부산대학교 도서관

Somatic mutations in cancer genomes are associated with DNA replication timing (RT) and chromatin accessibility (CA), however these observations are based on normal tissues and cell lines while primary cancer epigenomes remain uncharacterised. Here we use machine learning to model megabase-scale mutation burden in 2,500 whole cancer genomes and 17 cancer types via a compendium of 900 CA and RT profiles covering primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than those of normal tissues, are most predictive of regional mutagenesis in most cancer types. Feature prioritisation shows that the epigenomes of matching cancer types and organ systems are often the strongest predictors of regional mutation burden, highlighting disease-specific associations of mutational processes. The genomic distributions of mutational signatures are also shaped by the epigenomes of matched cancer and tissue types, with SBS5/40, carcinogenic and unknown signatures most accurately predicted by our models. In contrast, fewer associations of RT and regional mutagenesis are found. Lastly, the models highlight genomic regions with overrepresented mutations that dramatically exceed epigenome-derived expectations and show a pan-cancer convergence to genes and pathways involved in development and oncogenesis, indicating the potential of this approach for coding and non-coding driver discovery. The association of regional mutational processes with the epigenomes of primary cancers suggests that the landscape of passenger mutations is predominantly shaped by the epigenomes of cancer cells after oncogenic transformation. Author summary: Cancer cells harbour thousands of somatic mutations that have complex genomic distributions. A few so-called driver mutations cause cancer by unlocking oncogenic pathways, while most mutations are functionally neutral passengers. Passenger mutations are caused by various mutational processes of aging, carcinogens, and errors in DNA repair. Therefore, passengers represent a footprint of cancer evolution that is informative of tumor development and exposures. The distribution of passenger mutations also associates with epigenetics, including chromatin accessibility and DNA replication timing. Here we use machine learning to show that mutational processes and signatures in cancer genomes associate with the epigenetic profiles of cancer cells, rather than normal tissues and common cell lines that have been indicated previously. This suggests that many passenger mutations occur after normal cells have transformed to cancer cells and acquired their epigenetic properties. These interactions are highly tissue-specific, as the epigenomes of specific cancer types are the strongest predictors of mutational processes in these cancer types. Our integrative models also indicate known and potential cancer driver mutations in the genomic regions where epigenetics does not fully explain the overrepresented mutations. Thus, our study characterises the complex interplay of mutational processes and genome function in cancer. [ABSTRACT FROM AUTHOR]