A nomogram based on genetic and clinicopathologic features to predict the nonsentinel lymph node metastases in Chinese women breast cancer patients.

e12577 Background: Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals 20–60 % of patients without non-sentinel lymph node metastasis (NSLNM) after ALND. Unnecessary ALND increases the risk of lymphedema and detracts from quality of life. In this study, we expect to develop a nomogram based on genetic and clinicopathologic features to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients. Methods: This retrospective study collected data from 310 women breast cancer patients who underwent SLNB followed by ALND and without any neo-adjuvant therapy in Chinese PLA General Hospital, China, between 2016 and 2017. Genetic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility and prognosis based on the GWAS studies and clinical information. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the gene functions and signaling pathways. The polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Least absolute shrinkage and selection operator (LASSO) regression model was adopted for feature selection and nomogram construction. Internal validation was performed and the area under ROC curve (AUC) was assessed. Results: 81 patients of 310 patients (26%) had a positive axillary NSLNM. The LASSO regression analysis identified the clinicopathologic characteristics including molecular subtype, cN-stage, number of positive SLNs and number of negative SLNs as significant predictors of NSLNM. Furthermore, two SNP clusters were also showed statistically significant in the prediction of NSLNM. In internal validation, the average AUC of the nomogram was 0.795 and the model was well calibrated. Conclusions: We present a new nomogram by combining genetic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLNM in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.