Abstract
Breast cancer is the most common malignancy in women and the leading cause of cancer deaths in women worldwide. Breast cancers are heterogenous and exist in many different subtypes (luminal A, luminal B, triple negative, and human epidermal growth factor receptor 2 (HER2) overexpressing), and each subtype displays distinct characteristics, responses to treatment, and patient outcomes. In addition to varying immunohistochemical properties, each subtype contains a distinct gene mutation profile which has yet to be fully defined. Patient treatment is currently guided by hormone receptor status and HER2 expression, but accumulating evidence suggests that genetic mutations also influence drug responses and patient survival. Thus, identifying the unique gene mutation pattern in each breast cancer subtype will further improve personalized treatment and outcomes for breast cancer patients. In this study, we used the Ion Personal Genome Machine (PGM) and Ion Torrent AmpliSeq Cancer Panel to sequence 737 mutational hotspot regions from 45 cancer-related genes to identify genetic mutations in 80 breast cancer samples of various subtypes from Chinese patients. Analysis revealed frequent missense and combination mutations in PIK3CA and TP53, infrequent mutations in PTEN, and uncommon combination mutations in luminal-type cancers in other genes including BRAF, GNAS, IDH1, and KRAS. This study demonstrates the feasibility of using Ion Torrent sequencing technology to reliably detect gene mutations in a clinical setting in order to guide personalized drug treatments or combination therapies to ultimately target individual, breast cancer-specific mutations.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-015-0024-4) contains supplementary material, which is available to authorized users.
Highlights
Breast cancer is the second most common malignancy worldwide and the most frequent in women
PIK3CA mutations and TP53 mutations were the most prevalent (32.5% and 10.0%, respectively), and mutations were identified in BRAF, GNAS, IDH1, KRAS, and PTEN all at a frequency of 1.3% (Figure 3)
While the study of Kalinsky et al found overall patient survival to be significantly improved in patients with PIK3CA mutations [25], we found that patients with PIK3CA mutations collectively had a roughly equal disease-free survival (DFS) time to patients with WT PIK3CA (38.6 vs. 38.2 months, respectively); patients with triple negative and human epidermal growth factor receptor 2 (HER2)-overexpressing tumors and PIK3CA mutations had a shorter average DFS (34.5 and 20 months, respectively) than those with WT PIK3CA (39 and 37 moths, respectively), possibly suggesting that the other characteristics of the tumor may have greater prognostic value than PIK3CA mutations
Summary
Breast cancer is the second most common malignancy worldwide and the most frequent in women. Breast cancer incidence differs with population and geographic location, where China alone accounted for more than 187,000 cases and nearly 48,000 deaths in 2012, whereas over 230,000 cases and more than 43,000 deaths were reported in the US [1]. While risk factors for developing breast cancer include ethnicity, older age, and environmental factors, lifestyle and diet play a significant role, where westernization in Asia is thought to have contributed to the rise in spontaneous breast cancer incidence in Chinese populations over the last 20 years [3,4,5]. Breast cancers are highly heterogenous and may display different characteristics of hormone receptors (HR) (estrogen receptor (ER) and progesterone receptor (PR)) and human epidermal growth factor receptor 2 (HER2) status, and together this information helps to distinguish different types of breast cancers: luminal A (HR+/HER2−, tumor grade 1 or 2), luminal B/HER2− (HR+/HER2−, tumor grade 3 or 4), luminal B/HER2+
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