Background: Breast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Indeed, gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, Luminal A, Luminal B, Her2, Normal-like and Basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. It is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.

Methods: We have developed a user-friendly web-based system to allow the identification and evaluation of genes that are significantly associated with disease progression and survival for breast cancer in general and also with respect to molecular subtype. The underlying algorithm combines gene expression data from multiple DNA microarray experiments and detailed clinical data to correlate outcome with gene expression levels. This algorithm integrates gene expression and survival data from 21 datasets on 10 different microarray platforms corresponding to 20,017 gene sequences across 3,519 samples.

Results: We demonstrate the robustness of our approach in comparison to two commercially available prognostic tests in breast cancer. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the complexity and cost of these multi-index assays. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype of breast cancer, identifying seven receptor tyrosine kinases associated with poor disease-free survival and/or overall survival in this subtype (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFRβ, ROS, TIE1). A web application for using this algorithm is currently available at

Conclusions: BreastMark is a useful tool for examining putative prognostic markers at the RNA level in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer as a whole and within its molecular subtypes. It will be of particular use to clinical and academic research groups with limited bioinformatics facilities.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P3-04-12.