Abstract 2741: Assessing the utility of circulating tumor DNA as a surveillance tool for sarcomas and Li-Fraumeni syndrome using a pre-clinical model
Bibliographic record
Abstract
Abstract Li-Fraumeni Syndrome (LFS) is a hereditary cancer predisposition syndrome commonly characterized by the presence of inherited mutations in the tumor suppressor gene TP53. This leads to an early onset of a wide spectrum of tumors in multiple organ systems. Current surveillance protocols for early tumor detection include biochemical screening, MRI and ultrasound scans, colonoscopy and mammography (for adults). While early detection is associated with improved survival, the complexity of testing, potential to ‘misdiagnose’ tumors (false positive/negative) and requirement for multiple imaging modalities makes clinical surveillance challenging to implement and interpret. Liquid biopsies are a recently described diagnostic and prognostic tool that takes advantage of analyzing circulating tumor DNA - fragmented genomic material that are released into the blood from dying tumor cells. In this proof-of-principle study, we use mouse xenograft tumor models to assess the dynamic relationship between tumor burden and ctDNA concentrations, as well as resolve the sensitivity of capturing the presence of lesions in their earliest stages of growth. Longitudinal analyses of ctDNA from serially collected blood were performed on mice with xenografts of rhabdomyosarcoma (Rh4 and Rh30), osteosarcoma (HOS) and non-small cell lung carcinoma (H1975) of both localized and simulated metastatic tumors. We characterized ctDNA using droplet digital PCR (ddPCR) for known gene mutations that were specific for the cancer cell lines used. We observe obvious increases in ctDNA with increased tumor burden and a complete clearance of ctDNA after tumor resection. In addition, using the metastatic model, we have conducted synchronized imaging and blood-based biopsies to determine the smallest lesion able to be detected in the blood by ctDNA. These studies provide the foundation for early tumor detection with ctDNA in Trp53 mutant mice that develop spontaneous tumors analogous to LFS. This model will help further our understanding on the utility of ctDNA as a surveillance and diagnostic tool for LFS as well as assess its potential for clinical use. Citation Format: Sangeetha Paramathas, Nathan Lewis, Tanya Guha, Zainab Motala, David Malkin. Assessing the utility of circulating tumor DNA as a surveillance tool for sarcomas and Li-Fraumeni syndrome using a pre-clinical model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2741. doi:10.1158/1538-7445.AM2017-2741
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".