Sample-to-Answer Isolation and mRNA Profiling of Circulating Tumor Cells
Bibliographic record
Abstract
The isolation and rapid molecular characterization of circulating tumor cells (CTCs) from a liquid biopsy could enable the convenient and effective characterization of the state and aggressiveness of cancerous tumors. Existing technologies enumerate CTCs using immunostaining; however, these approaches are slow, labor-intensive, and often fail to enable further genetic characterization of CTCs. Here, we report on an integrated circuit that combines the capture of CTCs with the profiling of their gene expression signatures. Specifically, we use a velocity valley chip to efficiently capture magnetic nanoparticle-bound CTCs, which are then directly analyzed for their gene expression profiles using nanostructured microelectrode biosensors. CTCs are captured with 97% efficiency from 2 mL of whole blood, yielding a 500-fold concentration within 1 h. We show efficient capture of as few as 2 cancer cells/(mL of blood) and demonstrate that the gene expression module accurately profiles the expression of prostate-specific genes in CTCs captured from whole blood. This advance provides the first sample-to-answer solution for gene-based testing of CTCs. The approach was successfully validated using samples collected from prostate cancer patients: both CTCs and prostate-specific antigen (PSA) mRNA sequences were detected in all cancer patient samples and not in the healthy controls.
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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".