Accuracy of ctDNA-based minimal residual disease detection in predicting postoperative recurrence of breast cancer: a meta-analysis

TL;DR

This study evaluates how well ctDNA can predict breast cancer recurrence after surgery, finding that a surveillance approach improves accuracy but still needs refinement.

Contribution

The study introduces a meta-analysis comparing landmark and surveillance strategies for ctDNA-based recurrence prediction in breast cancer.

Findings

The surveillance strategy improved ctDNA detection sensitivity without significant loss of specificity.

Triple-negative breast cancer showed the best performance under the surveillance strategy.

WGS, ddPCR, and WES all demonstrated high sensitivity within the surveillance framework.

Abstract

Detection of circulating tumor DNA (ctDNA) has attracted growing attention for predicting postoperative breast cancer recurrence; however, the differences between the landmark and surveillance strategies remain unclear.

We systematically searched the PubMed, Cochrane Library, Embase, and Ovid MEDLINE databases for studies published up to April 17, 2025. Effect models were selected based on heterogeneity tests to pool diagnostic indicators, including sensitivity and specificity. Subgroup analyses were conducted according to molecular subtype, detection method, analytical strategy, and disease stage.

A total of 17 studies were included in the analysis. The sensitivity and specificity of the landmark strategy were 0.40 (95% CI: 0.22–0.62) and 0.95 (95% CI: 0.81–0.99), respectively. For the surveillance strategy, sensitivity was 0.79 (95% CI: 0.71–0.85) and specificity was 0.98 (95% CI: 0.92–0.99). The surveillance strategy significantly improved sensitivity without a substantial loss of specificity. Among molecular subtypes, triple-negative breast cancer(TNBC) exhibited the best performance under the surveillance strategy. Whole-genome sequencing (WGS), droplet digital PCR (ddPCR), and whole-exome sequencing (WES) all demonstrated high sensitivity within the surveillance framework.

ctDNA serves as a highly specific biomarker for predicting postoperative breast cancer recurrence. The surveillance strategy substantially improves its sensitivity; however, the current performance remains below the ideal threshold for clinical implementation. Future research should focus on refining detection strategies and technologies to achieve personalized recurrence risk stratification and guide therapeutic decision-making.

https://www.crd.york.ac.uk/prospero/, identifier CRD420251056270.

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