Computational approaches for cancer genomics
Identifying somatic intergenic mutations of relevance in cancer
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Cancer is a complex genomic disease characterized by accumulation of somatic mutations over the lifetime of a patient. Identification of somatic driver mutations that contribute to tumorigenesis is a major goal of cancer genomics. With the recent advances in the sequencing technologies it became possible to study somatic mutations on the whole-genome scale in multiple cancers. While most of the cancer genomics studies were previously focused on identification of driver mutations affecting exons, several examples of driver events within the non-protein-coding regions of the genome were identifi...
Cancer is a complex genomic disease characterized by accumulation of somatic mutations over the lifetime of a patient. Identification of somatic driver mutations that contribute to tumorigenesis is a major goal of cancer genomics. With the recent advances in the sequencing technologies it became possible to study somatic mutations on the whole-genome scale in multiple cancers. While most of the cancer genomics studies were previously focused on identification of driver mutations affecting exons, several examples of driver events within the non-protein-coding regions of the genome were identified, including the recurrent TERT promoter mutations. Such findings have spurred searches for similar examples of recurrent non-coding mutations using computational cancer genomics. In this book I present several computational approaches aimed to identify somatic driver mutations of relevance in cancer, with a specific focus on intergenic regions of the genome.
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