Using Circulating Tumor DNA in Colorectal Cancer: Current and Evolving Practices
Bibliographic record
Abstract
There exists a tremendous opportunity in identifying and determining the appropriate predictive and prognostic biomarker(s) for risk stratification of patients with colorectal cancers (CRCs). Circulating tumor DNA (ctDNA) has emerged as a promising prognostic and possibly predictive biomarker in the personalized management of patients with CRCs. The disease is particularly suited to a liquid biopsy-based approach since there is a great deal of shedding of circulating tumor fragments (cells, DNA, methylation markers, etc). ctDNA has been shown to have several potential applications, including detecting minimal residual disease (MRD), monitoring for early recurrence, molecular profiling, and therapeutic response prediction. The utility of ctDNA has broadened from its initial use in the advanced/metastatic setting for molecular profiling and detection of acquired resistance mechanisms, toward identifying MRD, as well as early detection. Prospective studies such as CIRCULATE, COBRA, Dynamic II/III, and ACT3 are underway in the MRD setting to further understand how ctDNA may be used to inform clinical decision making using both tumor-informed and tumor-agnostic platforms. These prospective studies use ctDNA to guide management of patients with CRC and will be critical to help guide how and where ctDNA should or should not be used in clinical decision making. It is also important to understand that there are different types of ctDNA liquid biopsy platforms, each with advantages and disadvantages in different clinical indications. This review provides an overview of the current and evolving use of ctDNA in CRC.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".