MR-guided focused ultrasound liquid biopsy enriches circulating biomarkers in patients with brain tumors
Bibliographic record
Abstract
BACKGROUND: Liquid biopsy is promising for early detection, monitoring of response, and recurrence of cancer. The blood-brain barrier (BBB) limits the shedding of biomarker, such as cell-free DNA (cfDNA), into the blood from brain tumors, and their detection by conventional assays. Transcranial MR-guided focused ultrasound (MRgFUS) can safely and transiently open the BBB, providing an opportunity for less-invasive access to brain pathology. We hypothesized that MRgFUS can enrich the signal of circulating brain-derived biomarkers to aid in liquid biopsy. METHODS: Nine patients were treated in a prospective single-arm, open-label trial to investigate serial MRgFUS and adjuvant temozolomide combination in patients with glioblastoma (NCT03616860). Blood samples were collected as an exploratory measure within the hours before and after sonication, with control samples from non-brain tumor patients undergoing BBB opening (BBBO) alone (NCT03739905). RESULTS: Brain regions averaging 7.8 ± 6.0 cm3 (range 0.8-23.1 cm3) were successfully treated within 111 ± 39 minutes without any serious adverse events. We found MRgFUS acutely enhanced plasma cfDNA (2.6 ± 1.2-fold, P < .01, Wilcoxon signed-rank test), neuron-derived extracellular vesicles (3.2 ± 1.9-fold, P < .01), and brain-specific protein S100b (1.4 ± 0.2-fold, P < .01). Further comparison of the cfDNA methylation profiles suggests a signature that is disease- and post-BBBO-specific, in keeping with our hypothesis. We also found cfDNA-mutant copies of isocitrate dehydrogenase 1 (IDH1) increased, although this was in only one patient known to harbor the tumor mutation. CONCLUSIONS: This first-in-human proof-of-concept study shows MRgFUS enriches the signal of circulating brain-derived biomarkers, demonstrating the potential of the technology to support liquid biopsy for the brain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".