A generalized non-linear model predicting efficacy of neoadjuvant therapy in HER2+ breast cancer
Yusong Wang,
Xiaoyan Liu,
Keda Yu,
Shouping Xu,
Pengfei Qiu,
Xinwen Zhang,
Mozhi Wang,
Yingying Xu
Affiliations
Yusong Wang
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province 110001, China
Xiaoyan Liu
Department of Breast Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, Liaoning Province 110801, China
Keda Yu
Department of Breast Surgery, Fudan University Shanghai Cancer Center and Cancer Institute, Shanghai 200032, China
Shouping Xu
Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province 150081, China
Pengfei Qiu
Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, Shandong Province 250117, China
Xinwen Zhang
Center of Implant Dentistry, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Disease, Shenyang, Liaoning Province 110001, China
Mozhi Wang
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province 110001, China; Corresponding author
Yingying Xu
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province 110001, China; Corresponding author
Summary: Neoadjuvant therapy (NAT) is currently recommended to patients with human epidermal growth factor receptor 2-positive breast cancer (HER2+ BC) that typically exhibit a poor prognosis. The tumor immune microenvironment profoundly affects the efficacy of NAT. However, the correlation between tumor-infiltrating lymphocytes or their specific subpopulations and the response to NAT in HER2+ BC remains largely unknown. In our study, the immune infiltration status of 295 patients was classified as “immune-rich” or “immune-poor” phenotypes. The “immune-rich” phenotype was significantly positively related to pathological complete response (pCR). Ten genes were correlated with both pCR and the immune phenotype based on the results of spline and logistic regression. We constructed a generalized non-linear model combining linear and non-linear gene effects and successfully validated its predictive power using an internal and external validation set (AUC = 0.819, 0.797; respectively) and a clinical set (accuracy = 0.75).
