Enumeration, characterization, and collection of intact circulating tumor cells by cross contamination‐free flow cytometry
Bibliographic record
Abstract
Circulating tumor cells (CTC) are an important biomarker for several solid cancers. Most of the commercially available systems for enumeration of CTC are based on immunomagnetic enrichment of epithelial cell adhesion molecule (EpCAM/CD326)-expressing CTC before microscopic cell imaging or reverse-transcription PCR (RT-PCR). The aim of this study was to establish a practical method for enumeration of CTC using a novel flow cytometer that has a disposable microfluidic chip, which is designed to realize absolute cross contamination-free measurements and to collect the analyzed cell sample. Although the process of enumeration and labeling of CTC was optimized for this device, the simplified protocol described here could be applied to other flow cytometers. Cultured cancer cells spiked into normal blood were enriched using MACS® EpCAM-MicroBeads following cell labeling with an allophycocyanin (APC)-conjugated EpCAM mAb, instead of by intracellular staining of cytokeratins (CK). The EpCAM double-positive selection/labeling method allows enumeration of intact CTC, maintenance of cellular integrity, and the concomitant performance of a CTC viability test. The combination of the fine-tuned CTC enrichment process and the cytometric multicolor analysis resulted in a linear relationship between the output cell count and the input cell number from zero to hundreds of cells. In particular, a satisfactory signal/noise ratio was obtained by gate-exclusion of leukocyte signals using an anti-CD45 mAb. The entire process had little influence on the viability of the spiked lung cancer cell PC-9. Measured PC-9 and breast cancer MCF-7 cells bearing EpCAM-MicroBeads, APC-conjugated EpCAM mAb, and the DNA staining dye SYTO9 grew normally, demonstrating the potential usefulness of the collected samples for further studies. This intact CTC enumeration and analysis procedure (iCeap) would be of great benefit to clinicians by providing them with rapid stratification of antitumor therapy, and to basic researchers by permitting further molecular and cellular characterization of CTC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".