Systematic benchmarking of multi-modal approaches for tumor-naive ctDNA detection and quantification
Ting Qi, Denis OdinokoviD, Lakshmi Narayanan LakshmananiD, Nathalia Graf GrachetiD, Melanie Lou, Seng Lor SaeleeiD, Gladys Garcia-MontoyaiD, Pui Mun WongiD, Rafeed Chowdhury Rahman, Hosseinali AsgharianiD, Andrea Teh Xin Yi, Nicole Hnin Ye Pyone, Lan Ying Wang, Guat Tin Tan, Hanaé CarriéiD, Avril Lim, Lau Yi Ting, Anna GaniD, Polly S. Y. PooniD, Steven Ngo, Jordan Snyder, Harpreet Kaur, Aaron TaniD, Yoon Sim YapiD, D SW Shao-Weng TaniD, , A. SkanderupiD
Abstract
Longitudinal monitoring of circulating tumor DNA (ctDNA) has emerged as a promising framework for characterizing treatment response dynamics in cancer. Scalable tumor-naive approaches for quantifying ctDNA often involve whole-genome sequencing (WGS) or DNA methylation profiling, but their comparative performance and capacity for complementary integration remain poorly understood. Here we systematically benchmarked tumor-naive WGS- and methylation-based ctDNA quantification methods using plasma from 150 patients with colorectal, lung and breast cancer. Using paired high-depth WGS and EM-seq data, we generated 40,000 in silico samples and evaluated detection accuracy, limits of detection (LoD) and quantification (LoQ) across cancer types and sequencing depths (0.1x-30x). We further assessed single- and multimodal method combinations, identifying conditions under which integrated approaches enhance analytical performance for detection and quantification relative to single modalities. This benchmark delineates key performance trade-offs and provides a practical framework to support method development and guide future research applications in ctDNA-based biomarker studies.
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