Deep learning-based survival prediction using DNA methylation-derived 3D genomic information

Three-dimensional (3D) genome states are closely related to cancer development. Nonetheless, the 3D genome information has not been clinically utilized to the best of our knowledge, due to the costly production of Hi-C data which is a manifest source of 3D genome information. Therefore, there is a need for a novel metric computable from a 3D genome-related data which is more easily accessible for the clinical utilization of 3D genome information. We here propose a method to extract 3D genome-aware epigenetic features from DNA methylation data and use these features for a deep learning-based survival prediction. These features are derived from the 3D genome structures which are rebuilt from the DNA methylation data in an individual level. The results showed that usage of 3D genome-aware features contributed to more accurate risk prediction across seven cancer types, suggesting the effectiveness of the knowledge about 3D genome structure embedded in these features. The deeper biological investigation revealed that altered DNA methylation level in risk-high group could be related to the anomalously activated genes involved in cancer-related pathways. Altogether, the risks predicted from 3D genome-aware epigenetic features showed its significance as a survival predictor in seven cancer types, along with its biological importance.