The purpose of this study was to investigate the relationship between proliferation and immune gene signatures.

Gene expression signatures indicative of tumor proliferative capacity and tumor-immune cell interactions have emerged as principal biology-driven predictors of breast cancer outcomes. The aim of this study was to investigate how these signatures relate to one another in biological and prognostic contexts.

Data represent different patients’ populations from 16 medical centers in the USA, Europe, and Asia. Raw data (CEL files) were extracted from Gene Expression Omnibus (GSE1456, GSE2034, GSE5327, GSE12093, GSE7390, GSE6532, GSE9195, GSE2603, GSE7378, GSE8193, GSE4922, GSE11121, and GSE45255).

Of the initial 2,116 tumor profiles as described in Nagalla et al, 2,034 profiles represent primary invasive breast tumors sampled at the time of surgical resection, without exposure to neoadjuvant treatment. Of these, 1,954 cases were annotated with DMFS time and event. Other clinical annotation such as treatment type, ER status, nodal status, tumor size, histologic grade and patient age were available for the majority of cases. Of these, a final total of 1,839 cases were available via GEO and have been uploaded in this instance of GXB.

Raw array data (CEL files) were pre-processed and normalized using the R software package and library files provided by the Bioconductor project. In order to preserve a consistent normalization strategy across all study populations, raw data were MAS5.0 normalized on individual study populations using the justMAS function in the simpleaffy library from Bioconductor (no background correction, mean target intensity of 600). The specific array platforms employed were the HG-U133A, HG-U133 PLUS 2.0 and HG-U133A2 gene chips. To ensure equal information content from each chip type, only probe sets common to all chip types were utilized in subsequent analysis. This resulted in the use of 22,268 probe sets that were common to all microarrays in all study populations. Cross-population batch effects were corrected using the COMBAT empirical Bayes method.

Nagalla, Srikanth, Jeff W. Chou, Mark C. Willingham, Jimmy Ruiz, James P. Vaughn, Purnima Dubey, Timothy L. Lash, et al. 2013. “Interactions between Immunity, Proliferation and Molecular Subtype in Breast Cancer Prognosis.” Genome Biology 14 (4): R34. doi:10.1186/gb-2013-14-4-r34.