Introduction. Breast cancer can be divided into several subgroups characterized by unique patterns of pathway activation. Platelet-derived growth factor receptor (PDGFR) signalling has not yet been included in this classification scheme, although it has been reported to be a potential target for therapy. In this study, we have constructed a PDGFR-activation signature and investigated its relevance in breast cancer. Materials and Methods. Sixteen PDGFR-modulated genes were identified by intersecting two published PDGFR-modulated gene lists. The resulting gene signature was applied onto a publicly available gene expression data set of GIST (GSE17743) using principle component analysis. The segregation of PDGFR- and KIT-mutated GIST samples was investigated using permutation analysis and classification sensitivity and specificity were assessed. Using the regression coefficients from the first principal component, a PDGFR-activation score was constructed and applied onto a second data set in order to validate the score (GSE1923). Finally, the score was applied onto a gene expression data set of 389 breast cancer ***samples, including 137 samples from patients with IBC. Results. Sixteen PDGFR-modulated genes (NR4A1, EGR3, JUNB, IER3, TIEG, JUN, BCL3, MYC, NR4A3, PLAU, MCL1, DUSP1, DUSP5, DUSP6, SGK, GADD45A) were able to discriminate PDGFR-mutated GIST samples from KIT-mutated GIST samples with a sensitivity of 75% and a specificity of 85%. Application of the PDGFR-activation score onto a data set of control and PDGF-treated glioblastoma cells showed a significant increase in the PDGFR-activation score in the treated condition (P=0.0302). Application of the PDGFR-signature onto our series of IBC and nIBC samples demonstrated a significant and molecular subtype-independent increase in PDGFR-activation in IBC (P=0.0015; FDR=3%). In addition, in our series of nIBC samples only, PDGFR-activation was associated with decreased DMFS and RFS (P=0.0038 and P=0.0137 respectively). In fact, PDGFR-activation was an independent prognosticator in a multivariate model incorporating the molecular subtypes. Discussion. We identified a gene signature composed of 16 genes able to predict PDGFR-activation in tissue samples by gene expression analysis. PDGFR-activation is significantly increased in samples from patients with IBC, an aggressive form of locally advanced breast cancer. In addition, in nIBC, PDGFR-activation is associated with DMFS and RFS, independently of the molecular subtypes suggesting that PDGFR-activation might add another level of clinically relevant heterogeneity in breast cancer. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-01-01.