Computational cancer genomics

Cancer is a complex genetic disease primarily caused by somatic mutations in the genome. Somatic mutations have a pivotal role in the initiation and development of tumor growth. The advent of newer and advanced next-generation sequencing technologies has revolutionized cancer genomics research. It has facilitated genomic analysis of multiple samples in a short time span, thus enabling analysis of large sequencing omics data. However, the sequencing and assembly procedures introduce several errors and artifacts. The computational approaches are becoming increasingly significant for the systematic detection of somatic mutations and in reducing the false positive and false negative mutations. This review focuses on the computational techniques in cancer genomics, specifically databases, methods, and tools for detecting cancer driver genes. The methods for the detection of germline and somatic mutations, noncoding mutations, structural variants, and variant annotations are discussed. As specific biological pathways are capable of complicated rewiring between conditions, methods involving pathway analysis and network-based analyses prove to be useful in cancer prognosis. Methods are developed to predict drug combinations for targeted therapy. The emergence of mutation-specific drugs will lead to precision medicine according to the mutational profile of an individual. This chapter highlights the recent advancements in computational cancer genomics along with challenges and strategies used to gain an in-depth understanding of cancer biology of different types of tumors.