A streamlined method for detecting structural variants in cancer genomes by short read paired-end sequencing

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Understanding cancer genome architecture, including structural variants, is crucial for oncogenesis research and personalized therapy development.
• Short read sequencing is sensitive for somatic mutations but struggles with accurate structural variant detection due to repetitive genomic regions and mapping challenges, leading to false positives.

Purpose of the Study:

• To develop and validate an efficient method for identifying large, tumor-specific structural rearrangements (deletions, inversions, duplications, translocations) from low-coverage sequencing data.
• To improve the accuracy of structural variant detection by implementing novel filtering procedures to minimize false positive calls.

Main Methods:

• Utilized SVDetect and BreakDancer software for initial identification of structural variants.
• Developed and applied a set of novel filtering procedures to reduce false positive structural variant calls.
• Applied the method to analyze a spontaneous T cell lymphoma in a RAG2/p53-deficient mouse model.

Main Results:

• Successfully identified 40 validated tumor-specific structural rearrangements.
• The method demonstrated effectiveness even with as few as 2 independent read pairs supporting a rearrangement.
• The novel filtering procedures significantly reduced false positive calls in low-coverage sequencing data.

Conclusions:

• The developed method provides an efficient and accurate approach for detecting large structural rearrangements in cancer genomes using low-coverage sequencing.
• This technique enhances the reliability of structural variant detection, aiding in the study of tumor biology and the advancement of personalized cancer treatments.