Synergistic induction of tumor antigens by Wnt-1 signaling and retinoic acid revealed by gene expression profiling.

Novel drug targets can be identified by differential analysis of RNA transcripts isolated from cancer cell lines and tissues. We have extended this approach by analyzing differences in gene expression resulting from the drug treatment of transformed and nontransformed cells. A mouse mammary epithelial cell line (C57MG), which conditionally expresses the Wnt-1 proto-oncogene, was left untreated or treated with retinoic acid in the presence or absence of Wnt-1 expression. The experiment was performed in triplicate, and RNA extracted from the four samples was analyzed by hybridization to over 12,000 unique oligonucleotide probe sets. Reproducible alterations in gene expression that occurred in response to retinoic acid, Wnt-1, or retinoic acid plus Wnt-1 relative to untreated cells were identified. Greater attention was given to genes encoding cell surface antigens that were selectively up-regulated by the combination of Wnt-1 and retinoic acid. These genes included the tumor necrosis factor family 4-1BB ligand, ephrin B1, stra6, autotaxin, and ISLR. Administration of retinoic acid to mice bearing tumors driven by activation of the Wnt-1/beta-catenin pathway resulted in increased expression of stra6 in the tumors but not in normal tissue. In principal, the therapeutic index of antibodies directed against these antigens should be enhanced by co-administration of retinoic acid.

The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment.

A large-scale effort, termed the Secreted Protein Discovery Initiative (SPDI), was undertaken to identify novel secreted and transmembrane proteins. In the first of several approaches, a biological signal sequence trap in yeast cells was utilized to identify cDNA clones encoding putative secreted proteins. A second strategy utilized various algorithms that recognize features such as the hydrophobic properties of signal sequences to identify putative proteins encoded by expressed sequence tags (ESTs) from human cDNA libraries. A third approach surveyed ESTs for protein sequence similarity to a set of known receptors and their ligands with the BLAST algorithm. Finally, both signal-sequence prediction algorithms and BLAST were used to identify single exons of potential genes from within human genomic sequence. The isolation of full-length cDNA clones for each of these candidate genes resulted in the identification of >1000 novel proteins. A total of 256 of these cDNAs are still novel, including variants and novel genes, per the most recent GenBank release version. The success of this large-scale effort was assessed by a bioinformatics analysis of the proteins through predictions of protein domains, subcellular localizations, and possible functional roles. The SPDI collection should facilitate efforts to better understand intercellular communication, may lead to new understandings of human diseases, and provides potential opportunities for the development of therapeutics.