MRDtarget: A heuristic Gaussian approach for optimizing targeted capture regions to enhance Minimal Residual Disease detection

Molecular residual disease (MRD) detection, initially developed for hematologic malignancies, has become a critical biomarker for monitoring solid tumors. MRD detection primarily relies on circulating tumor DNA (ctDNA) analysis using next-generation sequencing, offering high sensitivity and broad genomic coverage. However, challenges remain in designing cost-effective panels that maximize mutation detection while maintaining biological relevance. Fixed panels often lack sufficient patient-specific mutation coverage, while WES-based personalized MRD assays, despite their high sensitivity, are costly and less accessible. We developed a tumor comprehensive genomic profiling (CGP)-informed personalized MRD assay to detect tumor-derived mutations, which allowed us to design patient-specific personalized panels and meanwhile, provide a cost-effective alternative to whole exome sequencing (WES). To address these limitations, we developed MRDtarget, a heuristic multivariate Gaussian model-based targeted capture region selection method. By expanding beyond traditional hotspot regions, MRDtarget optimizes variant tracking for MRD detection, significantly improving sensitivity. Using a Bayesian inference-based heuristic approach, MRDtarget integrates multi-feature informativeness rates to identify optimal genomic regions for capture. Experimental results demonstrate that MRDtarget enables the detection of more variants per patient. This study underscores the importance of rational panel design to improve MRD sensitivity and provides a novel approach to enhance precision diagnostics and treatment for solid tumor patients.Author summary: Minimal residual disease (MRD) detection plays a critical role in cancer prognosis and treatment monitoring, especially for solid tumors. However, existing sequencing panels often fail to provide sufficient coverage of tumor-specific mutations in every patient, limiting the clinical sensitivity of MRD detection. To address this gap, we developed MRDtarget, a computational tool that designs personalized targeted sequencing panels by optimizing the selection of genomic regions likely to contain informative mutations. Our method goes beyond conventional hotspot-based panels by incorporating multi-dimensional mutation features, including recurrence, clonality, and functional relevance, into a probabilistic model that prioritizes regions most informative for MRD detection. We evaluated MRDtarget using both clinical and public datasets and found that it consistently outperforms traditional approaches in mutation capture efficiency and patient coverage. Notably, it raises the proportion of patients with four or more trackable mutations—considered the minimum threshold for reliable MRD monitoring. MRDtarget also demonstrates robust performance across different cancer types and sequencing conditions. This approach enables a cost-effective and personalized solution for improving MRD detection, with broad implications for early relapse prediction and treatment guidance in precision oncology.