In silico study of breast cancer associated gene 3 using LION Target Engine and other tools

Sequence analysis of individual targets is an important step in annotation and validation. As a test case, we investigated human breast cancer associated gene 3 (BCA3) with LION Target Engine and with other bioinformatics tools. LION Target Engine confirmed that the BCA3 gene is located on 11p15.4 and that the two most likely splice variants (lacking exon 3 and exons 3 and 5, respectively) exist. Based on our manual curation of sequence data, it is proposed that an additional variant (missing only exon 5) published in a public sequence repository, is a prediction artifact. A significant number of new orthologs were also identified, and these were the basis for a high-quality protein secondary structure prediction. Moreover, our research confirmed several distinct functional domains as described in earlier reports. Sequence conservation from multiple sequence alignments, splice variant identification, secondary structure predictions, and predicted phosphorylation sites suggest that the removal of interaction sites through alternative splicing might play a modulatory role in BCA3. This in silico approach shows the depth and relevance of an analysis that can be accomplished by including a variety of publicly available tools with an integrated and customizable life science informatics platform.     

Stromal gene expression predicts clinical outcome in breast cancer

Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.     

T47-D cells and type V collagen: a model for the study of apoptotic gene expression by breast cancer cells

We have previously reported that type V collagen is a poorly adhesive, anti-proliferative and motility-inhibitory substrate for the 8701-BC breast cancer cell line, which also triggers DNA fragmentation and impairs survival of the same cell line. In the present work we have extended to other breast cancer cell lines (T47-D, MDA-MB231, Hs578T) our investigation of type V collagen influence on the DNA status and cell survival, also examining whether adhesion and growth of cells on this collagen substrate could exert some effect on the expression level of selected apoptosis-related genes. We report here that, among the cell lines tested, only T47-D is responsive to the death-promoting influence of type V collagen. In addition, the latter induces changes in gene expression by up-regulating p53, Waf-1, Cas, Dap kinase and caspases 1, -5 and -14 and down-regulating Bcl-2. Our data validate the T47-D line as a suitable in vitro model for further and more detailed studies on the molecular mechanisms of the death response induced by type V collagen on mammary tumor cells.     

In silico feedback for in vivo regulation of a gene expression circuit

We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.     

Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome

Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.     

Engineering tumor constructs to study matrix-dependent angiogenic signaling of breast cancer cells

Breast cancer is the leading cause of cancer deaths among females globally. The crosstalk between tumor microenvironment and neoplastic cells is the key for promoting tumor growth, stimulating tumor angiogenesis, and metastasis to distant organs. Thus, it is highly important to investigate tumor cell–matrix interactions to facilitate screening of different anti-cancer agents, individually or in combination. We, herein report, the development of an in vitro three-dimensional (3D) breast cancer model to investigate the effect of stromal crosslinking and consequent, stiffening on the angiogenic activity of cancer cells. Crosslinking of collagen gels was altered via non-enzymatic glycation and highly aggressive breast cancer cells, MDA-MB-231, were encapsulated in these gels. Cells encapsulated in glycated/stiffer matrices displayed an increased expression of pro-angiogenesis-related signals. Inhibition of mechanotransduction pathways on the angiogenic activity of aggressive tumor cells in stiff matrices was investigated using Y-27632, blebbistatin, and cytochalasin D. Rho-associated kinase (ROCK) inhibitor, Y-27632, diminished the pro-angiogenic signaling, thereby suggesting the potential dependence of breast cancer cells on the Rho/ROCK pathway in regulating tumor angiogenesis. Our findings highlight the potential of the developed model to be used as a tool to investigate matrix-associated tumor angiogenesis and screen different therapeutic agents towards inhibiting it.     

Effects of androgen receptor and androgen on gene expression in prostate stromal fibroblasts and paracrine signaling to prostate cancer cells

The androgen receptor (AR) is expressed in a subset of prostate stromal cells and functional stromal cell AR is required for normal prostate developmental and influences the growth of prostate tumors. Although we are broadly aware of the specifics of the genomic actions of AR in prostate cancer cells, relatively little is known regarding the gene targets of functional AR in prostate stromal cells. Here, we describe a novel human prostate stromal cell model that enabled us to study the effects of AR on gene expression in these cells. The model involves a genetically manipulated variant of immortalized human WPMY-1 prostate stromal cells that overexpresses wildtype AR (WPMY-AR) at a level comparable to LNCaP cells and is responsive to dihydrotestosterone (DHT) stimulation. Use of WPMY-AR cells for gene expression profiling showed that the presence of AR, even in the absence of DHT, significantly altered the gene expression pattern of the cells compared to control (WPMY-Vec) cells. Treatment of WPMY-AR cells, but not WPMY-Vec control cells, with DHT resulted in further changes that affected the expression of 141 genes by 2-fold or greater compared to vehicle treated WPMY-AR cells. Remarkably, DHT significantly downregulated more genes than were upregulated but many of these changes reversed the initial effects of AR overexpression alone on individual genes. The genes most highly effected by DHT treatment were categorized based upon their role in cancer pathways or in cell signaling pathways (transforming growth factor-β, Wnt, Hedgehog and MAP Kinase) thought to be involved in stromal-epithelial crosstalk during prostate or prostate cancer development. DHT treatment of WPMY-AR cells was also sufficient to alter their paracrine potential for prostate cancer cells as conditioned medium from DHT-treated WPMY-AR significantly increased growth of LNCaP cells compared to DHT-treated WPMY-Vec cell conditioned medium.     

T cells stimulate catabolic gene expression by the stromal cells from giant cell tumor of bone

The factors that promote the localized bone resorption by giant cell tumor of bone (GCT) are not fully understood. We investigated whether T cells could contribute to bone resorption by stimulating expression of genes for parathyroid hormone-related protein (PTHrP), matrix metalloproteinase (MMP)-13, and the receptor activator of nuclear-factor κB ligand (RANKL). Two cell lines, Jurkat clone E6-1 and D1.1, were co-cultured with isolated GCT stromal cells. Real-time PCR analyses demonstrated a significant increase of all three genes following 48h incubation, and PTHrP and MMP-13 gene expression was also increased at 24h. Further, we examined the expression of CD40 ligand (CD40L), a protein expressed by activated T cells, and its receptor, CD40, in GCT. Immunohistochemistry results revealed expression of the CD40 receptor in both the stromal cells and giant cells of the tumor. RNA collected from whole GCT tissues showed expression of CD40LG, which was absent in cultured stromal cells, and suggests that CD40L is expressed within GCT. Stimulation of GCT stromal cells with CD40L significantly increased expression of the PTHrP and MMP-13 genes. Moreover, we show that inhibition of PTHrP with neutralizing antibodies significantly decreased MMP13 expression by the stromal cells compared to IgG-matched controls, whereas stimulation with PTHrP (1-34) increased MMP-13 gene expression. These results suggest that T cells may potentiate the catabolic effect of GCT.