An Integrated Approach to Knowledge and Prediction Modeling of Breast Cancer Metastasis Using Gene Regulatory...

Understanding why only some breast cancers become metastatic, and predicting metastatic risks at earlier cancer stages, are two major goals of breast cancer research. These goals are clearly connected and synergistic, but are rarely integrated within a single study. Knowledge discovery-oriented research has identified critical biological pathways related to metastasis, but the interplay between these pathways remains elusive, resulting in models that lack prediction accuracy. Conversely, complex machine learning models achieve high prediction accuracy without detailed biological knowledge, making the explanation challenging. Here, we propose a novel computational framework that addresses both goals simultaneously. To support knowledge discovery, we construct gene regulatory networks (GRNs) to model the cellular states of metastatic and non-metastatic patients. To enable explainable metastasis prediction, we introduce a dysregulation score based on the GRN models. Experimental results demonstrate that our method not only identified significant metastasis-associated GRN changes, but also revealed the loss of co-regulation among key biological processes in metastatic patients. Leveraging the dysregulation score, our model-free classifier outperformed complex machine learning models under rigorous evaluation. This work bridges a significant gap between knowledge discovery and accurate, explainable prediction by employing carefully designed knowledge models and knowledge-based prediction, with potential applicability in other disease contexts.