No abstract

GDCRNATools is an R/Bioconductor package that is freely available at Bioconductor (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html). Detailed instructions, manual and example code are also available in Github (https://github.com/Jialab-UCR/GDCRNATools).

Tissue samples from many diseases have been used for gene expression profiling studies, but these samples often vary widely in the cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue component can be estimated from the profiling data and used to triage samples before studying correlations with disease parameters. Four large gene expression microarray data sets from prostate cancer, whose tissue components were estimated by pathologists, were used to test the performance of multivariate linear regression models for in silico prediction of major tissue components. Ten-fold cross-validation within each data set yielded average differences between the pathologists' predictions and the in silico predictions of 8% to 14% for the tumor component and 13% to 17% for the stroma component. Across independent data sets that used similar platforms and fresh frozen samples, the average differences were 11% to 12% for tumor and 12% to 17% for stroma. When the models were applied to 219 arrays of “tumor-enriched” samples in the literature, almost one quarter were predicted to have 30% or less tumor cells. Furthermore, there was a 10.5% difference in the average predicted tumor content between 37 recurrent and 42 nonrecurrent cancer patients. As a result, genes that correlated with tissue percentage generally also correlated with recurrence. If such a correlation is not desired, then some samples might be removed to rebalance the data set or tissue percentages might be incorporated into the prediction algorithm. A web service, “CellPred,” has been designed for the in silico prediction of sample tissue components based on expression data.

Anchorage independence and motility are hallmarks of tumor cell growth. Tumor cell growth and morphology can be normalized by contact with nontransformed cells. The Src tyrosine kinase phosphorylates specific sites on the focal adhesion adaptor protein Crk-associated substrate (Cas) to promote nonanchored cell growth and migration. We studied the effects of Src and Cas on the expression of >14,000 genes to identify molecular events that underlie these activities. Gene expression in tumor cells that were normalized by neighboring nontransformed cells was used as an additional filter to identify genes that control metastatic cell growth. This process enabled the identification of genes that play roles in anchorage-independent cell growth and migration. One candidate, four and a half LIM domains 1 (Fhl1), acts as a transcriptional regulator that can associate with cell junctions as well as with the nucleus. We show here that Src phosphorylates Cas to block Fhl1 expression. In addition, suppression of Fhl1 is required for Src to promote tumor cell growth. These data show that Fhl1 is a tumor suppressor gene that acts downstream of Src and Cas to specifically block anchorage-independent cell growth and migration. Moreover, Fhl1 was suppressed in tumors from several human tissues. Thus, identification of how Fhl1 controls fundamental aspects of tumor cell growth and metastasis may lead to the development of novel markers that can be used to diagnose human clinical specimens as well as open innovative avenues of investigations aimed at developing reagents that target cancer cells while minimizing damage to normal cells. (Cancer Res 2006; 66(3): 1543-52)