The molecular landscape of early-stage breast cancer with lymph node metastasis
Abstract
Axillary lymph nodes (ALNs) are the primary site of metastasis in breast cancer, and their involvement has implications in disease staging, prognostication, and treatment decisions. A non-invasive modality of assessing the risk of ALN metastasis can improve care in patients with early-stage breast cancer by omitting the morbidity and costs associated with axillary surgery.
This thesis explores the molecular landscape of early-stage breast cancers with ALN metastasis and shows the potential of tumour molecular signatures in predicting ALN involvement. After a systematic review of the literature, we use data from The Cancer Genome Atlas (TCGA) to develop molecular signatures correlated with ALN metastasis. We then use machine-learning to develop predictive models. We show that the predictive performance of models may be improved by accounting for the intrinsic molecular subtype of breast cancer. If validated externally, these models can reduce the rates of axillary surgery in patients with early-stage breast cancer.