Abstract
Abstract The insulin-like growth factor (IGF) pathway mediates aberrant function during the initiation and progression of primary tumors and secondary metastases in cancer. As a result, a host of tyrosine kinase inhibitors and monoclonal antibodies directed against the type 1 IGF receptor (IGF-1R) have entered clinical trials with early positive results. However, no predictive biomarkers have yet emerged from these initial studies. We propose that expanding IGF biomarkers beyond IGF-1R alone may identify the most appropriate candidates for anti-IGF therapy. Previous work has shown that the insulin receptor substrate (IRS) proteins serve as the functional link between IGF-I-induced IGF-1R phosphorylation and downstream signaling linked to cellular behavior. Our work has demonstrated that IRS isoforms differentially mediate IGF-I action, whereby IRS-1 drives proliferation and IRS-2 triggers motility. Here we employed the T47D-YA (IRS null) breast cancer cell line and T47D-YA/IRS clones stably transfected with IRS-1 or IRS-2. In response to acute (4h) and chronic (24h) IGF-I stimulation, global gene expression patterns were assessed by Affymetrix U133 Plus 2.0 microarray analysis. Analysis revealed that IGF-1R activation alone was insufficient to affect gene expression as no genes were regulated by IGF-I in T47D-YA cells. Conversely, ligand stimulation of IRS-1 and IRS-2 clones induced or repressed hundreds of transcripts in both overlapping and distinct fashions. Direct comparison of IRS-1 to IRS-2 clones revealed a number of early (4h) IRS-2 genes linked to metastasis and late (24h) IRS-1 proliferative genes. Interestingly, a 10-fold upregulation in the transforming growth factor beta 2 (TGFβ2) gene by IGF-I in IRS-2 cells suggested a link between the IRS-derived gene signatures and the TGFβ pathway known to regulate breast cancer metastasis. We then compared our arrays with published IGF-I (MCF-7) and TGFβ-derived (MCF10A, MDA-231, HaCaT, HPL1) microarrays to find a list of commonly regulated genes and performed cluster analysis to reveal consistent patterns of gene expression (Creighton el al 2008 & Padua et al 2008). We found 75 genes that were regulated in common between these signatures. To explore the clinical relevance of the signatures we developed, we examined the NKI-295 dataset used to establish the 70-gene profile of prognosis and found 71 genes regulated in common between all four datasets (van de Vijver et al 2002). Strikingly, we discovered that patient survival was heavily influenced by the degree to which tumor expression correlated to the conserved signatures. A high degree of correlation resulted in the poorest disease free survival and an inverse correlation resulted in an improved disease free survival. Our data suggest that IGF stimulation of breast cancer cells results in distinct profiles of gene expression that are dependent on IRS adaptor protein expression. In addition, some of the “IRS-regulated” genes are shared in common with other gene signatures of poor prognosis. With the use of anti-IGF therapies in breast cancer, attention should focus on the use of these profiles as prognostic and predictive biomarkers. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3031.
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