Early cancer detection using higher-order genome architecture and chromatin interactions

Cancer is a complex disease which requires interactions between cell-intrinsic alterations and tumor microenvironment. The connection between epigenetics and genomic structure plays a key role in chromatin interactions and enhancer-promoter communications for transcriptional activities. Alterations of these components in oncogenic signaling pathway potentially cause cancer cell-intrinsic changes and inappropriate instructions to normal cell cycles, leading to abnormal cell growth.
' Topologically associating domains (TADs) and A/B compartments are the main structures of higher-order chromatin structure. These contact domains, chromatin states, super-enhancers, and histone modifications together regulate transcription and gene expression for normal/abnormal cell cycles.
' Several bioinformatics tools were utilized ' FANC for processing raw FASTQ data to Hi-C contact matrices, JuicerTools for obtaining the locations of contact domains on the entire genome, and CoolBox for visualizing chromatin contacts in different cell lines.
' High-resolution chromatin contacts showed dynamic interactions among chromosomal regions in different cell lines.
' Qualitative and quantitative features were comprehensively engineered from 3D chromatin folding and epigenetic regulators using available packages (scikit learn, pytorch, pandas, numpy, matplotlib, etc.).
' XGBoost multi-class classifier achieved the highest accuracy of 80.90% in classifying normal and cancer cell lines based on chromatin interactions, followed by Random Forest at 73.76% and TabNet classifier at 70.00%.