Large-scale DNA organization analysis of primary breast cancers for prediction of axillary lymph node metastases

Breast cancer is the leading cause of cancer-related deaths among women worldwide. It is standard practice for patients to undergo a sentinel lymph node biopsy (SLNB) with breast surgery for staging. However, more than 60% of patients with primary operable breast cancers do not have axillary lymph node metastases. Hence, most patients risk complications from SLNB, but do not benefit. There has been considerable research into non-invasive methods for axillary lymph node staging, but few are externally validated. We hypothesized that a new method using large-scale DNA organization (LDO) analysis on a biopsy of the breast primary could predict axillary metastases. LDO, which measures various nuclear characteristics such as the size, shape, and chromatin texture, has been correlated with a variety of clinical outcomes in different cancers, including survival in breast cancer. In this study, we determined that Random Forest models performed best for LDO analysis. On an external test set, our models using LDO features alone achieved an area under the curve (AUC) = 0.711 and clinicopathological features alone achieved AUC = 0.741. Our best and final model using LDO and clinicopathological features together achieved AUC = 0.775. We identified several LDO features that were important for predicting axillary lymph node metastases, including nuclear radius and staining intensity. We present the first histologically-based, externally-validated, and explainable machine learning model capable of predicting axillary lymph node metastases preoperatively.