Gene expression profiling of single circulating tumor cells in ovarian cancer – Establishment of a multi‐marker gene panel
Bibliographic record
Abstract
The presence of circulating tumor cells (CTCs) in the blood of ovarian cancer patients was shown to correlate with decreased overall survival, whereby CTCs with epithelial-mesenchymal-transition (EMT) or stem-like traits are supposed to be involved in metastatic progression and recurrence. Thus, investigating the transcriptional profiles of CTCs might help to identify therapy resistant tumor cells and to overcome treatment failure. For this purpose, we established a multi-marker panel for the molecular characterization of single CTCs, detecting epithelial (EpCAM, Muc-1, CK5/7), EMT (N-cadherin, Vimentin, Snai1/2, CD117, CD146, CD49f) and stem cell (CD44, ALDH1A1, Nanog, SOX2, Notch1/4, Oct4, Lin28) associated transcripts. First primer specificity and PCR-performance of the multiplex-RT-PCRs were successfully validated on genomic DNA and cDNA isolated from OvCar3 cells. The assay sensitivity of the epithelial panel was evaluated by adding defined numbers of tumor cells into the blood of healthy donors and performing a subsequent immunomagnetic tumor cell enrichment (AdnaTest OvarianCancerSelect), resulting in a 100% concordance for the epithelial markers EpCAM and Muc-1 to the AdnaTest OvarianCancerDetect. Additionally, by processing blood from ovarian cancer patients, high assay sensitivity could be verified. In blood of healthy donors no signals for epithelial markers were detected, for EMT and stem cell markers, however, signals were obtained mainly originating from leukocytes which calls for single cell analysis. To that aim by using the ovarian cancer cell line OvCar3, we successfully established a workflow enabling the characterization of single CTCs. It consists of a density gradient-dependent enrichment for nucleated cells, a depletion of CD45-positive cells of hematopoietic origin followed by immunofluorescent labeling of CTCs by EpCAM and Muc-1. Single CTCs are then isolated by micromanipulation and processed for panel gene expression profiling. Finally, fifteen single CTCs from three ovarian cancer patients were analyzed and found to be positive for stem cell (CD44, ALDH1A1, Nanog, Oct4) and EMT markers (N-cadherin, Vimentin, Snai2, CD117, CD146). Albeit, inter-cellular and intra/inter-patient heterogeneity and co-expression of epithelial, mesenchymal and stem cell transcripts on the same CTC was observed. We have established a robust workflow to perform sensitive single cell panel gene expression analysis without the need of pre-amplification steps. Our data point towards a heterogeneous expression of stem cell and EMT associated transcripts in ovarian cancer CTCs.
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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 | 0.000 | 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) | 0.001 | 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 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".