Beyond the Variants: Mutational Patterns in Next-Generation Sequencing Data for Cancer Precision Medicine

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Massively parallel sequencing, or next-generation sequencing (NGS), offers comprehensive genetic analysis beyond single nucleotide alterations.
• NGS data reveals complex variations including insertions, deletions, copy number alterations, and structural variants.
• Somatic cancer NGS data yields genome-wide biomarkers, categorized as mutational patterns and signatures.

Purpose of the Study:

• To review common genome-wide biomarkers derived from next-generation sequencing data in cancer.
• To highlight the significance of tumor mutational burden, microsatellite instability, homologous recombination deficiency, and mutational signatures.

Main Methods:

• Review of current literature on next-generation sequencing applications in cancer genomics.
• Analysis of methodologies for identifying and interpreting genome-wide biomarkers from NGS data.

Main Results:

• Next-generation sequencing enables the detection of diverse genetic alterations, from single nucleotides to complex structural variants.
• Genome-wide biomarkers, including tumor mutational burden, microsatellite instability, homologous recombination deficiency, and mutational signatures, can be identified from NGS data.
• These biomarkers provide insights into cancer biology and potential therapeutic targets.

Conclusions:

• Next-generation sequencing is a powerful tool for comprehensive cancer genome analysis.
• Genome-wide biomarkers identified through NGS are crucial for understanding cancer and guiding treatment decisions.
• Further research into mutational patterns and signatures will enhance precision oncology.