Whole-slide images contain diagnostic cues spanning local neighborhoods, distant regions, and hierarchical tissue organization, but existing graph and MIL models do not unify these relations effectively. This paper proposes a prototype-aware heterogeneous graph attention network that lets each region interact with diverse heterogeneous neighbors while guiding slide-level representation learning with multilevel prototypes. The framework strengthens whole-slide classification by jointly modeling local, non-local, and hierarchical structure within a single representation space.