Abstract IA05: Circulating tumor DNA as a tool for prognostication, translational discovery, and early cancer detection
Bibliographic record
Abstract
Abstract New sequencing technologies have facilitated the detection and profiling of circulating tumor DNA (ctDNA) in the blood of patients with cancer. Recent efforts have demonstrated that ctDNA can be detected in the blood of pediatric patients with solid malignancies, using assays specifically designed for the unique somatic profiles observed in these cancers. Once identified in a blood sample, ctDNA levels appear to correlate with outcome and can be tracked over time to measure response to therapy and detect recurrence. Deep profiling of ctDNA can identify subclonal somatic variants that are not detectable in single-tumor biopsy samples, enabling a broader understanding of tumor heterogeneity and the ability to discover novel patterns of tumor evolution and treatment resistance. Validation of the prognostic and translational value of these assays requires leveraging large prospective studies, and this work is ongoing. One obvious application of this new technology is the potential to detect the presence of cancer in pediatric patients with increased cancer risks, including cancer predisposition syndromes. Collaborative multi-institutional efforts are under way to collect samples and develop new ctDNA assays for early cancer detection. Citation Format: Brian Crompton. Circulating tumor DNA as a tool for prognostication, translational discovery, and early cancer detection [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA05.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".