Digital pathology has enabled advanced cancer analysis, yet traditional patch-based methods struggle to capture complex, topological structures in whole-slide images (WSIs). This study introduces a graph-based framework using graph neural networks (GNNs) to model histopathology samples as multi-level cell graphs, integrating both cell- and disease-level information for improved classification. Our approach encompasses five cancer types and two staining protocols, modeling each sample as a graph to capture spatial and phenotypic relationships between cells and diseases. We implemented this framework using graph random neural networks (GRAND) [1], achieving a cell-level classification accuracy of 88% and a disease-level accuracy of 83%, significantly outperforming CNN and XGBoost baselines [2, 3]. These results emphasize the potential of GNNs to generalize across multiple cancers and staining methods, providing a valuable diagnostic tool for pathologists. Future research will extend this model to additional cancer types and explore its applicability and robustness in diverse clinical settings, with the goal of achieving interpretable and scalable computational pathology solutions [4].