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Related Experiment Video

Updated: Jul 14, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

MRDsteer: quality-aware AI-driven closed-loop optimization enhances ctDNA-based minimal residual disease detection.

Tianci Wang^1,2,3, Xin Lai^2,3, Shenjie Wang^1,2,3

¹The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an 710004, China.

Briefings in Bioinformatics

|July 12, 2026

Summary

MRDsteer, an AI-driven agent, enhances circulating tumor DNA (ctDNA) analysis for minimal residual disease (MRD) detection. It improves variant calling stability and sensitivity, especially at ultra-low frequencies, aiding cancer monitoring.

Keywords:

ctDNA deep reinforcement learning minimal residual disease probabilistic modeling spatial heterogeneity variant detection

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Last Updated: Jul 14, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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Published on: June 3, 2022

Area of Science:

Computational Biology
Genomics
Artificial Intelligence

Background:

Accurate circulating tumor DNA (ctDNA) analysis is crucial for minimal residual disease (MRD) profiling.
Current ctDNA workflows lack continuous performance monitoring, limiting detection stability in heterogeneous genomic regions.

Purpose of the Study:

To develop an AI-driven agent, MRDsteer, for autonomous closed-loop control of ctDNA variant calling.
To enhance the stability and sensitivity of ctDNA variant detection, particularly at ultra-low frequencies.

Main Methods:

MRDsteer utilizes multidimensional quality metrics to monitor variant-calling reliability in real-time.
It triggers localized re-calling in high-risk regions when reliability drops below a threshold.
The agent operates as a closed-loop system, assessing reliability and applying targeted interventions.

Main Results:

MRDsteer improved the stability and sensitivity of ctDNA variant detection in simulated and real-world datasets.
It demonstrated superior performance compared to baseline methods under challenging conditions, including ultra-low variant allele frequencies.
Clinical cohort analyses showed improved MRD stratification and progression-free survival separation in non-small cell lung cancer (NSCLC) and nasopharyngeal carcinoma (NPC) subgroups.

Conclusions:

MRDsteer offers a robust computational strategy for sensitive MRD detection and longitudinal ctDNA monitoring.
The AI-driven approach enhances the reliability of variant calling in ctDNA analysis.
This technology has the potential to improve clinical decision-making for cancer patients.