Cancer whole-genome sequencing: present and future.

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Next-generation sequencing and computational tools enable whole-genome sequencing (WGS) for cancer genome analysis.
• Major projects like ICGC and TCGA primarily use exome sequencing, leaving non-coding regions and rearrangements underexplored.
• Limited understanding exists regarding somatic mutations in non-coding DNA, fusion genes, and pathogen detection within cancer genomes.

Purpose of the Study:

• To highlight the potential of WGS in comprehensively analyzing cancer genomes.
• To emphasize the need for exploring non-coding regions, rearrangements, and pathogen detection.
• To underscore the necessity for advancements in WGS technologies and data analysis.

Main Methods:

• Utilizing whole-genome sequencing (WGS) to detect coding and non-coding mutations, somatic copy number alterations, and rearrangements.
• Analyzing large-scale cancer genome datasets from global and local projects.
• Integrating multi-omics, functional, and clinical data with WGS findings.

Main Results:

• WGS can identify a broader spectrum of genomic alterations, including those in non-coding regions and fusion genes.
• Current WGS analysis is primitive, requiring significant improvements in sequencing, informatics, and computational resources.
• Understanding the full landscape of cancer genomes necessitates analyzing more WGS data and integrating diverse datasets.

Conclusions:

• WGS provides a more complete picture of cancer genomic alterations than exome sequencing.
• Advancements in WGS technology and computational analysis are essential for deeper insights into cancer.
• Integrating WGS data with multi-omics and clinical information is key to interpreting cancer genome diversity.