Cyst fluid ctDNA as a biomarker for genetic profiling and treatment monitoring in cystic brain metastases.

BACKGROUND

Cystic brain metastases (CBM) present significant clinical challenges due to their heterogeneity and the limitations of current diagnostic methods in guiding treatment. Traditional tissue biopsies are invasive and may not capture tumour heterogeneity, while plasma circulating tumour DNA (ctDNA) analysis is impeded by the blood-brain barrier, leading to low sensitivity for detecting intracranial lesions. These limitations create a critical gap in the personalised management of patients with CBM.

METHODS

We evaluated the utility of cyst fluid ctDNA as a minimally invasive biomarker for genetic profiling and treatment monitoring in CBM patients. ctDNA was extracted from cyst fluid, tumour tissue, plasma, and cerebrospinal fluid (CSF) samples collected from 18 patients. NGS was performed to analyse genetic mutations. Mutation detection rates and genetic heterogeneity were compared across different sample types. Dynamic changes in ctDNA mutation abundance in cyst fluid were assessed in relation to treatment responses.

RESULTS

Cyst fluid ctDNA demonstrated a higher mutation detection rate and captured more significant genetic heterogeneity than plasma ctDNA and, in some cases, even matched tissue samples. Clinically significant mutations, including actionable driver genes such as EGFR and TP53, were identified in cyst fluid ctDNA but were undetectable in plasma. Moreover, dynamic changes in the abundance of ctDNA mutations in cyst fluid correlated with treatment responses, indicating its potential for real-time therapeutic efficacy monitoring.

CONCLUSIONS

Cyst fluid ctDNA provides a sensitive and comprehensive method for capturing the genetic landscape of CBM, effectively overcoming the limitations of tissue biopsies and plasma ctDNA analysis. By establishing a real-time molecular surveillance network, cyst fluid ctDNA analysis redefines precision neuro-oncology paradigms, transitioning CBM management from static histomolecular snapshots to adaptive therapeutic ecosystems.