Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy
Abstract
Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer with a 5-year survival limited to approximately 40%. Diagnosis, based on clinical and/or pathological criteria, may be difficult. Optimal systemic neoadjuvant therapy and accurate predictors of pathological response have yet to be defined for increasing response rate and survival. Using DNA microarrrays containing approximately 8,000 genes, we profiled breast cancer samples from 81 patients, including 37 with IBC and 44 with noninflammatory breast cancer (NIBC). Global unsupervised hierarchical clustering was able to some extent to distinguish IBC and NIBC cases and revealed subclasses of IBC. Supervised analysis identified a 109-gene set the expression of which discriminated IBC from NIBC samples. This molecular signature was validated in an independent series of 26 samples, with an overall performance accuracy of 85%. Discriminator genes were associated with various cellular processes possibly related to the aggressiveness of IBC, including signal transduction, cell motility, adhesion, and angiogenesis. A similar approach, with leave-one-out cross-validation, identified an 85-gene set that divided IBC patients with significantly different pathological complete response rate (70% in one group and 0% in the other group). These results show the potential of gene expression profiling to contribute to a better understanding of IBC, and to provide new diagnostic and predictive factors for IBC, as well as for potential therapeutic targets.