Circulating Tumor Cells (CTC) and Cell-Free DNA (cfDNA) Workshop 2016: Scientific Opportunities and Logistics for Cancer Clinical Trial Incorporation
Bibliographic record
Abstract
Despite the identification of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) as potential blood-based biomarkers capable of providing prognostic and predictive information in cancer, they have not been incorporated into routine clinical practice. This resistance is due in part to technological limitations hampering CTC and cfDNA analysis, as well as a limited understanding of precisely how to interpret emergent biomarkers across various disease stages and tumor types. In recognition of these challenges, a group of researchers and clinicians focused on blood-based biomarker development met at the Canadian Cancer Trials Group (CCTG) Spring Meeting in Toronto, Canada on 29 April 2016 for a workshop discussing novel CTC/cfDNA technologies, interpretation of data obtained from CTCs versus cfDNA, challenges regarding disease evolution and heterogeneity, and logistical considerations for incorporation of CTCs/cfDNA into clinical trials, and ultimately into routine clinical use. The objectives of this workshop included discussion of the current barriers to clinical implementation and recent progress made in the field, as well as fueling meaningful collaborations and partnerships between researchers and clinicians. We anticipate that the considerations highlighted at this workshop will lead to advances in both basic and translational research and will ultimately impact patient management strategies and patient outcomes.
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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 | 0.178 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".