Circulating tumor cells: detection, molecular profiling and future prospects
Bibliographic record
Abstract
Disseminated malignancy is responsible for the vast majority of cancer-related deaths. During this process, circulating tumor cells (CTC) are generated, spread from the primary tumor, colonize distant organs and lead to overt metastatic disease. CTC are essential for establishing metastasis; however, they are not sufficient as this process is highly inefficient and most will fail to grow in target sites. Several CTC die during migration while others remain dormant for several years and very few grow into macrometastases. CTC have been well documented in the bloodstream of cancer patients; however, the clinical relevance of this detection is still the subject of controversies and their biology is poorly understood. Indeed, available markers fail to distinguish between subgroups of CTC, and several current methods lack sensitivity, specificity or reproducibility in CTC characterization and detection. The advent of more precise technologies is renewing the interest in CTC biology. We will review herein recent findings on CTC biology, on the role of host-tumor interactions in CTC shedding and implantation, available methods of CTC detection and future perspectives for the molecular characterization of the CTC subset(s) responsible for the development of metastasis. Ultimately, understanding CTC biology and host-tumor 'complementarities' will help define metastasis-related biomarkers providing formidable and tailored novel therapeutic targets.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".