Evaluating Bioinformatics Processing of Somatic Variant Detection in cfDNA Using Targeted Sequencing with UMIs

• Published in: International journal of molecular sciences 2024-11, Vol.25 (21), p.11439
• Main Authors: Lin, Yixin, Rasmussen, Mads Heilskov, Christensen, Mikkel Hovden, Frydendahl, Amanda, Maretty, Lasse, Andersen, Claus Lindbjerg, Besenbacher, Søren
• Format: Article
• Language: English
• Subjects: Aged; Binomial distribution; Biomarkers, Tumor - genetics; Biopsy; Cancer; Cancer cells; Cell-Free Nucleic Acids - blood; Cell-Free Nucleic Acids - genetics; Circulating Tumor DNA - blood; Circulating Tumor DNA - genetics; Colorectal cancer; Colorectal Neoplasms - blood; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - genetics; Computational Biology - methods; Diagnosis; DNA damage; DNA sequencing; Female; Genetic aspects; Genetic variation; High-Throughput Nucleotide Sequencing - methods; Humans; Identification and classification; Male; Medical screening; Middle Aged; Mutation; Nucleotide sequencing; Patients; Plasma; ROC Curve; Software
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms252111439

Circulating tumor DNA (ctDNA) is a promising cancer biomarker, but accurately detecting tumor mutations in cell-free DNA (cfDNA) is challenging due to their low frequency and sequencing errors. Our study benchmarked Mutect2, VarScan2, shearwater, and DREAMS-vc using deep targeted sequencing of cfDNA with Unique Molecular Identifiers (UMIs) from 111 colorectal cancer patients. Performance was assessed at both the mutation level (distinguish tumor variants from errors) and the sample level (detect if an individual has cancer). Additionally, we investigated the effects of various UMI grouping and consensus strategies. The shearwater-AND variant calling method demonstrated the highest precision in detecting tumor-derived mutations from plasma, and reached the highest ROC-AUC of 0.984 for sample classification in tumor-informed cfDNA analyses. DREAMS-vc exhibited the highest ROC-AUC of 0.808 for sample classification in tumor-agnostic studies. We also found that sequencing depth differences in PBMCs could lead to false positives, particularly with VarScan2 and Mutect2, which was addressed by downsampling to equivalent mean depths. Additionally, network-based UMI grouping methods outperformed those using identical UMIs when all reads were retained. Our findings emphasize that the optimal variant caller depends on the study context-whether focused on mutation or sample classification, and whether conducted under tumor-informed or tumor-agnostic conditions.