A new enrichment model for high sensitivity detection and downstream analyses of circulating tumor cells in breast cancer patients.
Bibliographic record
Abstract
Abstract Abstract #4162 The detection of circulating tumor cells (CTCs) in breast cancer patients have the potential to improve prognostication and the monitoring of response to treatment. Most CTC enrichment technologies are based on binding to anti-EpCAM antibodies. The sensitivity of such assays is limited by tumors that express no or undetectable levels of EpCAM. Improvements in CTC detection coupled with the development of systems to interrogate CTCs for therapeutic target expression could lead to novel applications for patient monitoring, clinical diagnosis and treatment. In this study, we describe a sensitive and reproducible enrichment method for CTCs. We defined cells as circulating tumor cells with three criteria: Positive for cytokeratin (CK+) and DAPI (nuclear) (DAPI+) and negative staining for CD45 (CD45-). We have previously reported that this system has a higher sensitivity for circulating tumor cell detection and provides a better platform for CTC downstream analyses compare to the methods currently available in the market. Herein, we describe the use of this platform for the evaluation of breast cancer biomarkers in CTCs. Blood samples from patients with metastatic breast cancer were used for CellSearch™ assay (Veridex , LLC ) and our CTC assay (A1000 CTC enrichment and detection kit, Genetix). We performed the CTC enrichment assay using the combination of anti-CK and anti-EpCAM antibodies. CTCs were identified with brightfield and fluorescence labeled anti-CK, anti-CD45 and DAPI (nuclear stain) images. The Ariol® system (Applied Imaging Corporation) was employed for automated cell image capture and analysis of CTCs on glass slides. CTCs enriched on the glass slides were used for CTC downstream analysis. Our CTC enrichment model is designed to have the capability to enrich all the three types of CTCs including CK+ & EpCAM+, CK+ & EpCAM-/low and CK-/low & EpCAM+ cells. Compared to the enrichment methods using anti-EpCAM or anti-cytokeratin antibody alone, a higher CTC detection rate and a larger dynamic CTC detected range were obtained with our new enrichment model. Interestingly there were clear CTC number differences with enrichment methods in the higher CTC count patient samples which indicate that the different enrichment methods may enrich different types of CTCs from patient blood samples. Results of DNA and RNA FISH analyses on enriched CTCs indicate that the CTCs on glass slides can be used for its downstream analyses directly or indirectly. Our method may have better performance on enrichment of heterogeneous CTCs and provide a better platform for CTCs profiling for biomarker evaluations and CTC downstream analyses. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4162.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".