CESAR: High-Sensitivity Detection of Copy Number Variations in ctDNA Using Segmentation and Anchor Recalibration

BackgroundDetecting copy number variations (CNVs) in circulating tumor DNA (ctDNA) is crucial for the companion diagnosis and resistance monitoring of various solid tumors (e.g., NSCLC, Glioblastoma). However, when tumor-derived DNA fractions are extremely low (often <1%), traditional depth-based methods frequently fail due to non-linear sequencing depth fluctuations and probe-specific capture biases inherent to targeted Next-Generation Sequencing (NGS). MethodsWe developed CESAR (CNV Estimation with Segmentation and Anchor Recalibration), a novel computational tool optimized for ultra-sensitive, tumor-only CNV detection in targeted NGS panels. CESAR utilizes Circular Binary Segmentation (CBS) to re-partition target regions based on relative capture efficiency. It then introduces a dynamic "anchor" selection algorithm that identifies a personalized set of genomic segments mirroring the non-linear coverage behavior of each target gene. By minimizing the Coefficient of Variation (CV) through iterative anchor selection, CESAR effectively recalibrates the baseline to suppress technical noise. ResultsValidation using standard DNA reference materials demonstrated that CESAR successfully identified both amplifications (e.g., MET, ERBB2, EGFR) and relative copy number deletions at ultra-low tumor fractions. Notably, CESAR achieved stable detection of focal alterations as subtle as 2.18 copies (a mere 1.09x fold change relative to the diploid baseline), while maintaining zero false positives in control regions. Evaluation across distinct clinical biofluids--36 clinical plasma samples and 41 glioma cerebrospinal fluid (CSF) samples--identified critical, previously undetected CNV events, including subtle ERBB2 gains and distinct MET deletions. Furthermore, comprehensive benchmarking revealed that CESAR consistently outperformed the widely used CNVkit, particularly in suppressing technical variance and resolving ultra-low-level copy number gains that CNVkit failed to distinguish from background noise. ConclusionsCESAR provides a highly stable and sensitive algorithmic framework for tumor-only CNV calling in liquid biopsies, facilitating precise therapeutic decision-making in precision oncology.