CD44v6 Defines a New Population of Circulating Tumor Cells Not Expressing EpCAM
Bibliographic record
Abstract
Circulating tumor cells (CTCs) are promising diagnostic and prognostic tools for clinical use. In several cancers, including colorectal and breast, the CTC load has been associated with a therapeutic response as well as progression-free and overall survival. However, counting and isolating CTCs remains sub-optimal because they are currently largely identified by epithelial markers such as EpCAM. New, complementary CTC surface markers are therefore urgently needed. We previously demonstrated that a splice variant of CD44, CD44 variable alternative exon 6 (CD44v6), is highly and specifically expressed by CTC cell lines derived from blood samples in colorectal cancer (CRC) patients. Two different approaches-immune detection coupled with magnetic beads and fluorescence-activated cell sorting-were optimized to purify CTCs from patient blood samples based on high expressions of CD44v6. We revealed the potential of the CD44v6 as a complementary marker to EpCAM to detect and purify CTCs in colorectal cancer blood samples. Furthermore, this marker is not restricted to colorectal cancer since CD44v6 is also expressed on CTCs from breast cancer patients. Overall, these results strongly suggest that CD44v6 could be useful to enumerate and purify CTCs from cancers of different origins, paving the way to more efficacious combined markers that encompass CTC heterogeneity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".