Liquid biopsy in glioblastoma: emerging technologies and translational opportunities
Synopsis
This review focuses on liquid biopsy for glioblastoma, noting that tissue biopsy is invasive and fails to capture tumor heterogeneity or temporal dynamics, while liquid biopsy can provide noninvasive real-time monitoring via circulating biomarkers such as cell-free DNA, circulating tumor DNA, circulating tumor cells, and extracellular vesicles in plasma, cerebrospinal fluid, urine, and saliva; it advances beyond prior biomarker catalogs by delivering a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms in terms of sensitivity, clinical actionability, cost, and scalability, and introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection a
Interpretation
The review proposes liquid biopsy as a noninvasive real-time monitoring route for glioblastoma, covering circulating biomarkers including cfDNA, ctDNA, CTCs, and EVs in plasma, cerebrospinal fluid, urine, and saliva. Relative to a tissue-biopsy-centered approach, the text states that tissue biopsy is "invasive" and "fail to capture tumor heterogeneity or temporal dynamics," positioning liquid biopsy as an alternative that can be sampled repeatedly and track temporal change. This is a review-level argument based on synthesizing existing categories of circulating biomarkers, not on a single prospective cohort or trial dataset.
The review provides a quantitative technology scorecard comparing cfDNA-, CTC-, and EV-based platforms across sensitivity, clinical actionability, cost, and scalability. The text explicitly says it "advances beyond prior biomarker catalogs," meaning it moves from listing markers to placing different platforms within a shared comparative framework. The scorecard is a comparative tool proposed by the review authors; its evidential strength depends on the quality of the platform studies included, and the loaded text does not give a specific number of platforms or statistical pooling.
The review introduces a multimodal decision matrix and an AI-driven fusion pipeline integrating fragmentomics, EV proteomics, and CTC transcriptomics to enhance minimal residual disease detection and guide precision neuro-oncology. Relative to single-marker or single-omics analysis, this framework combines multiple liquid-biopsy signals with AI toward more integrated clinical decision support. This is a conceptual framework and pipeline design; the loaded text reports no validation results, sample sizes, or performance metrics for the fusion pipeline.
Perspective
The scope of this review is the assessment of liquid biopsy technologies and translational directions for glioblastoma, aimed at researchers and clinical translation specialists interested in neuro-oncology, liquid biopsy, and AI-assisted diagnostics. Its scorecard and fusion pipeline are intended for comparing cfDNA, CTC, and EV platforms and for discussing noninvasive monitoring and minimal residual disease detection in sample settings including plasma, cerebrospinal fluid, urine, and saliva.
The loaded text is a review-level summary and does not include specific platform counts, sensitivity values for each platform, cost data, sample sizes, or validation results for the AI fusion pipeline. Readers should still watch how strong the original studies underlying the scorecard are, whether the multimodal decision matrix has been tested on independent data, and whether the AI fusion pipeline can be reproduced in real glioblastoma cohorts and influence clinical decisions.