Cell-free DNA (cfDNA): Clinical Significance and Utility in Cancer Shaped By Emerging Technologies
Bibliographic record
Abstract
Precision oncology is predicated upon the ability to detect specific actionable genomic alterations and to monitor their adaptive evolution during treatment to counter resistance. Because of spatial and temporal heterogeneity and comorbidities associated with obtaining tumor tissues, especially in the case of metastatic disease, traditional methods for tumor sampling are impractical for this application. Known to be present in the blood of cancer patients for decades, cell-free DNA (cfDNA) is beginning to inform on tumor genetics, tumor burden, and mechanisms of progression and drug resistance. This substrate is amenable for inexpensive noninvasive testing and thus presents a viable approach to serial sampling for screening and monitoring tumor progression. The fragmentation, low yield, and variable admixture of normal DNA present formidable technical challenges for realization of this potential. This review summarizes the history of cfDNA discovery, its biological properties, and explores emerging technologies for clinically relevant sequence-based analysis of cfDNA in cancer patients. Molecular barcoding (or Unique Molecular Identifier, UMI)-based methods currently appear to offer an optimal balance between sensitivity, flexibility, and cost and constitute a promising approach for clinically relevant assays for near real-time monitoring of treatment-induced mutational adaptations to guide evidence-based precision oncology. Mol Cancer Res; 14(10); 898-908. ©2016 AACR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".