Bibliographic record
Abstract
BACKGROUND: Liquid biopsy, in which tumor cells and tumor-derived biomolecules are collected from the circulation, is an attractive strategy for the management of cancer that allows the serial monitoring of patients during treatment. The analysis of circulating DNA produced by tumors provides a means to collect genotypic information about the molecular profile of a patient's cancer. Phenotypic information, which may be highly relevant for therapeutic selection, is ideally derived from intact cells, necessitating the analysis of circulating tumor cells (CTCs). CONTENT: Recent advances in profiling CTCs at the single-cell level are providing new ways to collect critical phenotypic information. Analysis of secreted proteins, surface proteins, and intracellular RNAs for CTCs at the single-cell level is now possible and provides a means to quantify molecular markers that are involved with the mechanism of action of the newest therapeutics. We review the latest technological advances in this area along with related breakthroughs in high-purity CTC capture and in vivo profiling approaches, and we also present a perspective on how genotypic and phenotypic information collected via liquid biopsies is being used in the clinic. SUMMARY: Over the past 5 years, the use of liquid biopsy has been adopted in clinical medicine, representing a major paradigm shift in how molecular testing is used in cancer management. The first tests to be used are genotypic measurements of tumor mutations that affect therapeutic effectiveness. Phenotypic information is also clinically relevant and essential for monitoring proteins and RNA sequences that are involved in therapeutic response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".