Genomic Approaches to Understanding Chromatin Dysregulation in Liver Cancer

The alterations in chromatin structure that play a critical role in liver cancer progression by driving aberrant gene expression can be studied using genomic approaches, such as CUT&RUN and RNA sequencing. CUT&RUN is a widely used assay that maps proteins and histone modifications across the genome, although normalization methods are poorly standardized, which can substantially influence biological interpretation. RNA sequencing is a valuable assay for characterizing how chromatin dysregulation impacts gene expression in liver cancer, but comprehensive genomic analyses that resolve both the diverse cell types and spatial organization in tissues remains limited. We hypothesized that characterizing the impact of chromatin dysregulation in liver cancer required both optimized normalization methods for chromatin profiling data and gene expression approaches capable of resolving heterogeneous cell types within tumors. To address this, we systematically evaluated normalization strategies for CUT&RUN data from liver cancer cells by comparing approaches based on exogenous spike-in DNA added at known quantities with normalization approaches implemented in the csaw algorithm. We demonstrated that normalization with the algorithm csaw yielded results that better reflected expected chromatin patterns compared with spike-in exogenous controls, with the additional advantage of being widely applicable across analyses. Additionally, we used spatially-resolved RNA sequencing data from mouse liver tumors with altered chromatin remodelling complexes to perform differential expression analyses and biological pathway enrichment analyses. We identified distinct transcription patterns associated with hepatocellular carcinoma cells and cholangiocarcinoma-like cells within individual liver tumors, indicating this model successfully resolved heterogeneous cell populations in the tumor microenvironment. Collectively, these investigations highlighted the importance of optimized and thorough genomic analyses to better understand how an altered chromatin landscape contributes to liver cancer.