The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry analysis. We present BioX-CPath, an explainable graph neural network architecture for Whole Slide Image classification that leverages both spatial and semantic features across multiple stains. At its core, BioX-CPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis (accuracy of 0.90 ±0.019) and Sjogren's Disease (accuracy of 0.84 ±0.018) multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores that align with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Code will be made public upon paper acceptance.