ORIGINAL RESEARCH Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns

Abstract: Molecular stratifi cation of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifi cations can be shown to be stable against variations in sample source and data perturbation. Classifi cations inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We fi nd that within the now commonly accepted BCA categories identifi ed by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36 % of the samples identifi ed as Luminal B and 55% identifi ed as ERBB2+ cannot be assigned an accurate category because the classifi cation is sensitive to data perturbation. We identify a “core cluster ” of samples for each category, and from these we determine “patterns ” of gene expression that distinguish the core clusters from each other. We fi nd that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodel-ing and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We fi nd, as expected, that the classifi cation of samples identifi ed as Luminal A an