Comprehensive analysis of structural variants in breast cancer genomes using single-molecule sequencing

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Accurate identification of structural variants (SVs) is crucial for understanding cancer progression and developing targeted treatments.
• Current sequencing methods have limitations in comprehensively detecting complex genomic alterations in cancer.

Purpose of the Study:

• To evaluate the accuracy and sensitivity of long-read sequencing technologies for structural variant (SV) and copy number variant (CNV) detection in breast cancer genomes.
• To integrate SV and CNV data for a unified representation of mutated cancer genomes.

Main Methods:

• Whole-genome sequencing of SKBR3 cell line and patient-derived organoids using Illumina/10x Genomics, PacBio, and ONT.
• Inference of SVs and CNVs using an ensemble of computational methods.
• Integration of SV and CNV data into a karyotype-graph structure.

Main Results:

• Long-read sequencing (PacBio, ONT) demonstrated substantially higher accuracy and sensitivity for SV detection compared to short reads.
• High concordance (90-95%) was observed between different long-read technologies for SV detection.
• Hundreds of cancer-relevant variants, undetectable by short-read sequencing, were identified using long reads.

Conclusions:

• Long-read sequencing is essential for precise analysis of genetic instability and comprehensive characterization of cancer genomes.
• The findings underscore the need to incorporate long-read sequencing into cancer genomics research for improved variant detection and therapeutic insights.