Clinical evaluation of a novel microfluidic device for epitope-independent enrichment of circulating tumour cells in patients with small cell lung cancer
Bibliographic record
Abstract
Circulating tumour cells (CTCs) have potential utility as minimally-invasive biomarkers to aid cancer treatment decision making. However, many current CTC technologies enrich CTCs using specific surface epitopes that do not necessarily reflect CTC heterogeneity. Here we evaluated the epitope-independent Parsortix system which enriches CTCs based on size and rigidity using both healthy normal volunteer blood samples spiked with tumour cells and blood samples from patients with small cell lung cancer (SCLC). Blood samples were maintained unfractionated at room temperature for up to 4 days followed by plasma removal for circulating free DNA (cfDNA) isolation and direct application of the remaining cell component to the Parsortix system. For tumour cells expressing the EpCAM cell surface marker the numbers of spiked cells retained using the Parsortix system and by EpCAM-positive selection using CellSearch® were not significantly different, whereas only the Parsortix system showed strong enrichment of cells with undetectable EpCAM expression. In a pilot clinical study we banked both enriched CTCs as well as plasma from SCLC patient blood samples. Upon retrieval of the banked Parsortix cellular samples we could detect cytokeratin positive CTCs in all 12 SCLC patients tested. Interestingly, processing parallel samples from the same patients by EpCAM enrichment using CellSearch® revealed only 83% (10/12) with cytokeratin positive CTCs indicating the Parsortix system is enriching for EpCAM negative SCLC CTCs. Our combined results indicate the Parsortix system is a valuable tool for combined cfDNA isolation and CTC enrichment that enables CTC analysis to be extended beyond dependence on surface epitopes.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".