EMT-independent detection of circulating tumor cells in human blood samples and pre-clinical mouse models of metastasis
Bibliographic record
Abstract
Circulating tumor cells (CTCs) present an opportunity to detect/monitor metastasis throughout disease progression. The CellSearch® is currently the only FDA-approved technology for CTC detection in patients. The main limitation of this system is its reliance on epithelial markers for CTC isolation/enumeration, which reduces its ability to detect more aggressive mesenchymal CTCs that are generated during metastasis via epithelial-to-mesenchymal transition (EMT). This Technical Note describes and validates two EMT-independent CTC analysis protocols; one for human samples using Parsortix® and one for mouse samples using VyCap. Parsortix® identifies significantly more mesenchymal human CTCs compared to the clinical CellSearch® test, and VyCap identifies significantly more CTCs compared to our mouse CellSearch® protocol regardless of EMT status. Recovery and downstream molecular characterization of CTCs is highly feasible using both Parsortix® and VyCap. The described CTC protocols can be used by investigators to study CTC generation, EMT and metastasis in both pre-clinical models and clinical samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".