Quantifying EpCAM heterogeneity of circulating-tumor-cells (CTCs) from small cell lung cancer (SCLC) patients.
Bibliographic record
Abstract
e20091 Background: Tumor heterogeneity and evolution of SCLC is poorly defined. Serial longitudinal interrogation of tumor heterogeneity from CTCs detected in peripheral blood patient (pt) samples is a potential strategy to address this gap in knowledge. However, existing technology is generally limited to the capture and enumeration of CTCs, without a high-throughput method to quantify phenotypic properties. Here, we evaluated a novel nanotechnology platform – nanoparticle-mediated magnetic ranking cytometry (MagRC) to profile SCLC CTCs by EpCAM protein expression. Methods: Blood samples from 20 SCLC pts were processed through the MagRC platform. Magnetic nanoparticles conjugated with anti-EpCAM antibodies were incubated with whole blood samples then introduced into the MagRC device where CTCs are sorted by differently sized nickel micromagnets within microfluidic channels. Captured CTCs are ranked into 8 zones that correlate with EpCAM expression levels (zone 1 = highest to 8 = lowest). For 8 pts, all samples were processed at a 1mL/hr flow rate (fr), and for 12 pts, a 0.5mL/hr fr was also studied; 66% of all chips were processed at a 1ml/hr fr and 34% at a 0.5ml/hr fr. The average zone for each chip was compared to the flow rate, age, and stage (extensive-stage (ES) vs limited-stage (LS)). The differences were tested using the Wald test within the linear mixed effects model. Results: Among 20 pts, 11 were ES; 9 were LS. Median age at diagnosis was 69 years old (yo); 65% were male. We detected CTCs in 12 of 20 pts (60%), similar to other studies. When comparing the MagRC-ranked EpCAM zone, the 0.5mL/hr fr demonstrated a lower median zone (4.3 vs 6.5; p < 0.001) as compared to the 1mL/hr rate. Interestingly, pts > 65 yo had a higher median zone (6.2 vs. 3.5; p = 0.019) compared to those ≤65 yo. The effect remained significant after controlling for flow rate (p = 0.002). No EpCAM zone difference was detected between ES and LS. Conclusions: We demonstrate the ability of MagRC to quantify EpCAM expression levels of CTCs from SCLC pts. We observed a higher MagRC zone (i.e. lower EpCAM expression) from pts > 65 yo. This observation requires validation in larger datasets along with continued investigation into the biology of SCLC CTCs.
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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 | 0.000 | 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".