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Purpose: Sclerosing adenosis (SA), found in ¼ of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA., Methods: The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation., Results: Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67)., Conclusions: Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available.

Patients with the triple negative subtype of breast cancer have an overall poor outcome, with earlier relapses, distinct patterns of metastases, and lack of specific targets for treatment selection. Classification of these tumors has begun to be modified by inclusion of immunohistochemistry for various markers, and gene profiling. Further characterization of this subtype of breast cancer may aid in the identification of new targeted therapies. Anthracyclines and taxanes remain the standard of care in the adjuvant setting. However, novel anti-angiogenesis, anti-tubulin, and DNA repair agents are already under evaluation in (neo) adjuvant trials. Molecular characterization is being included in trials to identify optimal adjuvant strategies. The aim of this manuscript is to review data concerning the molecular characterization of triple negative breast cancers as well as the clinical outcomes of treating patients with existing adjuvant treatments, and to highlight newer adjuvant research strategies in development.