Abstract 2375: Rapid unbiased enrichment (negative selection) of circulating non-hematopoietic tumor cells directly from whole blood
Bibliographic record
Abstract
Abstract There is increasing interest in analyzing circulating non-hematopoietic tumor cells (CTC) in peripheral blood to evaluate disease progression or response to treatment; however, CTC enrichment is required prior to most analytic procedures. The ideal enrichment method would be rapid, permit a high recovery of viable CTC, and would be independent of the expression of specific epithelial cell surface markers, since CTCs in the peripheral blood may be undergoing EMT (epithelial mesenchymal transition) and may not express epithelial markers. RosetteSep™ CD45 depletion of hematopoietic cells directly from whole blood meets these criteria. However, RosetteSep™ enrichment of CTC involves density gradient centrifugation, which entails careful layering of the sample over the density gradient medium and careful pipetting to remove the enriched cells after centrifugation. Centrifugation must be performed with the brake off to avoid disturbing the enriched cell layer, further lengthening the process. SepMate™, a centrifugation tube with a specialized insert, was developed to minimize mixing of the sample with the density gradient medium, thus allowing rapid layering of the sample on the density gradient medium and easy pouring off of the enriched cells after centrifugation. We compared CTC enrichment using RosetteSep™ and the standard tubes and protocol with RosetteSep™ using SepMate™ tubes and reduced cocktail incubation and spin times on 5 donor whole blood samples seeded with ∼1% CAMA cells. Purity of viable (PI negative) CTC obtained with SepMate™ with RosetteSep™ was 85 ± 7%; purity of CTC obtained with RosetteSep™ alone was 91 ± 5% (no significant difference, paired t test, p=0.050). Purity of CTC obtained with density gradient centrifugation only (no RosetteSep™), either with or without SepMate™, was 4 ± 2%. There was no significant difference in the recovery of enriched CTC under any of the conditions tested (RosetteSep™ ± SepMate™, SepMate™ alone, density gradient separation alone, Tukey-Kramer test, p>0.05). CTC enrichment was accomplished in <40 min using SepMate™ with RosetteSep™. Simplifying the layering and layer removal step makes the entire process easily scalable to processing multiple samples simultaneously. Utilizing this method, CTCs can be enriched directly from whole blood without bias regarding their surface antigen expression. Large samples can be rapidly volume-reduced in preparation for detailed examination in a microfluidic device. Finally, enriched CTC are not labeled with antibodies or beads; there is nothing to interfere with subsequent further enrichment, culture, or evaluation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2375. doi:1538-7445.AM2012-2375
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".