Detection Algorithms For Genomic Structural Variation And Viral Integration In Circulating Tumor DN

Circulating tumor DNA(ctDNA)is fragmented tumor-derived DNA found in the bloodstream with small fragment sizes,between 50 and 166 bp.Mutations in ctDNA have been identified as excellent biomarkers for early detection and treatment monitoring of cancer.Although ctDNA was first described 50 years ago,base-resolution analysis of ctDNA was recently performed with next-generation sequencing(NGS)technology.Targeted deep sequencing has allowed the detection of multiple types of cancer-specific mutations in ctDNAs,such as single-nucleotide variants(SNVs),small insertions and deletions(InDels),and structural variations(SVs).However,due to the short fragment size and extremely low fraction of ctDNA,accurate detection of SVs in ctDNAs,especially for translocations and insertions/deletions larger than 50 bp,remains challenging.Here,we describe a new SV detection tool,called Aperture.It was developed to achieve sensitive detection of breakpoints introduced by SVs in ctDNA datasets.It is based on(i)a unique strategy of k-mer-based searching,which uses two different k lengths and spaced seeds to optimize the coverage of repetitive sequences at breakpoints,(ii)rapid approximation of the intersection approach to identify breakpoint junctions containing either novo-k-mers or repetitive sequences and(iii)a barcode-based filtering strategy designed for ctDNA datasets with molecular barcoding.Starting from raw sequencing data in FASTQ format,Aperture performs a k-merbased database search involving three different libraries and implements SV breakpoint detection by rapid approximation of set intersection using binary labels.Aperture then gathers candidate reads with identical junctions or similar genomic positions to achieve fault-tolerant evidence clustering for SV detection.The final output from Aperture includes the predicted SVs,number of supporting molecules,mapping quality of both breakends and sequences of identical microhomology at breakpoints.After a performance test using simulated ctDNA data,we found that Aperture achieved much higher sensitivity and specificity than existing SV callers at dilutions ranging from 0.1%to 10%.We also applied Aperture to three real patient ctDNA datasets from different clinical settings.Aperture successfully detected druggable translocations in lung cancer patients and HBV integration in the TERT promoter in liver cancer patients,including a complex rearrangement involving repetitive sequences that was missed by most SV callers.Since some fusions and viral integrations are closely related to certain tumor types,our work may enhance the diagnostic potential of ctDNA in early cancer detection and help in treatment monitoring.In addition,Aperture runs fast and is efficient in consumptions of computational resources.For these reasons,we believe that the method proposed in this study will not only be helpful in bio informatics community,but also offer a reliable tool in research and clinical practice and help progress precision medicine.Implemented in Java,Aperture is available as an open source tool at GitHub.