Papillary renal cell carcinoma subtyping requires subtle cellular and cell-layer patterns that conventional CNNs struggle to capture in large histopathological images. This paper proposes an instance-based vision transformer that focuses on top informative nuclei-centered instances and models their relationships with positional and grade-aware embeddings. The design learns finer morphological representations and improves performance on fine-grained pRCC subtyping.