Enhanced Detection of Cancer Biomarkers in Blood-Borne Extracellular Vesicles Using Nanodroplets and Focused Ultrasound
Bibliographic record
Abstract
The feasibility of personalized medicine approaches will be greatly improved by the development of noninvasive methods to interrogate tumor biology. Extracellular vesicles shed by solid tumors into the bloodstream have been under recent investigation as a source of tumor-derived biomarkers such as proteins and nucleic acids. We report here an approach using submicrometer perfluorobutane nanodroplets and focused ultrasound to enhance the release of extracellular vesicles from specific locations in tumors into the blood. The released extracellular vesicles were enumerated and characterized using micro flow cytometry. Only in the presence of nanodroplets could ultrasound release appreciable levels of tumor-derived vesicles into the blood. Sonication of HT1080-GFP tumors did not increase the number of circulating tumor cells or the metastatic burden in the tumor-bearing embryos. A variety of biological molecules were successfully detected in tumor-derived extracellular vesicles, including cancer-associated proteins, mRNAs, and miRNAs. Sonication of xenograft HT1080 fibrosarcoma tumors released extracellular vesicles that contained detectable RAC1 mRNA with the highly tumorigenic N92I mutation known to exist in HT1080 cells. Deep sequencing serum samples of embryos with sonicated tumors allowed the identification of an additional 13 known heterozygous mutations in HT1080 cells. Applying ultrasound to HT1080 tumors increased tumor-derived DNA in the serum by two orders of magnitude. This work is the first demonstration of enhanced extracellular vesicle release by ultrasound stimulation and suggests that nanodroplets/ultrasound offers promise for genetic profiling of tumor phenotype and aggressiveness by stimulating the release of extracellular vesicles. Cancer Res; 77(1); 3-13. ©2016 AACR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".