Genetic analysis identifies the HMMR gene as a modifier of the breast cancer risk associated with BRCA1 gene mutation, while cell biological analysis of the protein product suggests a function in regulating development of the mammary gland.

Endocrine therapies targeting the proliferative effect of 17b-estradiol through estrogen receptor a (ERa) are the most effective systemic treatment of ERa-positive breast cancer. However, most breast tumors initially responsive to these therapies develop resistance through molecular mechanisms that are not yet fully understood. The longterm estrogen-deprived (LTED) MCF7 cell model has been proposed to recapitulate acquired resistance to aromatase inhibitors in postmenopausal women. To elucidate this resistance, genomic, transcriptomic and molecular data were integrated into the time course of MCF7-LTED adaptation. Dynamic and widespread genomic changes were observed, including amplification of the ESR1 locus consequently linked to an increase in ERa. Dynamic transcriptomic profiles were also observed that correlated significantly with genomic changes and were predicted to be influenced by transcription factors known to be involved in acquired resistance or cell proliferation (for example, interferon regulatory transcription factor 1 and E2F1, respectively) but, notably, not by canonical ERa transcriptional function. Consistently, at the molecular level, activation of growth factor signaling pathways by EGFR/ERBB/AKT and a switch from phospho-Ser118 (pS118)-to pS167-ERa were observed during MCF7-LTED adaptation. Evaluation of relevant clinical settings identified significant associations between MCF7-LTED and breast tumor transcriptome profiles that characterize ERa-negative status, early response to letrozole and tamoxifen, and recurrence after tamoxifen treatment. In accordance with these profiles, MCF7-LTED cells showed increased sensitivity to inhibition of FGFR-mediated signaling with PD173074. This study provides mechanistic insight into acquired resistance to endocrine therapies of breast cancer and highlights a potential therapeutic strategy.

Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/

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