Prognostic value of a 92-probe signature in breast cancer

Abstract

// Salima Akter 1 , Tae Gyu Choi 1 , Minh Nam Nguyen 1 , Abel Matondo 1 , Jin-Hwan Kim 1 , Yong Hwa Jo 1 , Ara Jo 1 , Muhammad Shahid 1 , Dae Young Jun 1 , Ji Youn Yoo 1 , Ngoc Ngo Yen Nguyen 1 , Seong-Wook Seo 1 , Liaquat Ali 2 , Ju-Seog Lee 3 , Kyung-Sik Yoon 1 , Wonchae Choe 1 , Insug Kang 1 , Joohun Ha 1 , Jayoung Kim 4 , Sung Soo Kim 1 1 Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea 2 Department of Biochemistry and Cell Biology, Bangladesh University of Health Sciences, Dhaka, Bangladesh 3 Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 4 Departments of Surgery and Biomedical Sciences, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA Correspondence to: Sung Soo Kim, e-mail: sgskim@khu.ac.kr Keywords: microarray, gene signature, breast cancer, prognosis Received: December 26, 2014      Accepted: March 10, 2015      Published: April 11, 2015 ABSTRACT Clinical applications of gene expression signatures in breast cancer prognosis still remain limited due to poor predictive strength of single training datasets and appropriate invariable platforms. We proposed a gene expression signature by reducing baseline differences and analyzing common probes among three recent Affymetrix U133 plus 2 microarray data sets. Using a newly developed supervised method, a 92-probe signature found in this study was associated with overall survival. It was robustly validated in four independent data sets and then repeated on three subgroups by incorporating 17 breast cancer microarray datasets. The signature was an independent predictor of patients’ survival in univariate analysis [(HR) 1.927, 95% CI (1.237–3.002); p < 0.01] as well as multivariate analysis after adjustment of clinical variables [(HR) 7.125, 95% CI (2.462–20.618); p < 0.001]. Consistent predictive performance was found in different multivariate models in increased patient population ( p = 0.002). The survival signature predicted a late metastatic feature through 5-year disease free survival ( p = 0.006). We identified subtypes within the lymph node positive ( p < 0.001) and ER positive ( p = 0.01) patients that best reflected the invasive breast cancer biology. In conclusion using the Common Probe Approach, we present a novel prognostic signature as a predictor in breast cancer late recurrences.