Circulating Tumor DNA as a Cancer Biomarker: Fact or Fiction?
Bibliographic record
Abstract
The notion of utilizing cell-free DNA (cfDNA)8 in the circulation as a surrogate biomarker is not a novel concept. Mandel and Metais identified the presence of cfDNA in the blood of healthy individuals almost 60 years ago. Decades later, multiple groups were able to extend the work of Mandel and Metais into the identification of tumor-derived cfDNA—also known as circulating tumor DNA (ctDNA)—in the blood of cancer patients. These findings suggested that a “liquid biopsy” may be a feasible clinical tool because tumors seem to release fragments of DNA into the circulatory system that are both detectable and specific to the tumor. In the past decade, we have witnessed a surge in both new technologies and improvements on existing technologies for sequencing DNA that have made this once-laborious process cheaper and faster. In 2009, the cost of sequencing per genome was $100 000, whereas in 2014, this cost dropped to $5000 (taking into account labor, administration, management, utilities, reagents, and consumables). As a result, the use of ctDNA as a liquid biopsy has become ever more feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.015 |
| 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".