Tumor budding with graph neural networks

Aim

Tumor budding and CD8+ lymphocytes, also known as cytotoxic T-cells, are essential factors in colorectal cancer. By studying them together, we can gain a complete understanding of the micro-environment and the biological response. Our aim is to use graph-based deep learning to investigate their spatial layout as a potential risk predictor for pT1 and stage II CRC patients. Graphs will be used to model the topology of the tumor buds and T-cells, providing a more comprehensive assessment that includes the cells' structural arrangement in addition to their raw count. To stratify patients, we will train Graph Neural Networks (GNNs) on the graph representations. For pT1 patients, we hope that this system will help pathologists make better-informed decisions on whether patients are high-risk and need colon resection, as these cancers are minimally invasive. For stage II patients, we have observed high variability in survival rates and aim to predict them better, as high-risk patients could benefit from adjuvant chemotherapy. By combining tumor budding and T-cell infiltration with graph-based deep learning, we hope to develop a more accurate risk stratification tool that can improve patient outcomes.