Computational approaches for cancer genomics

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.

Autorentext

Dr. Rudneva was born in Moscow, Russia. She studied bioengineering and bioinformatics at Lomonosov Moscow State University and received her PhD from Ruprecht-Karls-Universität Heidelberg in 2016. Dr. Rudneva works in the field of pediatric neuro-oncology, her research was published in such prestigeous journals as Nature and Lancet Oncology.