User‐defined protein marker assay development for characterization of circulating tumor cells using the CellSearch® system
Bibliographic record
Abstract
The majority of cancer-related deaths result from metastasis, which has been associated with the presence of circulating tumor cells (CTCs). It has been shown that CTC cut-off values exist that predict for poorer overall survival in metastatic breast (≥5), prostate (≥5), and colorectal (≥3) cancer based on assessment of 7.5 ml of blood. Development of the CellSearch® system (Veridex) has allowed for sensitive enumeration of CTCs. In the current study, protocols were developed and optimized for use with the CellSearch system to characterize CTCs with respect to user-defined protein markers of interest in human blood samples, including the cancer stem cell marker CD44 and the apoptosis marker M-30. Flow cytometry (FCM) experiments were initially carried out to assess expression of CD44 and M-30 on MDA-MB-468 human tumor cells. Human blood samples were then spiked with MDA-MB-468 cells and processed with the appropriate antibody (CD44/M-30) on the CellSearch. Detailed optimization of CD44 was carried out on the CellSearch using various antibody concentrations, exposure times, and cell lines with varying CD44 expression. Troubleshooting experiments were undertaken to explain observed discrepancies between FCM and CellSearch results for the M-30 marker. After extensive optimization, the best CD44/M-30 concentrations and exposure times were determined to be 1.5/3.5 μg/ml and 0.2/0.8 s, respectively. The percentage of CD44(+) tumor cells was 99.5 ± 0.39% by FCM and 98.8 ± 0.51% by the CellSearch system. The percentage of M-30(+) tumor cells following paclitaxel treatment was 17.6 ± 1.18% by FCM and 10.9 ± 2.41% by CellSearch. Proper optimization of the CD44 marker was achieved; however, M-30 does not appear to be a suitable marker for use in this platform. Taken together, the current study provides a detailed description of the process of user-defined protein marker development and optimization using the CellSearch, and will be an important resource for the future development of protein marker assays by users of this platform.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".