Biomedical subjects

Aleksandra M Michalowska

Publications and source records attributed to Aleksandra M Michalowska.

5 recordsLinked to original sources

PubMed2006

Chemokine (C-C motif) ligand 2 mediates the prometastatic effect of dysadherin in human breast cancer cells.

Dysadherin, a cancer-associated membrane glycoprotein, down-regulates E-cadherin and promotes cancer metastasis. This study examined the role of dysadherin in breast cancer progression. Expression of dysadherin was found to be highest in breast cancer cell lines and tumors that lacked the estrogen receptor (ER). Knockdown of dysadherin caused increased association of E-cadherin with the actin cytoskeleton in breast cancer cell lines that expressed E-cadherin. However, knockdown of dysadherin could still suppress cell invasiveness in cells that had no functional E-cadherin, suggesting the existence of a novel mechanism of action. Global gene expression analysis identified chemokine (C-C motif) ligand 2 (CCL2) as the transcript most affected by dysadherin knockdown in MDA-MB-231 cells, and dysadherin was shown to regulate CCL2 expression in part through activation of the nuclear factor-kappaB pathway. The ability of dysadherin to promote tumor cell invasion in vitro was dependent on the establishment of a CCL2 autocrine loop, and CCL2 secreted by dysadherin-positive tumor cells also promoted endothelial cell migration in a paracrine fashion. Finally, experimental suppression of CCL2 in MDA-MB-231 cells reduced their ability to metastasize in vivo. This study shows that dysadherin has prometastatic effects that are independent of E-cadherin expression and that CCL2 could play an important role in mediating the prometastatic effect of dysadherin in ER-negative breast cancer.

Actins↗

PubMed2006

Bone sialoprotein mediates the tumor cell-targeted prometastatic activity of transforming growth factor beta in a mouse model of breast cancer.

Transforming growth factor betas (TGF-beta) play a dual role in carcinogenesis, functioning as tumor suppressors early in the process, and then switching to act as prometastatic factors in late-stage disease. We have previously shown that high molecular weight TGF-beta antagonists can suppress metastasis without the predicted toxicities. To address the underlying mechanisms, we have used the 4T1 syngeneic mouse model of metastatic breast cancer. Treatment of mice with a monoclonal anti-TGF-beta antibody (1D11) significantly suppressed metastasis of 4T1 cells to the lungs. When metastatic 4T1 cells were recovered from lungs of 1D11-treated and control mice, the most differentially expressed gene was found to be bone sialoprotein (Bsp). Immunostaining confirmed the loss of Bsp protein in 1D11-treated lung metastases, and TGF-beta was shown to regulate and correlate with Bsp expression in vitro. Functionally, knockdown of Bsp in 4T1 cells reduced the ability of TGF-beta to induce local collagen degradation and invasion in vitro, and treatment with recombinant Bsp protected 4T1 cells from complement-mediated lysis. Finally, suppression of Bsp in 4T1 cells reduced metastasis in vivo. We conclude that Bsp is a plausible mediator of at least some of the tumor cell-targeted prometastatic activity of TGF-beta in this model and that Bsp expression in metastases can be successfully suppressed by systemic treatment with anti-TGF-beta antibodies.

Animals↗

PubMed2004

Gene expression profiling identifies a unique androgen-mediated inflammatory/immune signature and a PTEN (phosphatase and tensin homolog deleted on chromosome 10)-mediated apoptotic response specific to the rat ventral prostate.

Understanding androgen regulation of gene expression is critical for deciphering mechanisms responsible for the transition from androgen-responsive (AR) to androgen-independent (AI) prostate cancer (PCa). To identify genes differentially regulated by androgens in each prostate lobe, the rat castration model was used. Microarray analysis was performed to compare dorsolateral (DLP) and ventral prostate (VP) samples from sham-castrated, castrated, and testosterone-replenished castrated rats. Our data demonstrate that, after castration, the VP and the DLP differed in the number of genes with altered expression (1496 in VP vs. 256 in DLP) and the nature of pathways modulated. Gene signatures related to apoptosis and immune response specific to the ventral prostate were identified. Microarray and RT-PCR analyses demonstrated the androgen repression of IGF binding protein-3 and -5, CCAAT-enhancer binding protein-delta, and phosphatase and tensin homolog deleted on chromosome 10 (PTEN) genes, previously implicated in apoptosis. We show that PTEN protein was increased only in the luminal epithelial cells of the VP, suggesting that it may be a key mediator of VP apoptosis in the absence of androgens. The castration-induced immune/inflammatory gene cluster observed specifically in the VP included IL-15 and IL-18. Immunostaining of the VP, but not the DLP, showed an influx of T cells, macrophages, and mast cells, suggesting that these cells may be the source of the immune signature genes. Interestingly, IL-18 was localized mainly to the basal epithelial cells and the infiltrating macrophages in the regressing VP, whereas IL-15 was induced in the luminal epithelium. The VP castration model exhibits immune cell infiltration and loss of PTEN that is often observed in progressive PCa, thereby making this model useful for further delineation of androgen-regulated gene expression with relevance to PCa.

Androgens↗

PubMed2004

Effects of pooling mRNA in microarray class comparisons.

MOTIVATION: In microarray experiments investigators sometimes wish to pool RNA samples before labeling and hybridization due to insufficient RNA from each individual sample or to reduce the number of arrays for the purpose of saving cost. The basic assumption of pooling is that the expression of an mRNA molecule in the pool is close to the average expression from individual samples. Recently, a method for studying the effect of pooling mRNA on statistical power in detecting differentially expressed genes between classes has been proposed, but the different sources of variation arising in microarray experiments were not distinguished. Another paper recently did take different sources of variation into account, but did not address power and sample size for class comparison. In this paper, we study the implication of pooling in detecting differential gene expression taking into account different sources of variation and check the basic assumption of pooling using data from both the cDNA and Affymetrix GeneChip microarray experiments. RESULTS: We present formulas for the required number of subjects and arrays to achieve a desired power at a specified significance level. We show that due to the loss of degrees of freedom for a pooled design, a large increase in the number of subjects may be required to achieve a power comparable to that of a non-pooled design. The added expense of additional samples for the pooled design may outweigh the benefit of saving on microarray cost. The microarray data from both platforms show that the major assumption of pooling may not hold. SUPPLEMENTARY INFORMATION: Supplementary material referenced in the text is available at http://linus.nci.nih.gov/brb/TechReport.htm.

Algorithms↗

PubMed2003

Statistical issues in the design and analysis of gene expression microarray studies of animal models.

Appropriate statistical design and analysis of gene expression microarray studies is critical in order to draw valid and useful conclusions from expression profiling studies of animal models. In this paper, several aspects of study design are discussed, including the number of animals that need to be studied to ensure sufficiently powered studies, usefulness of replication and pooling, and allocation of samples to arrays. Data preprocessing methods for both cDNA dual-label spotted arrays and Affymetrix-style oligonucleotide arrays are reviewed. High-level analysis strategies are briefly discussed for each of the types of study aims, namely class comparison, class discovery, and class prediction. For class comparison, methods are discussed for identifying genes differentially expressed between classes while guarding against unacceptably high numbers of false positive findings. Various clustering methods are discussed for class discovery aims. Class prediction methods are briefly reviewed, and reference is made to the importance of proper validation of predictors.

Animals↗