Combination of Mechanical and Molecular Filtration for Enhanced Enrichment of Circulating Tumor Cells
Bibliographic record
Abstract
Circulating tumor cells (CTCs) have been linked to cancer progression but are difficult to isolate, as they are very rare and heterogeneous, covering a range of sizes and expressing different molecular receptors. Filtration has emerged as a simple and powerful method to enrich CTCs but only captures cells above a certain size regardless of molecular characteristics. Here, we introduce antibody-functionalized microfilters to isolate CTCs based on both size and surface receptor expression. We present a 3D printed filtration cartridge with microfabricated polymer filters with 8, 10, 12, 15, or 20 μm-diameter pores. Pristine filters were used to optimize sample dilution, rinsing protocol, flow rate, and pore size, leading to >80% for the recovery of spiked cancer cells with very low white blood cell contamination (<1000). Then, filters were functionalized with antibodies against either epithelial cell adhesion molecule (EpCAM) or epidermal growth factor receptor (EGFR) and the cartridges were used to enrich breast (MDA-MB-231, MCF-7) and renal (786-O, A-498) cancer cells expressing various levels of EpCAM and EGFR. Cancer cells were spiked into human blood, and when using filters with antibodies specific to a molecular receptor expressed on a cell, efficiency was increased to >96%. These results suggest that filtration can be optimized to target specific CTC characteristics such as size and receptor expression and that a diverse range of CTCs may be captured using particular combinations of pore size, filtration parameters, and antibody functionalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".