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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous

Tianci Wang^1,2, Xin Lai^1,2, Shenjie Wang³

¹School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.

Bioinformatics (Oxford, England)

|September 1, 2025

Summary

This summary is machine-generated.

MRDagent enhances minimal residual disease (MRD) detection using next-generation sequencing (NGS) by employing an adaptive framework and a CNN meta-model for stable and efficient variant calling from circulating tumor DNA (ctDNA). This tool offers a reliable solution for clinical applications.

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Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Published on: June 8, 2019

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Minimal residual disease (MRD) is a critical prognostic biomarker in cancer management, but its detection via next-generation sequencing (NGS)-based circulating tumor DNA (ctDNA) analysis is challenging due to low variant allele frequency (VAF) and sample heterogeneity.
Current methods struggle with stable MRD detection because of the need for individualized parameter tuning, interdependent workflow parameters, and limitations in automated optimization.

Purpose of the Study:

To develop a novel variant detection tool, MRDagent, specifically designed for robust and stable minimal residual disease (MRD) detection.
To address the challenges of parameter optimization in MRD detection workflows by creating an adaptive and efficient computational solution.

Main Methods:

MRDagent utilizes an iterative, self-adaptive optimization framework to manage complex and coupled parameters across different stages of variant detection.
Integration of a convolutional neural network (CNN)-based meta-model for rapid parameter prediction, trained on historical data to improve computational efficiency and generalizability.

Main Results:

MRDagent demonstrates superior and stable performance in MRD detection across simulated and real-world datasets.
The tool provides an efficient and reliable solution for MRD detection, enhancing the utility of ctDNA analysis in clinical and high-throughput research.

Conclusions:

MRDagent offers a significant advancement in MRD detection technology, overcoming key limitations of existing methods.
The developed tool is freely available, facilitating its adoption in clinical practice and research for improved cancer patient management.