Circulating tumor DNA in early-stage colon cancer: ready for prime time or needing refinement?
Bibliographic record
Abstract
Liquid biopsies are the detection of molecular information in fluids from patients with cancer. In colorectal cancer (CRC), the most promising liquid biopsy strategy is the use of circulating tumor DNA (ctDNA) from plasma. In early-stage CRC, the potential for ctDNA to impact care stems from the detection of minimal residual disease (MRD) to guide adjuvant therapy after curative intent treatment and in identifying recurrences during surveillance. As for any new diagnostic test, ctDNA assays must overcome pre-analytical and analytical challenges before clinical implementation. We will discuss important logistical and assay considerations that clinicians and patients should understand when assessing ctDNA assays. We will also delve into important concepts to aid in interpreting ctDNA results and potential incidental findings that may arise. Sequencing errors, germline variants, and clonal hematopoiesis of indeterminate potential (CHIP) must be addressed to properly interpret results. CHIP is also an important consideration that impacts patient prognosis through association with cardiovascular and hematologic diseases. With this background in place, we next review the best available evidence for the use of ctDNA in early-stage colon cancer. Observational cohorts have established MRD after surgery as a significant prognostic factor for recurrence in stage II and III colon cancer. It also has the ability to anticipate clinical recurrence before standard investigations when used in surveillance. The first and only interventional randomized trial to date evaluating ctDNA is DYNAMIC. The study demonstrated the noninferiority of a MRD detection-guided approach in selecting patients with stage II colon cancer for adjuvant treatment. Notwithstanding the important results, there are still important questions to be answered before ctDNA enters prime time in the clinic. However, future appears bright and ongoing trials will help clarify how to best use this technology in early-stage colon cancer.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".