Abstract 2493: SCANs: SNP-based contamination assessment with nuanced signal in targeted methylation sequencing data

Abstract Background: Multi-Cancer Early Detection (MCED) represents a promising approach with the potential for high sensitivity in detecting rare tumor-derived signals in the blood. However, contamination from adjacent samples or from environmental sources poses significant challenges that can compromise the accuracy of these results. Current contamination detection methods primarily designed for tissue samples, leaving a notable gap in methodologies for assessing low-level contamination in cfDNA targeted methylation sequencing data in blood. The challenges include the absent of matched control samples, low sequencing depth, and probe capture bias. To address these limitations, we have developed a machine learning-based method aimed at improving contamination detection accuracy in MCED datasets. Methods: We first identified single nucleotide polymorphisms (SNPs) with high prevalence in the East-Asian population. We calculated the allele frequencies of SNPs using in-silico synthetic contaminated samples and fitted the distributions of allele frequencies at varying contamination levels to identify informative SNPs. To enhance signal-to-noise value, we constructed features based on reads containing alternative bases of these informative SNPs at each tier, which was categorized according to its coverage and assumed contamination levels. This comprehensive feature matrix was used to train an XGBoost algorithm on 185 normal samples and their corresponding in-silico synthetic contaminated samples. The performance of our approach was validated using two distinct datasets: the first set comprised in-silico simulated contamination samples with contamination levels ranging from 0.05% to 10%, while the second was built by titrating three healthy cfDNA samples into three other healthy cfDNA backgrounds, with titration levels ranging from 0.05% to 2%. Results: This method effectively identifies contamination, particularly in scenarios where coverage at SNPs is relatively low, ranging from 50X to 300X. It demonstrated the ability to detect contamination levels as low as 0.2% in the titrated samples, with an in-silico limit of detection (LoD) established at 0.1%. Conclusions: We have developed SCANs, a tool designed to address the challenges posed by low sequencing depth and capture bias-induced abnormal allele signals in methylation data. SCANs enables the detection of low-level contamination, thereby facilitating the precise analysis of cfDNA methylation data in MCED. Citation Format: Shaohua Ma, Chaoyang Liang, Shuliang Guo, Luyan Shen, XiaoSheng He, Zhihui Xu, Jing Zhao, Yonghui Li, Shiqing Chen, Baoliang Zhu, Xiaohui Wu. SCANs: SNP-based contamination assessment with nuanced signal in targeted methylation sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2493.