Abstract A70: Aqueous humor is superior to blood as a liquid biopsy for retinoblastoma
Bibliographic record
Abstract
Abstract Purpose: To evaluate whether blood demonstrates similar potential as aqueous humor (AH) to be used as a liquid biopsy for retinoblastoma (RB) and whether tumor-derived cell-free DNA (cfDNA) can be isolated as effectively from the blood as AH. Methods: AH was extracted via clear corneal limbal paracentesis from RB eyes at diagnosis or during intravitreal injection of chemotherapy. Matched peripheral venous blood samples were drawn. Shallow whole-genome sequencing was performed to assess for cell-free tumor DNA fractions and highly recurrent somatic copy number alterations (SCNAs) in the blood and AH samples. Results: Seven samples of AH taken at diagnosis and 13 samples at the time of intravitreal injection were compared to matched blood samples. The presence of any detectable SCNA in the AH was 11/20 and 0/20 in the blood (p=<0.001). The median size distribution of cfDNA molecules in the AH was 157.5 bp versus 181.5 bp in the blood (p=<0.001). Conclusions: The AH appears to be superior to the blood as a source of cell-free tumor DNA for retinoblastoma, thus a better target for development as a liquid biopsy for this cancer. Citation Format: Liya Xu, Jesse L Berry, Ashley Polski, Rima Jubran, Peter Kuhn, Jonathan W. Kim, James Hicks. Aqueous humor is superior to blood as a liquid biopsy for retinoblastoma [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 A70.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".