Cells with distinct phenotypes including stem-cell-like properties have been proposed to exist in normal human mammary epithelium and breast carcinomas, but their detailed molecular characteristics and clinical significance are unclear. We determined gene expression and genetic profiles of cells purified from cancerous and normal breast tissue using markers previously associated with stem-cell-like properties. CD24+ and CD44+ cells from individual tumors were clonally related but not always identical. CD44+ cell-specific genes included many known stem-cell markers and correlated with decreased patient survival. The TGF-beta pathway was specifically active in CD44+ cancer cells, where its inhibition induced a more epithelial phenotype. Our data suggest prognostic relevance of CD44+ cells and therapeutic targeting of distinct tumor cell populations.

A single cancer cell contains large numbers of genetic alterations that in combination create the malignant phenotype. However, whether amplified and mutated genes form functional and physical interaction networks that could explain the selection for cells with combined alterations is unknown. To investigate this issue, we characterized copy number alterations in 191 breast tumors using dense single nucleotide polymorphism arrays and identified 1,747 genes with copy number gain organized into 30 amplicons. Amplicons were distributed unequally throughout the genome. Each amplicon had distinct enrichment pattern in pathways, networks, and molecular functions, but genes within individual amplicons did not form coherent functional units. Genes in amplicons included all major tumorigenic pathways and were highly enriched in breast cancer-causative genes. In contrast, 1,188 genes with somatic mutations in breast cancer were distributed randomly over the genome, did not represent a functionally cohesive gene set, and were relatively less enriched in breast cancer marker genes. Mutated and gained genes did not show statistically significant overlap but were highly synergistic in populating key tumorigenic pathways including transforming growth factor B, WNT, fibroblast growth factor, and PIP3 signaling. In general, mutated genes were more frequently upstream of gained genes in transcription regulation signaling than vice versa, suggesting that mutated genes are mainly regulators, whereas gained genes are mostly regulated. ESR1 was the major transcription factor regulating amplified but not mutated genes. Our results support the hypothesis that multiple genetic events, including copy number gains and somatic mutations, are necessary for establishing the malignant cell phenotype. [Cancer Res 2008;68(22):9532-40]

The authors have previously applied two integrated platforms, MetaCore and MetaDrug, for the assembly and analysis of human biological networks as a useful method for the integration and functional interpretation of high-throughput experimental data. The present study demonstrates in detail the specific algorithms that are used in both software platforms. Using a standard set of genes as input, namely CYP3A4 (an enzyme), PXR (a nuclear hormone receptor), MDR1 (a transporter) and hERG (an ion channel) related to the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) of xenobiotics, we have now generated networks with each algorithm. The relative advantages and disadvantages of these algorithms are explained using these examples as well as appropriate instances of utility to illustrate further the particular circumstances for their use. In addition, the benefits of the different network algorithms are identified when compared with algorithms available in other products, where this information is available.

Background: Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive.

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.