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.     

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.     

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.     

Gene expression profiling and clinical outcome in breast cancer

Pathologic and clinical heterogeneity of breast cancer reflects the poorly documented, complex, and combinatory molecular basis of the disease and is in part responsible for therapeutic failures. The DNA microarray technique allows the analysis of RNA expression of several thousands of genes simultaneously in a sample. There are multiple potential applications of the technique in cancer research. A number of recent studies have shown the promising role of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters and new prognostic and/or predictive gene signatures, whose predictive impact is superior to conventional histoclinical prognostic factors. In this review we describe current use of DNA microarrays in the prognosis of breast cancer. We also discuss issues that need to be addressed in the near future to allow the method to reach its full potential.     

The generation and characterization of a radiation-resistant model system to study radioresistance in human breast cancer cells.

To systematically study the selection of radioresistant cells in clinically advanced breast cancer, a model system was generated by treating MDA-MB231 breast cancer cells with fractionated gamma radiation. A clonogenic assay of the surviving cell populations showed that 2-6 Gy per fraction resulted in a rapid selection of radioresistant populations, within three to five fractions. Irradiation with additional fractions after this initial increase did not increase the radioresistance of the surviving population significantly. Doses of 0.5 and 8 Gy per fraction were not effective in selecting radioresistant cells. To further determine the cause of the changes in radiosensitivity, 15 clones were isolated from the cell populations treated with 40 or 60 Gy with 2 or 4 Gy per fraction, respectively, and were analyzed for radiosensitivity. The average D(10) for these clones was 6.75 +/- 0.36 Gy, which was higher than that for the parental cell population (D(10) = 6.0 +/- 0.2 Gy). The operation of cell cycle checkpoints and the doubling time were similar for both the nonirradiated parental population and the isolated radioresistant subclones. In contrast, a decrease in the apoptotic potential was correlated (r = 0.7, P < 0.01) with increased survival after irradiation, suggesting that apoptosis is an important factor in determining radioresistance under our experimental conditions. We also isolated several subclones from the nonirradiated parental cell population and analyzed them to determine their radiosensitivity after fractionated irradiation. Ten fractions of 4 Gy (40 Gy in total) did not result in a significant increase in the radioresistance of these subclones compared to the irradiated cell populations. The possible mechanisms of the increased radioresistance after fractionated irradiation are discussed.     

Matrix rigidity induces osteolytic gene expression of metastatic breast cancer cells

Nearly 70% of breast cancer patients with advanced disease will develop bone metastases. Once established in bone, tumor cells produce factors that cause changes in normal bone remodeling, such as parathyroid hormone-related protein (PTHrP). While enhanced expression of PTHrP is known to stimulate osteoclasts to resorb bone, the environmental factors driving tumor cells to express PTHrP in the early stages of development of metastatic bone disease are unknown. In this study, we have shown that tumor cells known to metastasize to bone respond to 2D substrates with rigidities comparable to that of the bone microenvironment by increasing expression and production of PTHrP. The cellular response is regulated by Rho-dependent actomyosin contractility mediated by TGF-ß signaling. Inhibition of Rho-associated kinase (ROCK) using both pharmacological and genetic approaches decreased PTHrP expression. Furthermore, cells expressing a dominant negative form of the TGF-ß receptor did not respond to substrate rigidity, and inhibition of ROCK decreased PTHrP expression induced by exogenous TGF-ß. These observations suggest a role for the differential rigidity of the mineralized bone microenvironment in early stages of tumor-induced osteolysis, which is especially important in metastatic cancer since many cancers (such as those of the breast and lung) preferentially metastasize to bone.