This study explores the application of computational pathology to analyze pediatric brain tumors utilizing hematoxylin and eosin (H&E) and immunohistochemistry (IHC) whole slide images (WSIs). Experiments were conducted on H&E images for predicting tumor diagnosis and fusing them with unregistered IHC images to investigate potential improvements. Patch features were extracted using UNI, a vision transformer (ViT) model trained on H&E data, and whole slide classification was achieved using the attention-based multiple instance learning CLAM framework. In the astrocytoma tumor classification, early fusion of the H&E and IHC significantly improved the differentiation between tumor grades (balanced accuracy: 0.82±0.05vs 0.84 ± 0.05). In the multiclass classification, H&E images alone had a balanced accuracy of 0.79 ± 0.03 without any improvement obtained when fused with IHC. The findings highlight the potential of using multi-stain fusion to advance the diagnosis of pediatric brain tumors, however, further fusion methods should be investigated.