Presentation method to inference and interpretation of copy number changes and genomic rearrangements breakpoints in single-cell sequencing of ovarian cancer

Presentation method to inference and interpretation of copy number changes and genomic rearrangements breakpoints in single-cell sequencing of ovarian cancer
Ali Abedini ,^1,* Mahdi Nasiri,²
1. Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
2. Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Introduction: Novel cancer genomics has emerged from the combination of the Human Genome Reference, massively parallel sequencing, and the measurement of tumor to normal DNA sequences, revealing novel insights into the cancer genome and its amazing diversity. Whole genome sequencing mixes the signals of sampled populations, diluting the signals of clone-specific aberrations, and complicating estimation of clone-specific genotypes.
Methods: We introduce ReMixT, a method to unmix tumor and contaminating normal signals and jointly predict mixture proportions, clone-specific segment copy number, and clone specificity of breakpoints.
Results: We have demonstrated that ReMixT improves both inference and interpretation of copy number changes and genomic rearrangements. Improved accuracy was observed for prediction of clone fraction, clone specific copy number, and clone specificity of breakpoints. We show how breakpoint copy number changes can be used a markers of clonal populations, and used to track clonal population dynamics in the same way as SNVs.
Conclusion: By linking clone specific copy number changes to breakpoints we show how targeted single cell sequencing can be used to jointly profile clonal genotypes in SNV and copy number space. We anticipate further benefit may be gained from jointly modelling copy number changes, rearrangements, SNPs and SNVs, all within the context of an appropriate phylogenetic model. Future research leveraging the patterns of genome damage and the totality of somatic alterations in a cancer’s evolutionary history to elucidate its biologic and mutagenic properties will derive benefit from ReMiXT’s improved accuracy in structural alteration detection and interpretation.
Keywords: Whole genome, Cancer genomics, SNV, SNP, ReMixT