Quantitative assessment of copy number alterations by liquid biopsy for neuroblastoma
Bibliographic record
Abstract
Liquid biopsy, a method of detecting genomic alterations using blood specimens, has recently attracted attention as a noninvasive alternative to surgical tissue biopsy. We attempted quantitative analysis to detect amplification of MYCN (MYCNamp) and loss of heterozygosity at 11q (11qLOH), which are clinical requisites as prognostic factors of neuroblastoma (NB). In this study, cell-free DNA (cfDNA) was extracted from plasma samples from 24 NB patients at diagnosis. Copy numbers of MYCN and NAGK genes were quantitatively analyzed by droplet digital PCR (ddPCR). 11qLOH was also assessed by detecting allelic imbalances of heterozygous single nucleotide polymorphisms in the 11q region. The results obtained were compared to those of specimens from tumor tissues. The correlation coefficient of MYCN copy number of cfDNA and tumor DNA was 0.88 (p < 0.00001). 11qLOH was also accurately detected from cfDNA, except for one case with localized NB. Given the high accuracy of liquid biopsy, to investigate components of cfDNA, the proportion of tumor-derived DNA was estimated by examining the variant allele frequency of tumor-specific mutations in cfDNA. The proportion of tumor-derived DNA in cfDNA was 42.5% (range, 16.9%-55.9%), suggesting sufficient sensitivity of liquid biopsy for NB. In conclusion, MYCN copy number and 11qLOH could be quantitatively analyzed in plasma cfDNA by ddPCR assay. These results suggest that plasma cfDNA can be substituted for tumor DNA and can also be applied for comprehensive genomic profiling analysis.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".