Global search for chromosomal abnormalities in infiltrating ductal carcinoma of the breast using array-comparative genomic hybridization

Vitamin D receptor (VDR) and metabolizing enzymes CYP27B1 and CYP24A1 in breast cancer

Vitamin D Receptor (VDR), a nuclear steroid receptor, is a transcription factor with a primary physiologic role in calcium metabolism. It has also a physiologic role in breast tissues during development of the gland and postpartum. In addition, it is commonly expressed in breast cancer and has tumor suppressive effects. Cytochrome enzymes CYP27B1 and CYP24A1 that perform the final conversion of the circulating form of vitamin D, 25-hydroxyvitamin D (25-OHD) to the active VDR ligand, 1a,25-dihydroxyvitamin D and the catabolism of it to inactive 24,25-dihydroxyvitamin D, respectively, are also expressed in breast cancer tissues. Defective regulation of the receptor and the metabolic enzymes of VDR ligand is prevalent in breast cancer and leads to decreased VDR signaling. The expression and molecular defects of VDR, CYP27B1 and CYP24A1 that perturb physiologic function, the implications for breast cancer progression and therapeutic opportunities are discussed in this paper.

The epigenetic modifier JMJD6 is amplified in mammary tumors and cooperates with c-Myc to enhance cellular transformation, tumor progression, and metastasis

Background

Oncogene overexpression in primary cells often triggers the induction of a cellular safeguard response promoting senescence or apoptosis. Secondary cooperating genetic events are generally required for oncogene-induced tumorigenesis to overcome these biologic obstacles. We employed comparative genomic hybridization for eight genetically engineered mouse models of mammary cancer to identify loci that might harbor genes that enhance oncogene-induced tumorigenesis.

Results

Unlike many other mammary tumor models, the MMTV-Myc tumors displayed few copy number variants except for amplification of distal mouse chromosome 11 in 80 % of the tumors (syntenic to human 17q23-qter often amplified in human breast cancer). Analyses of candidate genes located in this region identified JMJD6 as an epigenetic regulatory gene that cooperates with Myc to enhance tumorigenesis. It suppresses Myc-induced apoptosis under varying stress conditions through inhibition of p19ARF messenger RNA (mRNA) and protein, leading to reduced levels of p53. JMJD6 binds to the p19ARF promoter and exerts its inhibitory function through demethylation of H4R3me2a. JMJD6 overexpression in MMTV-Myc cell lines increases tumor burden, induces EMT, and greatly enhances tumor metastasis. Importantly, we demonstrate that co-expression of high levels of JMJD6 and Myc is associated with poor prognosis for human ER+ breast cancer patients.

Conclusions

A novel epigenetic mechanism has been identified for how JMJD6 cooperates with Myc during oncogenic transformation. Combined high expression of Myc and JMJD6 confers a more aggressive phenotype in mouse and human tumors. Given the pleiotropic pro-tumorigenic activities of JMJD6, it may be useful as a prognostic factor and a therapeutic target for Myc-driven mammary tumorigenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-016-0205-6) contains supplementary material, which is available to authorized users.

Breast cancer and protein biomarkers

Breast cancer is a healthcare concern of women worldwide. Despite procedures being available for diagnosis, prognosis and treatment of breast cancer, researchers are working intensively on the disease in order to improve the life quality of breast cancer patients. At present, there is no single treatment known to bring a definite cure for breast cancer. One of the possible solutions for combating breast cancer is through identification of reliable protein biomarkers that can be effectively used for early detection, prognosis and treatments of the cancer. Therefore, the task of identification of biomarkers for breast cancer has become the focus of many researchers worldwide.

Navigating breast cancer: axon guidance molecules as breast cancer tumor suppressors and oncogenes

Slit, Netrin, Ephrin, and Semaphorin's roles in development have expanded greatly in the past decade from their original characterization as axon guidance molecules (AGMs) to include roles as regulators of tissue morphogenesis and development in diverse organs. In the mammary gland, AGMs are important for maintaining normal cell proliferation and adhesion during development. The frequent dysregulation of AGM expression during tumorigenesis and tumor progression suggests that AGMs also play a crucial role as tumor suppressors and oncogenes in breast cancer. Moreover, these findings suggest that AGMs may be excellent targets for new breast cancer prognostic tests and more effective therapeutic strategies.

Exploratory factor analysis of pathway copy number data with an application towards the integration with gene expression data

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression.

Emerging applications of fluorescent nanocrystals quantum dots for micrometastases detection

The occurrence of metastases is one of the main causes of death in many cancers and the main cause of death for breast cancer patients. Micrometastases of disseminated tumour cells and circulating tumour cells are present in more than 30% of breast cancer patients without any clinical or even histopathological signs of metastasis. Low abundance of these cell types in clinical diagnostic material dictates the necessity of their enrichment prior to reliable detection. Current micrometastases detection techniques are based on immunocytochemical and molecular methods suffering from low efficiency of tumour cells enrichment and observer-dependent interpretation. The use of highly fluorescent semiconductor nanocrystals, also known as "quantum dots" and nanocrystal-encoded microbeads tagged with a wide panel of antibodies against specific tumour markers offers unique possibilities for ultra-sensitive micrometastases detection in patients' serum and tissues. The nanoparticle-based diagnostics provides an opportunity for highly sensitive parallel quantification of specific proteins in a rapid and low-cost method, thereby providing a link between the primary tumour and the micrometastases for early diagnosis.

Preprocessing and downstream analysis of microarray DNA copy number profiles

Analysis of DNA copy number profiles requires methods tailored to the specific nature of these data. The number of available data analysis methods has grown enormously in the last 5 years. We discuss the typical characteristics of DNA copy number data, as measured by microarray technology and review the extensive literature on preprocessing methods such as segmentation and calling. Subsequently, the focus narrows to applications of DNA copy number in cancer, in particular, several downstream analyses of multi-sample data sets such as testing, clustering and classification. Finally, we look ahead: what should we prepare for and which methodology-related topics may deserve attention in the near future?

Unraveling breast cancer heterogeneity through transcriptomic and epigenomic analysis

Breast cancer diversity is histologically evident as various proliferative benign lesions, in situ carcinomas, and invasive carcinomas that may develop into distant metastases. Breast tumor molecular subtypes have been defined by genome-wide expression microarray technology and reveal associations between genetic alterations and the malignant phenotype. Early work has been conducted to use subtype-specific biomarkers to elucidate targeted treatment options early in the course of breast cancer progression. Additionally, DNA methylation is an epigenetic modification that contributes to breast cancer progression by transcriptionally silencing certain tumor suppressor genes. Among the genes characterized as targets for silencing are well-established tumor suppressors such as RASSF1A, RARB, SFN, and TGM2. Measuring elevated gene copy number and aberrant gene promoter methylation can further facilitate characterization of breast tumor molecular subtype; however, profiling of breast tumors based on epigenetic criteria has yet to be established. Epigenomic analysis has been investigated for clinical applicability, and it has great promise when used in combination with minimally invasive techniques for both diagnostic and prognostic purposes.

Efficiency analysis of competing tests for finding differentially expressed genes in lung adenocarcinoma

In this study, we introduce and use Efficiency Analysis to compare differences in the apparent internal and external consistency of competing normalization methods and tests for identifying differentially expressed genes. Using publicly available data, two lung adenocarcinoma datasets were analyzed using caGEDA (http://bioinformatics2.pitt.edu/GE2/GEDA.html) to measure the degree of differential expression of genes existing between two populations. The datasets were randomly split into at least two subsets, each analyzed for differentially expressed genes between the two sample groups, and the gene lists compared for overlapping genes. Efficiency Analysis is an intuitive method that compares the differences in the percentage of overlap of genes from two or more data subsets, found by the same test over a range of testing methods. Tests that yield consistent gene lists across independently analyzed splits are preferred to those that yield less consistent inferences. For example, a method that exhibits 50% overlap in the 100 top genes from two studies should be preferred to a method that exhibits 5% overlap in the top 100 genes. The same procedure was performed using all available normalization and transformation methods that are available through caGEDA. The ‘best’ test was then further evaluated using internal cross-validation to estimate generalizable sample classification errors using a Naïve Bayes classification algorithm. A novel test, termed D1 (a derivative of the J5 test) was found to be the most consistent, and to exhibit the lowest overall classification error, and highest sensitivity and specificity. The D1 test relaxes the assumption that few genes are differentially expressed. Efficiency Analysis can be misleading if the tests exhibit a bias in any particular dimension (e.g. expression intensity); we therefore explored intensity-scaled and segmented J5 tests using data in which all genes are scaled to share the same intensity distribution range. Efficiency Analysis correctly predicted the ‘best’ test and normalization method using the Beer dataset and also performed well with the Bhattacharjee dataset based on both efficiency and classification accuracy criteria.

Amplification patterns of three genomic regions predict distant recurrence in breast carcinoma

Currently used clinical and histopathological parameters imprecisely define the risk of distant recurrence in breast cancer, underscoring the need for more informative prognostic markers. In the present fluorescence in situ hybridization study of archived surgical specimens, we derived an algorithm for computing a prognostic index (PI) from DNA copy numbers of three genomic regions (CYP24, PDCD6IP, and BIRC5) for estrogen/progesterone receptor-positive (ER/PR+) cancers and a distinct PI (based on NR1D1, SMARCE1, and BIRC5) for estrogen/progesterone receptor-negative (ER/PR-) cancers. Among independent test cases stratified by PI, recurrence rates were significantly higher among high-risk patients than low-risk patients for both ER/PR+ (odds ratio = 9.52, 95% confidence interval >2.12, P = 0.0024) and ER/PR- (odds ratio = 12.3, 95% confidence interval >1.45, P = 0.0188) cancers. Among the entire population, recurrences were significantly more prevalent for cases with PI above the medians for both ER/PR+ (Fisher's exact, P = 1.19 x 10(-5)) and ER/PR- (P = 0.0025) patients and for the node-negative subsets (ER/PR+ node-negative, P = 0.042 and ER/PR- node-negative, P = 0.039). In conclusion, these markers perform well in comparison with other criteria for recurrence risk assessment and can be used with routinely formalin-fixed, paraffin-embedded surgical specimens.

Tissue detection of biomolecular predictors in breast cancer

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

Receptor tyrosine kinase EphB4 is a survival factor in breast cancer

EphB4, a member of the largest family of receptor tyrosine kinases, is normally expressed on endothelial and neuronal cells. Although aberrant expression of EphB4 has been reported in several human tumors, including breast cancer, its functional significance is not understood. We report here that EphB4 is expressed in 7 of 12 (58%) human breast cancer specimens and 4 of 4 (100%) breast tumor cell lines examined. Overexpression of EphB4 in breast cancer cells was driven by gene amplification and by the erbB family of receptors via activation of Janus tyrosine kinase-signal transducers and activators of transcription and protein kinase B. The aberrantly expressed receptor was phosphorylated by its natural ligand, EphrinB2, and signaled via the protein kinase B pathway. Targeted knockdown of EphB4 expression by small interference RNA (and antisense oligodeoxynucleotides (ODNs)) led to dose-dependent reduction in cell survival, increased apoptosis, and sensitization to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Antisense ODN-mediated EphB4 knockdown resulted in reduced tumor growth in a murine tumor xenograft model. Antisense ODN-treated tumors were 72% smaller than control tumors at 6 weeks, with an 86% reduction in proliferating cells, 15-fold increase in apoptosis, and 44% reduction in tumor microvasculature. Our data indicate that biologically active EphB4 functions as a survival factor in breast cancer and is a novel target for therapy.

Array-CGH and breast cancer

The introduction of comparative genomic hybridization (CGH) in 1992 opened new avenues in genomic investigation; in particular, it advanced analysis of solid tumours, including breast cancer, because it obviated the need to culture cells before their chromosomes could be analyzed. The current generation of CGH analysis uses ordered arrays of genomic DNA sequences and is therefore referred to as array-CGH or matrix-CGH. It was introduced in 1998, and further increased the potential of CGH to provide insight into the fundamental processes of chromosomal instability and cancer. This review provides a critical evaluation of the data published on array-CGH and breast cancer, and discusses some of its expected future value and developments.

The molecular genetics of breast cancer: The contribution of comparative genomic hybridization

Comparative genomic hybridization (CGH) has been the technique of choice over the last 10 years for mapping DNA copy number changes in human tumors. Here we review the literature to demonstrate how CGH has contributed to the comprehension of molecular aspects of breast tumorigenesis. At least two distinct molecular pathways of breast cancer have been characterized that show a strong correlation with histological grade. It seems that grade I invasive ductal carcinomas (IDCs) arise from well-differentiated ductal carcinoma in situ (DCIS), whereas grade III IDCs come from poorly differentiated DCIS. In addition, dedifferentiation from a low- to a high-grade breast cancer has proven an unlikely phenomenon. CGH has been instrumental in dissecting distinct molecular pathways toward breast malignancy and in establishing a direct relationship between genotype and clinical pathological features.

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.