Abstract LB-165: Personalized prognostic prediction models for breast cancer recurrence and survival incorporating multidimensional data

Abstract

Abstract Background: In this study, we developed integrative, personalized prognostic models for breast cancer recurrence and overall survival (OS) that consider receptor subtypes, epidemiological data, quality of life (QoL), and treatment. Methods: 15,314 women with stage I to III invasive primary breast cancer treated at The University of Texas MD Anderson Cancer Center between 1997 and 2012 was used to generate prognostic models by Cox regression analysis including 10,809 women as discovery population (median follow-up: 6.09 years, 1,144 recurrence and 1,627 deaths) and 4,505 women as validation population (median follow-up: 7.95 years, 684 recurrence and 1,095 deaths). Model performance was assessed by calculating the area under the curve (AUC) and calibration analysis and compared with Nottingham Prognostic Index (NPI) and PREDICT. Results: In addition to the known clinical/pathological variables, the model for recurrence included alcohol consumption while the model for OS included smoking status and physical component summary score. The AUCs for recurrence and OS were 0.813 and 0.810 in the discovery and 0.807 and 0.803 in the validation, respectively, compared to AUC of 0.761 and 0.753 in discovery and 0.777 and 0.751 in validation for NPI. Our model further showed better calibration compared to PREDICT. We also developed race-specific and receptor subtype-specific models with comparable AUC. Racial disparity was evident in the distributions of many risk factors and clinical presentation of the disease. Conclusions: Our integrative prognostic models for breast cancer exhibit high discriminatory accuracy and excellent calibration and are the first to incorporate receptor subtype, epidemiological and QoL data. Citation Format: Xifeng Wu, Yuanqing Ye, Carlos H. Barcenas, Wong-Ho Chow, Qing H. Meng, Mariana Chavez Mac Gregor, Michelle Hildebrandt, Hua Zhao, Xiangjun Gu, Yang Deng, Elizabeth A. Wagar, Francisco J. Esteva, Debu Tripathy, Gabriel N. Hortobagyi. Personalized prognostic prediction models for breast cancer recurrence and survival incorporating multidimensional data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-165. doi:10.1158/1538-7445.AM2017-LB-165