Plasma ctDNA as a Treatment Response Biomarker in Metastatic Cancers: Evaluation by the RECIST Working Group
Bibliographic record
Abstract
Early indicators of metastatic cancer response to therapy are important for evaluating new drugs and stopping ineffective treatment. The RECIST guidelines based on repeat cancer imaging are widely adopted in clinical trials, are used to identify active regimens that may change practice, and contribute to regulatory approvals. However, these criteria do not provide insight before 6 to 12 weeks of treatment and typically require that patients have measurable disease. Recent data suggest that measuring on-treatment changes in the amount or proportion of ctDNA in peripheral blood plasma may accurately identify responding and nonresponding cancers at earlier time points. Over the past year, the RECIST working group has evaluated current evidence for plasma ctDNA kinetics as a treatment response biomarker in metastatic cancers and early endpoint in clinical trials to identify areas of focus for future research and validation. Here, we outline the requirement for large standardized trial datasets, greater scrutiny of optimal ctDNA collection time points and assay thresholds, and consideration of regulatory body guidelines and patient opinions. In particular, clinically meaningful changes in plasma ctDNA abundance are likely to differ by cancer type and therapy class and must be assessed before ctDNA can be considered a potential pan-cancer response evaluation biomarker. Despite the need for additional data, minimally invasive on-treatment ctDNA measurements hold promise to build upon existing response assessments such as RECIST and offer opportunities for developing novel early endpoints for modern clinical trials.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".