Evaluation of nanopore sequencing on polar bodies for routine pre-implantation genetic testing for aneuploidy
Bibliographic record
Abstract
Structured Abstract BACKGROUND PGT-A using polar body (PB) biopsy derives a clinical benefit by reducing the number of embryo transfers and miscarriage rates but is currently not cost-efficient. Nanopore sequencing technology opens possibilities by providing cost-efficient, fast sequencing results with uncomplicated sample preparation workflows. METHODS In this comparative experimental study, 102 pooled PB samples from 20 patients were analyzed for aneuploidy using nanopore sequencing technology and compared with aCGH results generated as part of the clinical routine. Samples were sequenced on a Nanopore MinION machine for up to 9 hours for 6 pooled PB samples. Whole-chromosome copy-numbers were called by a custom bioinformatic analysis software. Automatically called results were compared to aCGH results. RESULTS Overall, 96/99 samples were consistently detected as euploid or aneuploid in both methods (concordance=97.0%, sensitivity = 0.957, specificity = 1.0, PPV = 1.0, NPV = 0.906). On chromosomal level, concordance reached 98.7%. Chromosomal aneuploidies analyzed in this trial covered all 23 chromosomes with 98 trisomies, and 97 monosomies in 70 aCGH samples. The whole nanopore workflow is feasible in under 5 hours (for one sample) with maximum time of 16 hours (for 12 samples), enabling fresh PB-euploid embryo transfer. Material cost of 150€/sample possibly enable cost-efficient aneuploidy screening. CONCLUSIONS This is the first study, systematically comparing nanopore sequencing for aneuploidy of PBs with standard detection methods. High concordance rates confirmed feasibility of nanopore technology for this application. Additionally, the fast and cost-efficient workflow reveals clinical utility of this technology, making PB PGT-A clinically attractive.
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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 | 0.000 |
| 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".