Abstract 1562: Clusters of circulating tumor cells were selectively isolated in the blood of 12/12 epithelial ovarian cancer patients using facile gravity-flow-based filtration method adapted to clinical use
Bibliographic record
Abstract
Abstract Background The presence of circulating tumor cells (CTCs) in blood is correlated with disease progression in many cancers. Their prognosis value in ovarian cancer is still under debate.1 CTCs are heterogeneous in size and marker expression, and sub-populations with various metastatic potentials have been identified. CTC clusters, although rare and difficult to isolate, have emerged as a possible driver of metastasis owing to ~50-time higher metastatic potential than single CTCs.2 Only few methods have emerged to capture clusters, and are often complex and cumbersome, limiting our understanding of the role of clusters in metastasis. Here, we present a new filtration method for the selective capture of CTC clusters from blood and found clusters in 12/12 epithelial ovarian cancer (EOC) patients. Method Cluster capture was performed by filtration using a 3D printed cartridge3 and filters4 with pore diameters of 8, 10, 12, 15, 20 or 28 μm. We developed a gravity-driven process, generating reduced shear stress, and optimized capture using blood (1:6, v/v, in PBS) spiked with OV-90 and OVCAR-3 ovarian cancer single cells and clusters. Blood samples from 12 EOC patients were filtered. Clusters can be stained and imaged on the filter, or released for downstream analysis. Results Using the gravity-setup, we were able to selectively capture clusters with good integrity and with a rate that outperforms other technologies to the best of our knowledge. Viable CTC clusters, with 2 to >100 cells, were captured from 12/12 EOC patients. Their size distribution was surprisingly similar between patients. Small clusters (2-3 cells) were the most frequent, and this frequency decreased as their size increased. The molecular characterization of the captured clusters revealed a low and localized, heterogeneous expression of EpCAM (epithelial cell adhesion molecule), in combination with a widespread expression of c-MET (hepatocyte growth factor receptor) in all patients, suggesting a mesenchymal-like profile. Conclusion Using the gravity-filtration setup, CTC clusters were captured from the blood of all patients tested, suggesting that clusters are much more widespread than anticipated, and are in fact the norm rather than the exception. The cluster size distribution was conserved between patients with small clusters dominating, and some rare, very large clusters. Cluster staining revealed a mesenchymal profile, in agreement with a higher metastatic potential. Together, these results suggest that clusters should significantly contribute to disease progression, a hypothesis, which may be explored using our facile and selective method. References 1. Y. Zhou, et al. PLoS ONE 2015, 10, e0130873. 2. N. Aceto, et al. Cell 2014, 158, 1110. 3. A. Meunier, et al. Anal. Chem. 2016, 88, 8510. 4. J. A. Hernandez-Castro, et al. LOC 2017, 17, 1960 Citation Format: Anne Meunier, Sara Kheireddine, J. Alejandro Hernández-Castro, Benjamin Péant, Diane Provencher, Anne-Marie Mes-Masson, Teodor Veres, David Juncker. Clusters of circulating tumor cells were selectively isolated in the blood of 12/12 epithelial ovarian cancer patients using facile gravity-flow-based filtration method adapted to clinical use [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1562.
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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) | 0.002 | 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".