Breast cancer is a heterogeneous disease requiring accurate imaging interpretation for diagnosis and treatment planning. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in this setting by capturing tumour vascular dynamics. In this work, we present our contribution to the MAMA-MIA Challenge, which targets primary tumour segmentation (Task 1) and treatment response prediction from pre-treatment DCE-MRI (Task 2). For Task 1, we adopt the nnU-Net framework, combined with a tailored preprocessing pipeline designed to enhance generalisation and fairness across heterogeneous imaging centres and scanner vendors. Our pipeline focuses on extracting meaningful dynamic information from three representative DCE-MRI phases, used as multi-channel input to guide the model in learning temporal enhancement behaviour around the tumour. For Task 2, we extract temporal dynamics features from all phases and combine them with radiomics-based shape descriptors, derived from the predicted tumour mask, to train an XGBoost classifier for (pathological complete response (pCR)) prediction. Our work aligns with the broader goals of developing robust, generalisable, and equitable AI tools for breast cancer imaging using real-world, multi-centre MRI data.