Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing

Human Genomics, Feb 2015

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.

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Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing

Liu et al. Human Genomics Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing Suqin Liu 1 3 Hongjiang Wang 1 3 Lizhi Zhang 1 3 Chuanning Tang 2 3 Lindsey Jones 0 3 Hua Ye 2 3 Liying Ban 1 3 Aman Wang 1 3 Zhiyuan Liu 2 3 Feng Lou 2 3 Dandan Zhang 2 3 Hong Sun 2 3 Haichao Dong 2 3 Guangchun Zhang 2 3 Zhishou Dong 2 3 Baishuai Guo 2 3 He Yan 2 3 Chaowei Yan 2 3 Lu Wang 2 3 Ziyi Su 2 3 Yangyang Li 2 3 Xue F Huang 0 3 Si-Yi Chen 0 3 Tao Zhou 1 3 0 Norris Comprehensive Cancer Center, Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California , Los Angeles, CA , USA 1 The First Affiliated Hospital of Dalian Medical University , Dalian, Liaoning , China 2 San Valley Biotechnology Incorporated , Beijing , China 3 Comprehensive Cancer Center, Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California , Los Angeles, CA , USA 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. Breast cancer; Genetic mutations; Ion torrent sequencing; Targeted therapy; Personalized medicine - Introduction Breast cancer is the second most common malignancy worldwide and the most frequent in women. Roughly 1.67 million new cases and 522,000 deaths were reported globally in 2012, making breast cancer the fifth leading cause of cancer death. 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]. Patient screening is superior in the US than in China [2], which may account for a higher incidence despite a much smaller population. While risk factors for developing breast cancer include ethnicity, older age, and environmental factors, lifestyle and diet also 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 [3-5]. 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+ (HR+/HER2+), triple negative (HR/HER2), or HER2 overexpressing (HR/HER2+) [6]. Together luminal A and B subtypes account for 65%70% of all breast cancers, whereas 10%15% are triple negative and 10% 20% are HER2 overexpressing [7]. These distinct types of breast cancers all have different characteristics, behaviors, and prognoses and also respond differently to drug treatments. Nearly three quarters of all breast cancers are ER+ and are therefore in some way dependent on estrogen for growth, providing a useful target for treating these cancers via ER modulators or downregulators or aromatase inhibitors [8]. But only 20%40% of patients with advanced ER+ breast cancer have a response to endocrine therapy, which only averages 8 to 14 months [9]. Luminal A types tend to have the best outcome with a 95% 5-year survival rate, whereas luminal B tumors, which tend to (...truncated)


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Suqin Liu, Hongjiang Wang, Lizhi Zhang, Chuanning Tang, Lindsey Jones, Hua Ye, Liying Ban, Aman Wang, Zhiyuan Liu, Feng Lou, Dandan Zhang, Hong Sun, Haichao Dong, Guangchun Zhang, Zhishou Dong, Baishuai Guo, He Yan, Chaowei Yan, Lu Wang, Ziyi Su, Yangyang Li, Xue F Huang, Si-Yi Chen, Tao Zhou. Rapid detection of genetic mutations in individual breast cancer patients by next-generation DNA sequencing, Human Genomics, 2015, pp. 2, 9, DOI: 10.1186/s40246-015-0024-4