properties.

Results

We find the SHG emission attributes (directionality and relative intensity) and bulk optical parameters, both of which are related to the tissue structure, are significantly different in the tumors in a manner that is consistent with the change in collagen assembly. The normal and malignant tissues have highly different collagen fiber assemblies, where collectively, our findings show that the malignant ovaries are characterized by lower cell density, denser collagen, as well as higher regularity at both the fibril and fiber levels. This further suggests that the assembly in cancer may be comprised of newly synthesized collagen as opposed to modification of existing collagen.

Conclusions

Due to the large structural changes in tissue assembly and the SHG sensitivity to these collagen alterations, quantitative discrimination is achieved using small patient data sets. Ultimately these measurements may be developed as intrinsic biomarkers for use in clinical applications.

Potential role of vascular targeted therapy to combat against tumor

Tumors, like other tissues, have a fundamental requirement for access to the nutrients, oxygen and waste removal functions of the circulatory system. Vascular targeted therapy exploits this basic need, along with molecular heterogeneity observed between normal and tumor blood vessels, to develop efficient and selective chemotherapies that essentially starve tumors by destroying their vasculature. As the basic principle on which this therapy is based differs from agents that directly target cancerous cells, combining it with traditional therapies such as radiation, surgery and existing chemotherapies has the potential to create powerful new anticancer strategies. As the requirement for vascularization is universal to solid tumors, vascular targeted therapies have the potential for broad applicability. Vascular targeted therapies include both angiogenesis inhibitors, which inhibit neovascularization, and vascular disrupting agents, which destroy existing vasculature. Applications of this model include finding peptides that bind specifically to cell surface markers on tumor vessel endothelial cells and might deliver chemotherapeutic agents. Expression profiling with microarrays, serial analysis of gene expression, and in vitro and in vivo screening of phage display libraries have identified candidate peptides for targeted delivery to the tumor endothelium.

A minimally invasive microdevice for molecular sampling and analysis

In this paper, we present a new minimally invasive biopsy microdevice adapted to proteomic mass spectrometry analysis. The concept is born from a multidisciplinary collaboration in fields of proteomics, cancer research, and microtechnology. In mixing different skills, we have developed and manufactured a miniaturized biopsy device using microtechnology techniques in order to minimize tissue damage during surgical gesture. Dedicated chemically functionalized areas were added to the device in order to increase capture yield and specificity during tissue contact. Fields of application range from cancer research to the study of neurodegenerative diseases.

ISOLATE: a computational strategy for identifying the primary origin of cancers using high-throughput sequencing

Motivation: One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers.

Results: We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin.

Availability: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software

Contact:gerald.quon@utoronto.ca; quaid.morris@utoronto.ca

Supplementary information:Supplementary data are available at Bioinformatics online.

Mass Spectrometry in Diagnostic Oncoproteomics

Diagnostic oncoproteomics is the application of proteomic techniques for the diagnosis of malignancies. A new mass spectrometric technology involves surface enhanced laser desorption ionization combined with time-of flight mass analysis (SELDI-TOF-MS), using special protein chips. After the description of the relevant principles of the technique, including approaches to proteomic pattern diagnostics, applications are reviewed for the diagnosis of ovarian, breast, prostate, bladder, pancreatic, and head and neck cancers, and also several other malignancies. Finally, problems and prospects of the approach are discussed.

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individual's molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma

Among the adult population, renal cell carcinoma (RCC) constitutes the most prevalent form of kidney neoplasm. Unfortunately, RCC is relatively asymptomatic and there are no tumor markers available for diagnostic, prognostic or predictive purposes. Molecular profiling, the global analysis of gene and protein expression profiles, is an emerging promising tool for new biomarker identification in RCC. In this review, we summarize the existing knowledge on RCC regarding clinical presentation, treatment options, and tumor marker status. We present a general overview of the more commonly used approaches for molecular profiling at the genomic, transcriptomic and proteomic levels. We also highlight the emerging role of molecular profiling as not only revolutionizing the process of new tumor marker discovery, but also for providing a better understanding of the pathogenesis of RCC that will pave the way towards new targeted therapy discovery. Furthermore, we discuss the spectrum of clinical applications of molecular profiling in RCC in the current literature. Finally, we highlight some of the potential challenging that faces the era of molecular profiling and its transition into clinical practice, and provide an insight about the future perspectives of molecular profiling in RCC.

Fiber-optic microsphere-based antibody array for the analysis of inflammatory cytokines in saliva

Antibody microarrays have emerged as useful tools for high-throughput protein analysis and candidate biomarker screening. We describe here the development of a multiplexed microsphere-based antibody array capable of simultaneously measuring 10 inflammatory protein mediators. Cytokine-capture microspheres were fabricated by covalently coupling monoclonal antibodies specific for cytokines of interest to fluorescently encoded 3.1 microm polymer microspheres. An optical fiber bundle containing approximately 50,000 individual 3.1 microm diameter fibers was chemically etched to create microwells in which cytokine-capture microspheres could be deposited. Microspheres were randomly distributed in the wells to produce an antibody array for performing a multiplexed sandwich immunoassay. The array responded specifically to recombinant cytokine solutions in a concentration-dependent fashion. The array was also used to examine endogenous mediator patterns in saliva supernatants from patients with pulmonary inflammatory diseases such as asthma and chronic obstructive pulmonary disease (COPD). This array technology may prove useful as a laboratory-based platform for inflammatory disease research and diagnostics, and its small footprint could also enable integration into a microfluidic cassette for use in point-of-care testing.

Reverse-phase protein lysate microarrays for cell signaling analysis

'Reverse-phase' protein lysate microarray (RPA) assays use micro-scale, cell lysate dot blots that are printed to a substrate, followed by quantitative immunochemical protein detection, known to be particularly effective across many samples. Large-scale sample collection is a labor-intensive and time-consuming process; the information yielded from RPA assays, however, provides unique opportunities to experimentally interpret theoretical protein networks quantitatively. When specific antibodies are used, RPA can generate 1,000 times more data points using 10,000 times less sample volume than an ordinary western blot, enabling researchers to monitor quantitative proteomic responses for various time-scale and input-dose gradients simultaneously. Hence, the RPA system can be an excellent method for experimental validation of theoretical protein network models. Besides the initial screening of primary antibodies, collection of several hundreds of sample lysates from 1- to 8-h periods can be completed in approximately 10 d; subsequent RPA printing and signal detection steps require an additional 2-3 d.

Transfer and multiplex immunoblotting of a paraffin embedded tissue

In the functional proteome era, the proteomic profiling of clinicopathologic annotated tissues is an essential step for mining and evaluations of candidate biomarkers for disease. Previously, application of routine proteomic methodologies to clinical tissue specimens has provided unsatisfactory results. Multiplex tissue immunoblotting is a method of transferring proteins from a formalin-fixed, paraffin-embedded tissue section to a stack of membranes which can be applied to a conventional immunoblotting method. A single tissue section can be transferred to up to ten membranes, each of which is probed with antibodies and detected with fluorescent tags. By this approach, total protein and target signals can be simultaneously determined on each membrane; hence each antibody is internally normalized. Phosphorylation-specific antibodies as well as antibodies that do not readily work well with paraffin-embedded tissue are applicable to the membranes, expanding the menu of antibodies that can be utilized with formalin-fixed tissue. This novel platform can provide quantitative detection retaining histomorphologic detail in clinical samples and has great potential to facilitate discovery and development of new diagnostic assays and therapeutic agents.

Gene profiles between non-invasive and invasive colon cancer using laser microdissection and polypeptide analysis

AIM: To explore the expression of differential gene expression profiles of target cell between non-invasive submucosal and invasive advanced tumor in colon carcinoma using laser microdissection (LMD) in combination with polypeptide analysis.

METHODS: Normal colon tissue samples from 20 healthy individuals and 30 cancer tissue samples from early non-invasive colon cancer cells were obtained. The cells from these samples were used LMD independently after P27-based amplification. aRNA from advanced colon cancer cells and metastatic cancer cells of 40 cases were applied to LMD and polypeptide analysis, semiquantitative reverse transcribed polymerase chain reaction (RT-PCR) and immunohistochemical assays were used to verify the results of microarray and further identify differentially expressed genes in non-invasive early stages of colon cancer.

RESULTS: Five gene expressions were changed in colon carcinoma cells compared with that of controls. Of the five genes, three genes were downregulated and two were upregulated in invasive submucosal colon carcinoma compared with non-invasive cases. The results were confirmed at the level of aRNA and gene expression. Five genes were further identified as differentially expressed genes in the majority of cases (> 50%, 25/40) in progression of colon cancer, and their expression patterns of which were similar to tumor suppressor genes or oncogenes.

CONCLUSION: This study suggested that combined use of polypeptide analysis might identify early expression profiles of five differential genes associated with the invasion of colon cancer. These results reveal that this gene may be a marker of submucosal invasion in early colon cancer.

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.

Biomarker validation: in situ analysis of protein expression using semiquantitative immunohistochemistry-based techniques

Biomarker-driven cancer research is common in the current literature. Much of this research is a result of the increase in genomic and proteomic high-throughput technologies, which have increased our knowledge and also produced an abundance of data with unclear clinical significance. Immunohistochemistry-based assessment of protein expression is a natural validation method of expression-profiling data that is easily performed on tissue samples collected prospectively or from archived samples. Coupled with tissue microarray technology and the increasing number of available automated, quantitative systems to read these arrays, we now have an efficient method of validating biomarkers for prognostic and predictive capabilities and for the identification of drug development targets.

Overlapping gene expression profiles of cell migration and tumor invasion in human bladder cancer identify metallothionein 1E and nicotinamide N-methyltransferase as novel regulators of cell migration

Cell migration is essential to cancer invasion and metastasis and is spatially and temporally integrated through transcriptionally dependent and independent mechanisms. As cell migration is studied in vitro, it is important to identify genes that both drive cell migration and are biologically relevant in promoting invasion and metastasis in patients with cancer. Here, gene expression profiling and a high-throughput cell migration system answers this question in human bladder cancer. In vitro migration rates of 40 microarray-profiled human bladder cancer cell lines were measured by radial migration assay. Genes whose expression was either directly or inversely associated with cell migration rate were identified and subsequently evaluated for their association with cancer stage in 61 patients. This analysis identified genes known to be associated with cell invasion such as versican, and novel ones, including metallothionein 1E (MT1E) and nicotinamide N-methyltransferase (NNMT), whose expression correlated positively with cancer cell migration and tumor stage. Using loss of function analysis, we show that MT1E and NNMT are necessary for cancer cell migration. These studies provide a general approach to identify the clinically relevant genes in cancer cell migration and mechanistically implicate two novel genes in this process in human bladder cancer.

Cancer and the web

The applications of functional genomics, proteomics and informatics to cancer research have yielded a tremendous amount of information, which is growing all the time. Much of this information is available publicly on the Internet and ranges from general information about different cancers from a patient or clinical viewpoint, through to databases suitable for cancer researchers of all backgrounds, to very specific sites dedicated to individual genes or molecules. A simple search for 'cancer' from a typical Web browser search engine yields more than half a million hits; an even more specific search for 'leukaemia' (>40 000 hits) or 'p53' (>5700 hits) yields far too many hits to allow one to identify particular sites of interest. This review aims to provide a brief guide to some of the resources and databases that can be used as springboards to home in rapidly on information relevant to many fields of cancer research. As such, this article will not focus on a single website but hopes to illustrate some of the ways that postgenomic biology is revolutionizing cancer research. It will cover genomics and proteomics approaches that have been applied to studying global expression patterns in cancers, in addition to providing links ranging from general information about cancer to specific cancer gene mutation databases.

Advances in tumor-related molecular phenotypes and personalized therapy

Malignant tumor impairs human's health severely. Although progresses have been made in tumor's etiology, clinical management of tumor is still confronted by challenges. The standard treatments against tumor, including surgery, chemotherapy and radiotherapy, are not effective for all patients. The clinicians often feel confused while selecting appropriate therapeutic pattern and judging patients' prognosis. The clinical outcome of target-protein-based cancer therapy revealed that personalized molecular signature determined individualized curative effect. Effective management of tumor needs patient-tailored design. The so-called "omic" researches accelerate the studies in specific molecule phenotype of tumor cells, and the personalized therapy against such individualized malignant phenotype will represent the future direction in tumor treatment.

Automated laser capture microdissection for tissue proteomics

Laser Capture Microdissection (LCM) is a technique for isolating pure cell populations from a heterogeneous tissue section or cytological preparation through direct visualization of the cells. This technique is applicable to molecular profiling of diseased and disease-free tissue, permitting correlation of cellular molecular signatures with specific cell populations. DNA, RNA, or protein analysis may be performed with the microdissected tissue by any method with adequate sensitivity.Automated LCM platforms combine graphical user interfaces and annotation software for visualization of the tissue of interest in addition to robotically controlled microdissection. The principal components of LCM technology are (1) visualization of the cells of interest through microscopy, (2) transfer of laser energy to a thermolabile polymer with formation of a polymer-cell composite, and (3) removal of the cells of interest from the heterogeneous tissue section. Automated LCM is compatible with a variety of tissue types, cellular staining methods, and tissue preservation protocols allowing microdissection of fresh or archival specimens in a high-throughput manner. This protocol describes microdissection techniques compatible with downstream proteomic analyses.

Alterations in Barrett's-related adenocarcinomas: a proteomic approach

In this study, we applied high-resolution, two-dimensional, gel electrophoresis and matrix-assisted laser desorption/ionization, time-of-flight and tandem mass spectrometry analysis (MALDI TOF MS) to identify novel proteins that are involved in Barrett's tumorigenesis. We analyzed 12 primary tissue samples that included 8 Barrett's-related adenocarcinomas (BA) and 4 normal mucosae samples. Twenty-three spots were consistently altered (>or=2-fold) in at least half of the tumors when compared with all normal samples and thus subjected to further analysis. The MALDI TOF MS analysis demonstrated biologically interesting upregulated proteins such as ErbB3, Dr5 and Cyclin D1 as well as several members of the zinc finger proteins (Znf146, Znf212 and Znf363). Examples of downregulated proteins included Lgi1 and Klf6. We selected four proteins (ErbB3, Dr5, Znf146 and Lgi1) that are novel for BAs for validation using quantitative real-time reverse-transcription PCR on 39 BA tissue samples when compared with normal samples. We demonstrated mRNA upregulation of ERBB3 (51.3%), DR5 (41%) and ZNF146 (30.7%) and downregulation of LGI1 (100%) in BA. We have further validated the protein overexpression of ErbB3, Dr5 and Znf146, using immunohistochemical (IHC) analysis on a tissue microarray that contained 75 BAs and normal gastric and esophageal mucosae samples. BA tissue samples demonstrated overexpression of ErbB3 (42%), Dr5 (90%) and Znf146 (30%) when compared with normal tissues. In conclusion, we have identified and validated several novel proteins that are involved in Barrett's carcinogenesis.

Identification of gene signatures for invasive colorectal tumor cells

BACKGROUND

Gene signatures of sporadic colorectal carcinoma tissues and microdissected colorectal tumor cells were analyzed to identify stromal and tumor cell-specific markers, respectively.

METHODS

Serial sections of frozen colorectal tumors (n=29) were subjected to RNA isolation of (1) entire tissue sections with a various tumor cell content and of (2) microdissected invasive tumor cells. Three matching samples of microdissected normal colorectal epithelial and invasive tumor cells were similarly obtained. RNA samples were analyzed using the HG95A and HG95Av2 GeneChip microarrays (Affymetrix). The microarray data was evaluated by established methods and validated by Q-RT-PCR.

RESULTS

Unsupervised hierarchical cluster analysis of 18 sample pairs (training set) clearly distinguished tumors from microdissected tumor cells. A 149-gene signature was identified using statistical methods, which was then validated by a hierarchical clustering analysis of 11 independent sample pairs (test set). Genes specifically associated with microdissected invasive tumor cells were for example CKS2 and NME1. In contrast, genes associated with stromal cells were for example MMP2, SDF1 and FBLN2. Finally, a 65-gene signature distinguished normal colorectal epithelial cells and invasive tumor cells, including down-regulation of BMP2 and ANPEP mRNA expression as well as up-regulation of TKT, SPARC, MCM5 mRNA expression.

CONCLUSIONS

Our approach allowed precise evaluation of molecular signatures in morphologically defined cell populations and identified novel target genes related to stroma-tumor interactions in colorectal cancer. The approach enables further analysis of gene signatures in different tumor areas and cell types, such as within invasive margins to decipher molecular mechanisms of colorectal cancer invasion and metastasis.

Transcriptional regulation of human protease-activated receptor 1: a role for the early growth response-1 protein in prostate cancer

Transcriptional regulation plays a central role in the molecular pathways underlying preferential cancer growth and metastasis. In the present study, we investigated the regulation of human protease-activated receptor 1 (hPar1) gene overexpression in the malignant androgen hormone-resistant phase. We found increased hPar1 RNA chain elongation and no change in message stability in cells with high levels of PAR1 expression, indicating that increased transcription is largely responsible for the overexpression of hPar1 in prostate tumor progression. Enforced expression of early growth response-1 (Egr-1) plasmid markedly enhanced luciferase activity driven by the hPar1 promoter. The neuroendocrine peptide bombesin significantly induced hPar1 expression and increased the ability of the cells to invade Matrigel, an effect abolished by expression of hPar1 small interfering RNA, showing the importance of hPAR1 in invasion. Bombesin also markedly enhanced Egr-1 binding to the hPar1 promoter in vivo and in vitro. These data suggest that bombesin enhances Egr-1 expression leading to increased hPar1 transcription, thereby increasing PAR1 expression and function. Immunohistostaining of prostate tissue biopsy specimens revealed a direct correlation between the degree of prostate cancer malignancy, PAR1 expression, and EGR-1 expression. Altogether, we show that transcriptional regulation of hPar1 in the aggressive hormone-resistant prostate cancer stage is controlled in part by the transcription factor Egr-1 and may play a central role in invasiveness, an important indicator of malignancy.

Tumors caused by overexpression and forced activation of Stat5 in mammary epithelial cells of transgenic mice are parity-dependent and developed in aged, postestropausal females

In transgenic mice overexpressing Stat5 or a constitutively activated Stat5 variant (STAT5ca), we show for the first time that parity is required for the development of tumors in postestropausal females. Tumors were detected in glands of multiparous transgenic female mice after latency period of 14 months, but rarely in their age-matched virgin (AMV) counterparts. This period was not affected by distinguishable tumor pathologies and was not dependent upon transgenic Stat5 variant. To associate Stat5 deregulation, parity and the postestropausal tumor occurrence with mammary cancer formation, the activities of endogenous and transgenic Stat5 were measured in the glands of aged multiparous and AMV females. No differences in phosphorylated Stat5 (pStat5) levels were found between the 2 cohorts. However, promoter sequences comprising the Stat5 binding sites from the cyclin D1 or the bcl-x genes associate differentially with acetylated histone H4 in aged multiparous and AMV STAT5ca transgenic females. Individual epithelial cells varied greatly with respect to the presence of nuclear pStat5. A small subset of epithelial cells, in which pStat5 and cyclin D1 were co-expressed, was exclusively present in the multiparous glands. Changes in chromatin structure might persist past the reproductive life time of the multiparous mice and contribute to the transcription of the cyclin D1 gene by activated Stat5. This may cause the detectable expression of cyclin D1 and add to the process of tumorigenesis.