Katherine J. Martin

Terminal Differentiation of Human Breast Cancer through PPARγ

We have previously demonstrated that PPAR gamma stimulates the terminal differentiation of adipocyte precursors when activated by synthetic ligands, such as the antidiabetic thiazolidinedione (TZD) drugs. We show here that PPAR gamma is expressed at significant levels in human primary and metastatic breast adenocarcinomas. Ligand activation of this receptor in cultured breast cancer cells causes extensive lipid accumulation, changes in breast epithelial gene expression associated with a more differentiated, less malignant state, and a reduction in growth rate and clonogenic capacity of the cells. Inhibition of MAP kinase, shown previously to be a powerful negative regulator of PPAR gamma, improves the TZD ligand sensitivity of nonresponsive cells. These data suggest that the PPAR gamma transcriptional pathway can induce terminal differentiation of malignant breast epithelial cells and thus may provide a novel, nontoxic therapy for human breast cancer.

Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets.

Background One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Methods and Findings Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER− patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. Conclusions The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.

High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood

Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial cells. In a secondary screen, high-sensitivity arrays assessed expression levels of these genes in 48 blood samples, 22 from healthy volunteers and 26 from breast cancer patients. Cluster analysis identified a group of 12 genes that were elevated in the blood of cancer patients. Permutation analysis of individual genes defined five core genes (P ≤ 0.05, permax test). As a group, the 12 genes generally distinguished accurately between healthy volunteers and patients with breast cancer. Mean expression levels of the 12 genes were elevated in 77% (10 of 13) untreated invasive cancer patients, whereas cluster analysis correctly classified volunteers and patients (P = 0.0022, Fishers exact test). Quantitative real-time PCR confirmed array results and indicated that the sensitivity of the assay (1:2 × 108 transcripts) was sufficient to detect disseminated solid tumor cells in blood. Expression-based blood assays developed with the screening approach described here have the potential to detect and classify solid tumor cells originating from virtually any primary site in the body.

Gene expression signature in organized and growth-arrested mammary acini predicts good outcome in breast cancer.

Nonmalignant human mammary epithelial cells (HMEC) seeded in laminin-rich extracellular matrix (lrECM) form polarized acini and, in doing so, transit from a disorganized proliferating state to an organized growth-arrested state. We hypothesized that the gene expression pattern of organized and growth-arrested HMECs would share similarities with breast tumors with good prognoses. Using Affymetrix HG-U133A microarrays, we analyzed the expression of 22,283 gene transcripts in 184 (finite life span) and HMT3522 S1 (immortal nonmalignant) HMECs on successive days after seeding in a lrECM assay. Both HMECs underwent growth arrest in G0-G1 and differentiated into polarized acini between days 5 and 7. We identified gene expression changes with the same temporal pattern in both lines and examined the expression of these genes in a previously published panel of microarray data for 295 breast cancer samples. We show that genes that are significantly lower in the organized, growth-arrested HMEC than in their proliferating counterparts can be used to classify breast cancer patients into poor and good prognosis groups with high accuracy. This study represents a novel unsupervised approach to identifying breast cancer markers that may be of use clinically.

TRAIL Is a Novel Antiviral Protein against Dengue Virus

ABSTRACT Dengue fever is an important tropical illness for which there is currently no virus-specific treatment. To shed light on mechanisms involved in the cellular response to dengue virus (DV), we assessed gene expression changes, using Affymetrix GeneChips (HG-U133A), of infected primary human cells and identified changes common to all cells. The common response genes included a set of 23 genes significantly induced upon DV infection of human umbilical vein endothelial cells (HUVECs), dendritic cells (DCs), monocytes, and B cells (analysis of variance, P < 0.05). Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), one of the common response genes, was identified as a key link between type I and type II interferon response genes. We found that DV induces TRAIL expression in immune cells and HUVECs at the mRNA and protein levels. The induction of TRAIL expression by DV was found to be dependent on an intact type I interferon signaling pathway. A significant increase in DV RNA accumulation was observed in anti-TRAIL antibody-treated monocytes, B cells, and HUVECs, and, conversely, a decrease in DV RNA was seen in recombinant TRAIL-treated monocytes. Furthermore, recombinant TRAIL inhibited DV titers in DV-infected DCs by an apoptosis-independent mechanism. These data suggest that TRAIL plays an important role in the antiviral response to DV infection and is a candidate for antiviral interventions against DV.

Principles of differential display.

Publisher Summary The introduction of differential display (DD) and related techniques has contributed to the recent shift in focus from DNA genetics to expression genetics, especially in the field of cancer research. This chapter describes the principles of DD, including specific methods for working with the primers that have been used extensively. The chapter discusses the production of a DD gel, strategies for isolating and identifying DD band cDNAs, and information on performing high-throughput DD. DD is distinguished from related methods by a low stringency, competitive polymerase chain reaction (PCR) step that uses primer pairs to target the 3’ ends of messenger RNAs. One of the major criticisms of the DD technique has been the high number of false positives obtained in some studies.

Interaction of E-cadherin and PTEN Regulates Morphogenesis and Growth Arrest in Human Mammary Epithelial Cells

Phosphatase and tensin homologue deleted on chromosome 10 (PTEN) is a dual-function phosphatase with tumor suppressor function compromised in a wide spectrum of cancers. Because tissue polarity and architecture are crucial modulators of normal and malignant behavior, we postulated that PTEN may play a role in maintenance of tissue integrity. We used two nonmalignant human mammary epithelial cell lines that form polarized, growth-arrested structures (acini) when cultured in three-dimensional laminin-rich extracellular matrix gels (lrECM). As acini begin to form, PTEN accumulates both in the cytoplasm and at cell-cell contacts where it colocalizes with the E-cadherin/beta-catenin complex. Reduction of PTEN levels by shRNA in lrECM prevents formation of organized breast acini and disrupts growth arrest. Importantly, disruption of acinar polarity and cell-cell contact by E-cadherin function-blocking antibodies reduces endogenous PTEN protein levels and inhibits its accumulation at cell-cell contacts. Conversely, in Skbr-3 breast cancer cells lacking endogenous E-cadherin expression, exogenous introduction of E-cadherin gene causes induction of PTEN expression and its accumulation at sites of cell interactions. These studies provide evidence that E-cadherin regulates both the PTEN protein levels and its recruitment to cell-cell junctions in three-dimensional lrECM, indicating a dynamic reciprocity between architectural integrity and the levels and localization of PTEN. This interaction thus seems to be a critical integrator of proliferative and morphogenetic signaling in breast epithelial cells.

A Need for Basic Research on Fluid-Based Early Detection Biomarkers

Cancer continues to be a major cause of mortality despite decades of effort and expense. The problem reviewed here is that before many cancers are discovered they have already progressed to become drug resistant or metastatic. Biomarkers found in blood or other body fluids could supplement current clinical indicators to permit earlier detection and thereby reduce cancer mortality.

Expression genetics: a different approach to cancer diagnosis and prognosis

Expression genetics is a new approach to the identification of cancer-related genes. Instead of studying gene mutations at the genome level, it focuses on the investigation of heredity at the RNA level. By isolating genes whose expression is up or down regulated in cancers, expression geneticists study their function in the context of gene regulation. A major goal of expression genetics in cancer is to correct gene expression in tumors by the application of potential therapeutic agents.

Transcriptome profiling in clinical breast cancer: from 3D culture models to prognostic signatures.

Early detection has been one of the most effective strategies to control the growing cancer burden. The power of earlier detection has been demonstrated by the impact of pap‐smear, mammography, and PSA tests on cancer patient treatment and survival. These tests benefit patients independent of their genetic background or race. However, in many cases, we are still losing the battle against cancer because patients that initially presented with low‐grade disease progress rapidly to aggressive forms of the disease. As of yet, we have limited means to predict a particular patients fate or to specifically treat subtypes of cancer. A combination of earlier detection and targeted therapy, based on information from transcriptome analysis, could be a powerful ally in this battle. The theme of this review article is to briefly summarize innovative strategies using three‐dimensional (3D) cell cultures of human mammary epithelial cells to predict clinical outcome in breast cancer. This strategy has the potential to further enhance our understanding of breast cancer biology and to contribute to the identification of biologically significant bio‐markers that are also useful drug targets. J. Cell. Physiol. 209: 625–630, 2006.