Detection and Quantification of Circulating Tumor Cells in PBMCs or Blood Using Flow Cytometry
Bibliographic record
Abstract
Abstract Circulating Tumor Cells (CTCs) are cells that have detached from primary tumor tissues or metastases and entered into peripheral blood circulation, led to cancer recurrence and distal metastasis. Evaluation of CTCs conduces to tumor diagnosis, treatment, monitoring, and prognosis. Tumor cell spiking assay was frequently used to evaluate the sensitivity, specificity, accuracy, and repeatability of the methods or systems for enumeration of rare CTCs. In this work, a novel flow cytometer NovoCyte® Quanteon™ was employed to measure and quantify spiked human tumor cells of colon carcinoma (SW620) in normal PBMCs or peripheral blood. First, immunofluorescence labeled (EpCAM-APC antibody) SW620 cells (0–256 cells) were serially diluted in 1×106 PBMCs and tested directly on Quanteon. The data indicate that CTC detection using Quanteon has a sensitivity of 0.0001% (one CTC per million cells) and an excellent linear relationship between the number of cells detected and the predicted number added. Furthermore, SW620 cells (0–2000 cells) were serially diluted in 2mL normal whole blood, following the traditional isolation of PBMCs, magnetic EpCAM-positive selection, immunofluorescence labeling (CD45 FITC, EpCAM APC) and washing. The results show that the average recovery of CTCs is more than 75%. Flow cytometry is a valuable tool for therapeutic monitoring and prognosis assessment. Detection of CTC is critical for a “liquid biopsy”. These experimental results demonstrate that the NovoCyte Quanteon flow cytometer can provide high specificity and accuracy of CTC detection through both direct identification and EpCAM affinity bead-based enrichment methods.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".