Investigating the use of circulating tumor DNA for cancer surveillance and early detection in (pediatric) sarcomas and Li-Fraumeni Syndrome
Bibliographic record
Abstract
Circulating tumor DNA (ctDNA) is a biomarker that has been shown to be valuable in a variety of contexts in clinical oncology such as determining prognosis, monitoring treatment and predicting recurrence. Early detection using ctDNA may be invaluable for patients with a genetic risk to develop cancers. Li-Fraumeni Syndrome (LFS) is a cancer predisposition syndrome caused by inherited mutations in the tumor suppressor gene, TP53. LFS is characterized by early onset of a wide spectrum of tumors and an 83-fold lifetime risk of multiple cancers. The 'Toronto Protocol' is a multi-modality clinical surveillance protocol that was developed to facilitate early cancer detection in this population. The protocol utilizes a combination of MRI scans, ultrasounds, biochemical tests, and physical examinations. Though it has been shown to be effective in reducing tumor related mortality and treatment related morbidity, reduced sensitivity and specificity make clinical surveillance challenging to implement. To address these challenges, we are using a combination of xenograft and spontaneous tumor forming animal models to study the dynamics of ctDNA in relation to tumor burden and to resolve the feasibility of capturing the development of tumors at their earliest stages. Our work using a rhabdomyosarcoma xenograft model demonstrated that there is a strong relationship between tumor burden and ctDNA concentration in the blood. Furthermore, we detected ctDNA during the development of small, early lesions using simulated metastasis models. In a follow-up study, we used a Trp53R172H/+ pre-clinical model of LFS to assess the use of ctDNA as a surveillance tool for early detection of spontaneously developing tumors. We were able to identify ctDNA in blood samples collected before and at the onset of tumor formation as confirmed by radiological imaging and subsequently used ctDNA to monitor cancer progression. Our studies provide the first evidence demonstrating the capacity of circulating tumor DNA for early cancer detection in pediatric sarcomas, specifically arising from a hereditary cancer predisposition syndrome. Given the spontaneous nature of tumor formation in LFS, the benefits of early cancer detection and subsequent diagnosis are unparalleled in this patient population by accelerating introduction of early treatment intervention and improving disease prognosis. This work illustrates the capacity for ctDNA detection to complement and enhance cancer surveillance protocols in LFS patients. Furthermore, the outcomes of this study will support the validation and implementation of ctDNA analysis for cancer surveillance in the clinic.
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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 | 0.003 |
| 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 | 0.001 | 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".