Monitoring of breast cancer progression via aptamer-based detection of circulating tumor cells in clinical blood samples
Bibliographic record
Abstract
Introduction: Breast cancer (BC) diagnostics lack noninvasive methods and procedures for screening and monitoring disease dynamics. Admitted CellSearch® is used for fluid biopsy and capture of circulating tumor cells of only epithelial origin. Here we describe an RNA aptamer (MDA231) for detecting BC cells in clinical samples, including blood. The MDA231 aptamer was originally selected against triple-negative breast cancer cell line MDA-MB-231 using cell-SELEX. Methods: The aptamer structure in solution was predicted using mFold program and molecular dynamic simulations. The affinity and specificity of the evolved aptamers were evaluated by flow cytometry and laser scanning microscopy on clinical tissues from breast cancer patients. CTCs were isolated form the patients’ blood using the developed method of aptamer-based magnetic separation. Breast cancer origin of CTCs was confirmed by cytological, RT-qPCR and Immunocytochemical analyses. Results: MDA231 can specifically recognize breast cancer cells in surgically resected tissues from patients with different molecular subtypes: triple-negative, Luminal A, and Luminal B, but not in benign tumors, lung cancer, glial tumor and healthy epithelial from lungs and breast. This RNA aptamer can identify cancer cells in complex cellular environments, including tumor biopsies (e.g., tumor tissues vs. margins) and clinical blood samples (e.g., circulating tumor cells). Breast cancer origin of the aptamer-based magnetically separated CTCs has been proved by immunocytochemistry and mammaglobin mRNA expression. Discussion: We suggest a simple, minimally-invasive breast cancer diagnostic method based on non-epithelial MDA231 aptamer-specific magnetic isolation of circulating tumor cells. Isolated cells are intact and can be utilized for molecular diagnostics purposes.
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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".