# Analytical validation of a hybrid-approach combining tumor-informed and tumor-agnostic bespoke ctDNA panel assay for the sensitive detection of minimal residual disease

**Authors:** Sunghoon Heo, Seon-Kyu Ham, Hayoon Lee, Bom Han, Hanseong Roh, Seongmun Jeong, Hwang-Phill Kim, Duhee Bang, Sang-Hyun Song, Tae-You Kim

PMC · DOI: 10.1371/journal.pone.0334282 · PLOS One · 2025-11-10

## TL;DR

This paper validates a new hybrid ctDNA test that can detect very low levels of cancer DNA in blood, improving the ability to track minimal residual disease after treatment.

## Contribution

The novel hybrid approach combines tumor-specific and tumor-agnostic targets to achieve ultra-sensitive ctDNA detection.

## Key findings

- The CancerDetectTM test achieved a detection limit of 0.001% with 99.9% specificity.
- The hybrid approach enhances sensitivity beyond current liquid biopsy assays.
- Large-scale mutation profiling was leveraged to improve detection capabilities.

## Abstract

Minimal residual disease (MRD) is a small group of cancer cells not eliminated by anti-cancer treatment. Because of its small size, conventional imaging system may not be able to detect the MRD in routine clinical practice. Although the liquid biopsy tests can detect the circulating tumor DNA (ctDNA) when the tumor is present in the body, the fraction of ctDNA is considered lower than the 0.01% which is unreachable by current state-of-the-art liquid biopsy assay relying on fixed-gene panel approach. Here, we describe the analytical validation result of our previously developed a tumor-informed MRD test, CancerDetectTM (formerly reported as AlphaLiquid®Detect), leveraging large-scale mutation spectrum profiling strategy to enhance detection sensitivity. The CancerDetectTM is a hybrid-approach MRD test combining both personalized (bespoke) mutations and tumor-agnostic clinically actionable targets (hotspot mutations) with hybridization capture technology. The analytical validation result of CancerDetectTM showed limit of detection successfully reached down to 0.001% (10−5) with 99.9% specificity.

## Full-text entities

- **Diseases:** cancer (MESH:D009369)

## Full text

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## Figures

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## References

29 references — full list in the complete paper: https://tomesphere.com/paper/PMC12599964/full.md

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Source: https://tomesphere.com/paper/PMC12599964