Rates of upgrade to malignancy for 271 cases of flat epithelial atypia (FEA) diagnosed by breast core biopsy.

Flat epithelial atypia (FEA) is a borderline lesion that might represent an early stage in the development of certain low-grade carcinomas in situ and invasive cancers. There are no guidelines on its management. Our objectives were to determine the upgrade to malignancy rate and identify a subpopulation of patients that might undergo just mammographic surveillance. We retrospectively reviewed the data for 271 FEA cases among 5,555 breast core biopsies obtained over a 7-year period (January 2003-2010). We collated clinical data (age, history of cancer, menopausal status), radiological data (lesion type, size, Bi-Rads category), technical data (number of biopsies, needle gauge, excision quality) and histological data and sought correlations between these factors and upgrade rate. The 271 FEA comprised 128 cases of pure FEA, 135 cases of FEA + atypical ductal hyperplasia, and 8 cases of FEA + atypical lobular hyperplasia. Overall, 184 patients underwent surgery and 46 mammographic surveillance. Surgery detected 34 cases of malignancy (23 CIS, 7 invasive cancers, and 4 mixed cases) giving a 15% upgrade rate. Quality of excision was the only factor associated with under-diagnosis. The presence of FEA at biopsy warrants surgery.

A six-gene signature predicting breast cancer lung metastasis.

The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists of using tissue surgically resected from lung metastatic lesions and comparing their gene expression profiles with those from nonpulmonary sites, all coming from breast cancer patients. We show that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a 6-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the 6-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we show that the signature improves risk stratification independently of known standard clinical variables and a previously established lung metastasis signature based on an experimental breast cancer metastasis model.