Development of Reference Materials for Noninvasive Prenatal Aneuploidy Testing by Massively Parallel Sequencing: A Proof-of-Concept Study
Bibliographic record
Abstract
Abstract Background Noninvasive prenatal aneuploidy testing (NIPT) represents the first large-scale clinical application of massively parallel sequencing technology. However, no NIPT reference material (RM) has yet been widely adopted, impeding the development of quality management systems and standardization. Developing an NIPT RM from a biological sample is complicated by the low concentration of cell-free DNA (cfDNA), which implies pooling specimens and frequent resampling. Methods We tested the feasibility of using DNA from immortalized cell lines of a woman and her aneuploid offspring to spike an artificial plasma matrix. Enzymatic fragmentation of extracted DNA was optimized to achieve fragment size profiles with a mode of 150 to 200 bp, similar to biological cfDNA. This synthetic material was compared with routine biological samples from pregnant women by a targeted NIPT assay in a multiplex sequencing run on a Proton platform. Results Sequencing statistics were similar between artificially prepared material and routine biological samples, as well as relative chromosomal representation, and no matrix effects could be detected. Estimate of fetal fraction (FF) was within the range of expected value, and aneuploidy detection statistic (z-score) was also comparable between both types of samples. Conclusions Artificial plasma spiked with DNA from cell lines of mother and offspring is a promising strategy for developing NIPT RM. This type of material would offer the advantage of a constant and stable composition, allowing for greater standardization of NIPT assays. Moreover, it preserves the parental relatedness used by targeted assay to estimate FF by identification of paternal alleles in single-nucleotide polymorphisms or other variable regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".