Universal Reference RNA as a standard for microarray experiments

BACKGROUND: Obtaining reliable and reproducible two-color microarray gene expression data is critically important for understanding the biological significance of perturbations made on a cellular system. Microarray design, RNA preparation and labeling, hybridization conditions and data acquisition and analysis are variables difficult to simultaneously control. A useful tool for monitoring and controlling intra- and inter-experimental variation is Universal Reference RNA (URR), developed with the goal of providing hybridization signal at each microarray probe location (spot). Measuring signal at each spot as the ratio of experimental RNA to reference RNA targets, rather than relying on absolute signal intensity, decreases variability by normalizing signal output in any two-color hybridization experiment. RESULTS: Human, mouse and rat URR (UHRR, UMRR and URRR, respectively) were prepared from pools of RNA derived from individual cell lines representing different tissues. A variety of microarrays were used to determine percentage of spots hybridizing with URR and producing signal above a user defined threshold (microarray coverage). Microarray coverage was consistently greater than 80% for all arrays tested. We confirmed that individual cell lines contribute their own unique set of genes to URR, arguing for a pool of RNA from several cell lines as a better configuration for URR as opposed to a single cell line source for URR. Microarray coverage comparing two separately prepared batches each of UHRR, UMRR and URRR were highly correlated (Pearson's correlation coefficients of 0.97). CONCLUSION: Results of this study demonstrate that large quantities of pooled RNA from individual cell lines are reproducibly prepared and possess diverse gene representation. This type of reference provides a standard for reducing variation in microarray experiments and allows more reliable comparison of gene expression data within and between experiments and laboratories

Characterization of heterotypic interaction effects in vitro to deconvolute global gene expression profiles in cancer

Background: Perturbations in cell-cell interactions are a key feature of cancer. However, little is known about the systematic effects of cell-cell interaction on global gene expression in cancer. Results We used an ex vivo model to simulate tumor-stroma interaction by systematically co-cultivating breast cancer cells with stromal fibroblasts and determined associated gene expression changes with cDNA microarrays. In the complex picture of epithelial-mesenchymal interaction effects, a prominent characteristic was an induction of interferon-response genes (IRGs) in a subset of cancer cells. In close proximity to these cancer cells, the fibroblasts secreted type I interferons, which, in turn, induced expression of the IRGs in the tumor cells. Paralleling this model, immunohistochemical analysis of human breast cancer tissues showed that STAT1, the key transcriptional activator of the IRGs, and itself an IRG, was expressed in a subset of the cancers, with a striking pattern of elevated expression in the cancer cells in close proximity to the stroma. In vivo, expression of the IRGs was remarkably coherent, providing a basis for segregation of 295 early-stage breast cancers into two groups. Tumors with high compared to low expression levels of IRGs were associated with significantly shorter overall survival; 59% versus 80% at 10 years (log-rank p = 0.001). Conclusion In an effort to deconvolute global gene expression profiles of breast cancer by systematic characterization of heterotypic interaction effects in vitro, we found that an interaction between some breast cancer cells and stromal fibroblasts can induce an interferon-response, and that this response may be associated with a greater propensity for tumor progression.Medicine, Faculty ofPathology and Laboratory Medicine, Department ofNon UBCReviewedFacult

Correlation of the 70 genes signature 38, the wound signature 24, the hypoxia signature 25 and the interferon response score in the NKI dataset

Pairwise scatterplot-matrix of four gene signatures. Pearson correlations are shown in the lower part of each plot.<p><b>Copyright information:</b></p><p>Taken from "Characterization of heterotypic interaction effects to deconvolute global gene expression profiles in cancer"</p><p>http://genomebiology.com/2007/8/9/R191</p><p>Genome Biology 2007;8(9):R191-R191.</p><p>Published online 14 Sep 2007</p><p>PMCID:PMC2375029.</p><p></p

Overview of gene expression changes over multiple co-cultures of breast cancer cell lines and normal breast epithelial cells with fibroblasts

Correlation of the measured co-culture gene expression levels and their estimated expression levels based on the proportional contribution of each cell type determined by a linear regression fit of the monoculture to the co-culture data. Fold change of each gene associated with co-culturing of MDA-MB231 and CCL-171. Genes of the interferon response gene set (Additional data file 1) as determined by SAM are indicated in red. Fold change in expression of the interferon response gene set (Additional data file 1) in co-culture of MCF-7, HMECs and MDA-MB-231 with either the CCL-171 lung fibroblast or the HTB-125 breast fibroblast, showing that CCL-171 and HTB-125 induce a distinct, but very similar response in co-culture with different epithelial cells. Overview of collapsed data from repeat co-culture experiments of eight benign and malignant epithelial cells with three different fibroblasts. Hierarchical clustering of the interaction effects of 3,000 genes in co-cultures of 7 breast cancer cell lines and normal breast epithelial cells with fibroblasts. Red and green denote relative changes in expression associated heterotypic interaction. The magnitude of the relative change is given by color intensity.<p><b>Copyright information:</b></p><p>Taken from "Characterization of heterotypic interaction effects to deconvolute global gene expression profiles in cancer"</p><p>http://genomebiology.com/2007/8/9/R191</p><p>Genome Biology 2007;8(9):R191-R191.</p><p>Published online 14 Sep 2007</p><p>PMCID:PMC2375029.</p><p></p

Induction of interferon response in two types of breast cancer cell lines

MDA-MB231 cells were incubated in conditioned media from CCL-171 monoculture, MDA-MB231 monoculture, T47D monoculture, CCL-171/MDA-MB231 co-culture and CCL-171/T47D co-culture. gene expression was determined by quantitative RT-PCR. The gene expression level of GAPDH was used for normalization between the samples. A strong induction of by the supernatant from the CCL-171/MDA-MB231 co-culture can be seen in MDA-MB231. Incubation of T47D cells with conditioned media from CCL-171 monoculture, MDA-MB231 monoculture, T47D monoculture, CCL-171/MDA-MB231 co-culture and CCL-171/T47D co-culture showed that only the CCL-171/MDA-MB231 co-culture supernatant induced gene expression, although to a much lesser extent than in MDA-MB231 cells. Gene expression levels of were determined by quantitative RT-PCR. CCL-171 cells show much higher expression levels when isolated by FACS after co-culture with MDA-MB231 than with T47D cells. Expression levels in tumor cells are shown as controls. The error bars show the standard deviation from the normalized mean.<p><b>Copyright information:</b></p><p>Taken from "Characterization of heterotypic interaction effects to deconvolute global gene expression profiles in cancer"</p><p>http://genomebiology.com/2007/8/9/R191</p><p>Genome Biology 2007;8(9):R191-R191.</p><p>Published online 14 Sep 2007</p><p>PMCID:PMC2375029.</p><p></p

Immunohistochemical staining of STAT1 in a cohort of primary breast cancers: Kaplan-Meier disease-specific survival curve for 353 primary tumors assessed for STAT1

The red curve shows 102 patients bearing tumors with high STAT1 expression whereas the blue curve represents 251 patients with low or absent STAT1 expression. X = censored data.<p><b>Copyright information:</b></p><p>Taken from "Characterization of heterotypic interaction effects to deconvolute global gene expression profiles in cancer"</p><p>http://genomebiology.com/2007/8/9/R191</p><p>Genome Biology 2007;8(9):R191-R191.</p><p>Published online 14 Sep 2007</p><p>PMCID:PMC2375029.</p><p></p

Effect of heterotypic interaction between breast cancer cell line MDA-MB231 and CCL-171 fibroblasts

Biologically independent replicates of the monocultured fibroblast CCL-171, the breast cancer cell line MDA-MB231 and the mixed co-culture of CCL-171 and MDA-MB231 were grown for 48 h at low serum conditions and characterized by DNA microarray hybridization. Hierarchical clustering of a total of 4,333 elements that display a greater than 3-fold variance in expression in more than 3 different experimental samples. Data from individual elements or genes are represented as single rows, and different experiments are shown as columns. Red and green denote expression levels of the samples. The intensity of the color reflects the magnitude of the deviation from baseline. Unsupervised hierarchical clustering of the experiments grouped the biological replicates together. Gene expression varied considerably between fibroblast and MDA-MB231 cultures, as expected for cells of mesenchymal or epithelial origin, respectively. The co-culture profile showed mainly intermediate expression levels. However, the vertical black bar marks a cluster of genes induced in all co-cultures compared to both monocultures, indicating that they are induced by heterotypic interaction. Zooming in on the genes up-regulated in co-culture compared to monocultures reveals that they are associated with the response to interferon.<p><b>Copyright information:</b></p><p>Taken from "Characterization of heterotypic interaction effects to deconvolute global gene expression profiles in cancer"</p><p>http://genomebiology.com/2007/8/9/R191</p><p>Genome Biology 2007;8(9):R191-R191.</p><p>Published online 14 Sep 2007</p><p>PMCID:PMC2375029.</p><p></p

Repeated observation of breast tumor subtypes in independent gene expression data sets

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 “intrinsic” genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities