Yeast screens identify the RNA polymerase II CTD and SPT5 as relevant targets of BRCA1 interaction.

BRCA1 has been implicated in numerous DNA repair pathways that maintain genome integrity, however the function responsible for its tumor suppressor activity in breast cancer remains obscure. To identify the most highly conserved of the many BRCA1 functions, we screened the evolutionarily distant eukaryote Saccharomyces cerevisiae for mutants that suppressed the G1 checkpoint arrest and lethality induced following heterologous BRCA1 expression. A genome-wide screen in the diploid deletion collection combined with a screen of ionizing radiation sensitive gene deletions identified mutants that permit growth in the presence of BRCA1.

Acquired expression of periostin by human breast cancers promotes tumor angiogenesis through up-regulation of vascular endothelial growth factor receptor 2 expression.

The late stages of human breast cancer development are poorly understood complex processes associated with the expression of genes by cancers that promote specific tumorigenic activities, such as angiogenesis. Here, we describe the identification of periostin as a mesenchyme-specific gene whose acquired expression by human breast cancers leads to a significant enhancement in tumor progression and angiogenesis. Undetectable in normal human breast tissues, periostin was found to be overexpressed by the vast majority of human primary breast cancers examined.

High expression of tumor necrosis factor-related apoptosis-inducing ligand is associated with favorable ovarian cancer survival.

Purpose: The molecular determinants of survival in ovarian cancer are poorly understood. Using expression microarrays, we recently found that high expression of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) gene is associated with prolonged survival in advanced ovarian cancer. TRAIL has also been shown to synergize with chemotherapeutic agents to induce apoptosis in ovarian cancer cell lines.

Prediction and uncertainty in the analysis of gene expression profiles.

We have developed a complete statistical model for the analysis of tumor specific gene expression profiles. The approach provides investigators with a global overview on large scale gene expression data, indicating aspects of the data that relate to tumor phenotype, but also summarizing the uncertainties inherent in classification of tumor types. We demonstrate the use of this method in the context of a gene expression profiling study of 27 human breast cancers.