Nanoroughened adhesion-based capture of circulating tumor cells with heterogeneous expression and metastatic characteristics
Bibliographic record
Abstract
BACKGROUND: Circulating tumor cells (CTCs) have shown prognostic relevance in many cancer types. However, the majority of current CTC capture methods rely on positive selection techniques that require a priori knowledge about the surface protein expression of disseminated CTCs, which are known to be a dynamic population. METHODS: We developed a microfluidic CTC capture chip that incorporated a nanoroughened glass substrate for capturing CTCs from blood samples. Our CTC capture chip utilized the differential adhesion preference of cancer cells to nanoroughened etched glass surfaces as compared to normal blood cells and thus did not depend on the physical size or surface protein expression of CTCs. RESULTS: The microfluidic CTC capture chip was able to achieve a superior capture yield for both epithelial cell adhesion molecule positive (EpCAM+) and EpCAM- cancer cells in blood samples. Additionally, the microfluidic CTC chip captured CTCs undergoing transforming growth factor beta-induced epithelial-to-mesenchymal transition (TGF-β-induced EMT) with dynamically down-regulated EpCAM expression. In a mouse model of human breast cancer using EpCAM positive and negative cell lines, the number of CTCs captured correlated positively with the size of the primary tumor and was independent of their EpCAM expression. Furthermore, in a syngeneic mouse model of lung cancer using cell lines with differential metastasis capability, CTCs were captured from all mice with detectable primary tumors independent of the cell lines' metastatic ability. CONCLUSIONS: The microfluidic CTC capture chip using a novel nanoroughened glass substrate is broadly applicable to capturing heterogeneous CTC populations of clinical interest independent of their surface marker expression and metastatic propensity. We were able to capture CTCs from a non-metastatic lung cancer model, demonstrating the potential of the chip to collect the entirety of CTC populations including subgroups of distinct biological and phenotypical properties. Further exploration of the biological potential of metastatic and presumably non-metastatic CTCs captured using the microfluidic chip will yield insights into their relevant differences and their effects on tumor progression and cancer outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".