Establishment of Prognostic Nomograms for Predicting the Survival of HR-Positive, HER2-Negative Metastatic Breast Cancer Patients Treated with Everolimus

Fangfang Duan,* Chenge Song,* Yuyu Ma, Kuikui Jiang, Fei Xu, Xiwen Bi, Jiajia Huang, Ruoxi Hong, Zhangzan Huang, Qianyi Lu, Zhongyu Yuan, Shusen Wang, Wen Xia Departments of Medical Oncology, Sun Yat-sen University Cancer Center, The State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen XiaDepartment of Medical Oncology, Sun Yat-sen University Cancer Center, The State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, Guangdong, 510060, People’s Republic of ChinaEmail xiawen@sysucc.org.cnBackground: There are no clinically available prognostic models for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer treated with everolimus. We aimed to develop a tool to predict the progression-free survival (PFS) and overall survival (OS) of these patients and to identify optimal candidates who would benefit from everolimus-based treatment in this heterogeneous patient population.Methods: The clinical data of patients with HR+, HER2- metastatic breast cancer receiving everolimus between May 2012 and January 2018 at Sun Yat-sen University Cancer Center were retrospectively retrieved. Based on potential prognostic factors derived from multivariate Cox analysis, we established predictive nomogram models for PFS and OS and evaluated their predictive values by means of the concordance index (C-index). Calibration curves were used to estimate the consistency between the actual observations and the nomogram-predicted probabilities.Results: A total of 116 patients with HR+, HER2- metastatic breast cancer were enrolled in this study. Three independent prognostic factors, including the line of everolimus in the metastatic setting, everolimus clinical benefit rate and number of liver metastatic lesions, were identified from the multivariate Cox analysis. Prognostic models for individual survival prediction were established and graphically presented as nomograms. The C-index was 0.738 (95% confidence interval [CI]: 0.710– 0.767) for the PFS nomogram and 0.752 (95% CI: 0.717– 0.788) for the OS nomogram, which showed favourable discrimination. The calibration curves for the probabilities of 6-, 9-, and 12-month PFS and 1-, 2-, and 3-year OS suggested satisfactory consistency between the actual observations and the predicted probabilities.Conclusion: We constructed convenient nomogram models for patients with HR+, HER2- metastatic breast cancer to individually predict their potential benefits from everolimus in the metastatic setting. The models showed good performance in terms of accuracy, discrimination capacity and clinical application value.Keywords: prognostic nomogram, metastatic breast cancer, everolimus, progression-free survival, overall surviva