Breast cancer care requires multimodal reasoning across a clinical workflow spanning screening, diagnosis, and treatment planning. However, existing medical MLLMs are typically limited to isolated modalities or narrow task settings, restricting their ability to support workflow-level clinical reasoning. To address this gap, we introduce BreastStage, a workflow-aligned breast imaging instruction corpus containing 1.86M instruction-following pairs from 17 sub-datasets, 5 imaging modalities, and 136 task templates, together with BreastStage-Bench, a benchmark for evaluating multimodal reasoning across the breast cancer care continuum. Built upon this corpus, we propose BreastGPT, a unified MLLM with a dual-branch visual encoder and concept-preserving token compression to bridge standard radiology and gigapixel pathology. On BreastStage-Bench, BreastGPT achieves 75.66% closed-ended accuracy and 89.92% open-ended score, outperforming both general-purpose and medical-specific MLLMs across clinical stages and task formats. Data, code, and model checkpoints are publicly released at https://anonymous.4open.science/w/BreastGPT_io/.