Investigating the Use of Circulating Tumor DNA for Sarcoma Management
Bibliographic record
Abstract
Background/Objectives: Sarcomas are a heterogeneous group of cancers, many with high rates of recurrence and metastasis, leading to significant morbidity and mortality. Due to a lack of early diagnostic biomarkers, by the time recurrent disease can be clinically detected, it is often extensive and difficult to treat. Here, we sought to investigate methods of detecting ctDNA in sarcoma patient plasma to potentially monitor disease recurrence, progression, and response to treatment. Methods: Whole-exome sequencing of matched tumor and blood samples revealed patient-specific mutations, which were used to develop personalized assays to detect ctDNA in patient plasma. Since ctDNA is present in extremely low quantities, detection requires highly sensitive methodologies. Droplet digital PCR is highly sensitive; however, it is limited in that it can only be used to target one tumor variant at a time. Therefore, a protocol combining multiplex PCR and targeted amplicon sequencing was developed. Results: ddPCR was successfully able to detect tumor-specific mutations in plasma, confirming the presence of ctDNA in sarcoma patients. Multiplex PCR followed by amplicon sequencing was able to detect multiple tumor variants simultaneously, although it was not as sensitive as ddPCR. Additionally, ctDNA was detected in patient plasma collected at two different time points. Conclusions: This work demonstrates that although there is a lack of recurrent biomarkers, personalized assays detecting ctDNA have the potential to be used to monitor disease progression in sarcoma.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".