增强型变异（EV）分析流程用于改善低肿瘤负荷样本中循环肿瘤DNA（ctDNA）检测的分析性能

EVs represent a distinct class of genomic events, offering opportunities in cancer diagnostics. Tumor-informed ctDNA detection, a sensitive and specific approach for monitoring molecular residual disease (MRD) in plasma, continues to evolve with the discovery of novel biomarkers. Developing methodologies for unbiased EV detection could enhance assay performance. Here, we report on the performance of a novel EV caller. EV caller performance was evaluated using 2 cancer cell lines and their matched normal cell lines. EV truth sets were established based on the pure cell lines (100%; no dilution). Contrived genomic DNA samples were prepared to emulate 3 different tumor purity levels relative to normal (50%, 25%, 10%) and analyzed using whole genome sequencing. The results from the titrations were compared to the 100% tumor cell line (“truth set”) to estimate the EV caller's performance. Finally, to assess the performance of the EV caller in detecting ctDNA, a custom cfDNA-based mPCR primer design strategy was developed and applied to EVs in a tumor:normal cell line dilution series (range, 1:500 - 1:200,000), with 2-5 replicates for each condition. Each cancer cell line had approximately 700 EVs detected. Across all emulated tumor purity levels, the EV caller demonstrated a positive predictive value (PPV) of 90-95% at 5-10% variant allele frequency (VAF) and >99% PPV at >10% VAF. A positive percent agreement of >95% at >10% VAF was observed. For cell line ctDNA titrations, the EV caller showed target-level detection down to 0.0006% VAF. The target-level specificity was 99.99%. At the sample level, EVs were detected at the lowest tested dilution level (1:200,000). These analytical results demonstrate the exceptional performance of the EV caller algorithm to detect targets with high precision and specificity. EVs represent promising biomarkers that could enhance ctDNA assay sensitivity and specificity, particularly in samples with low tumor burden.