Somatic Mutation-Driven Cancer Stage Classification via Autoencoder Clustering and Explainable Deep Learning

The identification of the site of origin, stage and driver genes of cancer is important for the improved prognosis of cancer patients. Publicly available somatic mutation data and machine learning techniques provide a framework for cancer stage classification. However, due to the heterogeneity and sparsity of somatic mutation data, classifying cancer stages is challenging. Researchers are keen to employ Artificial Intelligence (AI), and specifically Machine Learning (ML) techniques to detect cancer in the early stages. Most of the models provided, however, are very specific to particular cancer types and do not generalise. We propose a pipeline named Clustering with Latent Autoencoder Representations and Interpretable Technology (CLARITY) for somatic mutation analysis, which works in two ways. First, we developed an autoencoder-based clustering algorithm for desparsified somatic mutation data to identify cancer stages based on cancer hallmark scores. Second, we created a Deep Neural Network (DNN) that is tightly integrated with autoencoder-based representation learning and explainable AI (XAI) to classify these stages and interpret the hallmarks that influence cancer progression. These hallmarks are candidates for biomarkers.We demonstrate CLARITY on Breast Invasive Carcinoma (BRCA), achieving 99.47% accuracy and outperforming state-of-the-art methods. Furthermore, we show the robustness of our performance using statistical analysis, traditional ML algorithms and data visualisation techniques. This demonstrates how well our clustering algorithm can identify cancer subtypes and how our classifier handles sparse and noisy somatic mutation data.