Recent Advances in the Molecular Characterization of Circulating Tumor Cells
Bibliographic record
Abstract
Although circulating tumor cells (CTCs) were first observed over a century ago, lack of sensitive methodology precluded detailed study of these cells until recently. However, technological advances have now facilitated the identification, enumeration, and characterization of CTCs using a variety of methods. The majority of evidence supporting the use of CTCs in clinical decision-making has been related to enumeration using the CellSearch® system and correlation with prognosis. Growing evidence also suggests that CTC monitoring can provide an early indication of patient treatment response based on comparison of CTC levels before and after therapy. However, perhaps the greatest potential that CTCs hold for oncology lies at the level of molecular characterization. Clinical treatment decisions may be more effective if they are based on molecular characteristics of metastatic cells rather than on those of the primary tumor alone. Molecular characterization of CTCs (which can be repeatedly isolated in a minimally invasive fashion) provides the opportunity for a "real-time liquid biopsy" that allows assessment of genetic drift, investigation of molecular disease evolution, and identification of actionable genomic characteristics. This review focuses on recent advances in this area, including approaches involving immunophenotyping, fluorescence in situ hybridization (FISH), multiplex RT-PCR, microarray, and genomic sequencing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".