1, 2Įxplosive advances in next-generation sequencer (NGS) and computational analyses handling massive data have enabled us to comprehensively analyze cancer genome profiles at research and clinical levels, such as targeted sequencing for hundreds of genes, whole exome sequencing (WES), RNA sequencing (RNA-Seq) and whole genome sequencing (WGS). 1, 4 Some of these oncogenic mutations have been successfully targeted for molecular therapy, and specific and recurrent mutations of these oncogenes are now used to predict sensitivity to therapy, prognosis and residual disease. 1, 2 A number of familial cancer segregation studies and loss-of-heterozygosity (LOH) analyses on cancer tissues have identified germline and somatic mutations of several classical tumor suppressor genes, such as RB1, TP53 and APC, 2, 3 and copy-number analysis has found some oncogenes and underlying oncogenic activators, such as HER2/ERBB2 and MYC. Taking into account the diversity of cancer genomes and phenotypes, interpretation of abundant mutation information from WGS, especially non-coding and structure variants, requires the analysis of large-scale WGS data integrated with RNA-Seq, epigenomics, immuno-genomic and clinic-pathological information.Ĭancer is essentially a disease of the genome which evolves and progresses with accumulations of somatic mutations, including copy-number alterations (CNA) and structural variants (SV), and epigenomic alterations with and without some hereditability (germline variants). This review describes recently developed technical approaches for cancer WGS and the future direction of cancer WGS, and discusses its utility and limitations as an analysis platform and for mutation interpretation for cancer genomics and cancer precision medicine.
#Genomic sequence analysis driver
Whole genome sequencing (WGS) approaches can be used to comprehensively explore all types of genomic alterations in cancer and help us to better understand the whole landscape of driver mutations and mutational signatures in cancer genomes and elucidate the functional or clinical implications of these unexplored genomic regions and mutational signatures. Structural variants and pathogen in cancer genomes remain widely unexplored. However, there is limited information on somatic mutations in non-coding regions, including introns, regulatory elements and non-coding RNA. Explosive advances in next-generation sequencer (NGS) and computational analyses have enabled exploration of somatic protein-altered mutations in most cancer types, with coding mutation data intensively accumulated.
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