Detection and utility of cell-free and circulating tumour DNA in bone and soft-tissue sarcomas
Bibliographic record
Abstract
Aims Cell-free DNA (cfDNA) and circulating tumour DNA (ctDNA) are used for prognostication and monitoring in patients with carcinomas, but their utility is unclear in sarcomas. The objectives of this pilot study were to explore the prognostic significance of cfDNA and investigate whether tumour-specific alterations can be detected in the circulation of sarcoma patients. Methods Matched tumour and blood were collected from 64 sarcoma patients (n = 70 samples) prior to resection of the primary tumour (n = 57) or disease recurrence (n = 7). DNA was isolated from plasma, quantified, and analyzed for cfDNA. A subset of cases (n = 6) underwent whole exome sequencing to identify tumour-specific alterations used to detect ctDNA using digital droplet polymerase chain reaction (ddPCR). Results Cell-free was present in 69 of 70 samples above 0.5 ng/ml. Improved disease-free survival was found for patients with lower cfDNA levels (90% vs 48% at one-year for ≤ 6 ng/ml and > 6 ng/ml, respectively; p = 0.005). Digital droplet PCR was performed as a pilot study and mutant alleles were detectable at 0.5% to 2.5% of the wild type genome, and at a level of 0.25 ng tumour DNA. Tumour-specific alterations (ctDNA) were found in five of six cases. Conclusion This work demonstrates the feasibility and potential utility of cfDNA and ctDNA as biomarkers for bone and soft-tissue sarcomas, despite the lack of recurrent genomic alterations. A larger study is required to validate these findings. Cite this article: Bone Joint Res 2021;10(9):602–610.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".