Targeting CTC Heterogeneity: Aptamer-Based Liquid Biopsy Predicts Outcome in Lung Cancer
Bibliographic record
Abstract
Background: The detection of circulating tumor cells (CTCs) holds significant promise for the diagnosis and monitoring of lung cancer (LC). However, the clinical utility of CTCs is limited by the heterogeneity of their phenotypes and the shortcomings of existing detection methods, which often rely on epithelial markers like EpCAM. DNA aptamers offer a promising alternative due to their high affinity, stability, and ability to recognize diverse cancer-specific biomarkers. Methods: This study utilized DNA aptamers LC-17 and LC-18, previously selected against primary lung tumor tissue, to isolate and detect CTCs in the peripheral blood of 43 non-small cell lung cancer (NSCLC) patients. Mass spectrometry (LC-MS/MS) was employed to identify the target proteins of aptamer LC-17. CTCs from patients’ blood and healthy donors were isolated via filtration after erythrocyte and lymphocyte lysis and stained with FAM-labeled LC-17 and LC-18 aptamers for detection using fluorescence and light microscopy. Results: Mass spectrometry identified neutrophil defensin 1 (DEFA1) and peroxiredoxin-2 (PRDX2) as the primary protein targets of aptamer LC-17 in CTCs, both of which were absent in healthy donor samples. CTC enumeration revealed statistically significant correlations between elevated CTC counts (>3 cells/4 mL blood) and advanced primary tumor size (T4 vs. T1–T3, p = 0.012), extensive regional lymph node metastasis (N3 vs. N1–N2, p = 0.014), and shorter overall survival (median 24 vs. 32 months, p < 0.05). Conclusions: The developed aptamer-based liquid biopsy method effectively captures heterogeneous CTC populations independent of EpCAM expression. The strong correlation of CTC counts with disease progression and survival underscores their clinical relevance as a prognostic biomarker in NSCLC. This approach presents a viable, non-invasive tool for disease monitoring and stratification of NSCLC patients, with potential for integration into clinical practice.
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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 | 0.001 |
| 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".