Pan-Cancer Classification System with Explainable AI Interpretation: A Feasibility Study

Cancer genomics identifies all genes playing critical roles in carcinogenesis. The state-of-the-art cancer genomics profiling characterized many clinically and biologically relevant patterns that are not resolvable by morphology nor distinguishable under the microscope for cancer diagnosis. With that genomic information, doctors can develop an individualized treatment plan for cancer patients and provide precision medicine. However, several technical challenges (such as low tumor purity, batch effects and formalin-fixed, paraffin-embedded (FFPE) tissue restoration) potentially led to ambiguous diagnoses that needed to be solved in the clinical setting. The purpose of this study is to develop a robust tumor classification framework to improve cancer diagnosis and provide Explainable Artificial Intelligence (XAI) based interpretable results with increased transparency model interpretability of the classification. We utilized a large set of over six thousand tumor samples (DNA methylation and gene expression) from The Cancer Genome Atlas (TCGA). We implemented realistic variable selection by separating the training and test datasets and removed artificial and technical sources of variabilities to overcome batch effect issues while identifying the biological variation and making the prediction meaningful and robust. The Random Forest classifier produced about 95 and 96% accuracy for mRNA and methylation-based models respectively with minimum features of 50 methylation probes and gene expression signatures. We further developed an XAI strategy and applied it to a large brain cancer patient group to make an explainable patient-specific decision while tailoring the provided recommendations based on each patient's characteristics. This strategy demonstrates more accurate and practical molecular subtype classification with explainable AI for model interpretation.