Validation of next-generation sequencing-based chimerism testing for accurate detection and monitoring of engraftment in hematopoietic stem cell transplantation
Bibliographic record
Abstract
Allogenic hematopoietic stem cell transplantation (allo-HSCT) is a life-saving treatment for various hematological disorders. The success of allo-HSCT depends on the engraftment of donor cells and the elimination of recipient cells monitored through chimerism testing. We aimed to validate a next-generation sequencing (NGS)-based chimerism assay for engraftment monitoring and to emphasize the importance of including the most prevalent cell subsets in proficiency testing (PT) programs. We evaluated the analytical performance of NGS-based chimerism testing (AlloSeq-HCT and CareDx) with a panel of targeted 202 informative single-nucleotide polymorphisms (SNPs) (i.e., linearity and precision, analytical sensitivity and specificity, system accuracy, and reproducibility). We further compared the performance of our NGS panel with conventional short tandem repeat (STR) analysis in unfractionated whole blood and cell-subset-enriched CD3 and CD66. Our NGS-based chimerism monitoring assay has an impressive detection limit (0.3% host DNA) for minor alleles and analytical specificity (99.9%). Pearson’s correlation between NGS- and STR-based chimerism monitoring showed a linear relationship with a slope of 0.8 and r = 0.973. The concordance of allo-HSCT patients using unfractionated whole blood, CD3, and CD66 was 0.95, 0.96, and 0.54, respectively. Utilization of CD3+ cell subsets for mixed chimerism detection yielded an average of 7.3 ± 7-fold higher donor percentage detection compared to their corresponding unfractionated whole blood samples. The accuracy of the NGS assay achieved a concordance of 98.6% on blinded external quality control STR samples. The reproducibility series showed near 100% concordance with respect to inter-assay, inter-tech, inter-instrument, cell flow kits, and AlloSeq-HCT software versions. Our study provided robust validation of NGS-based chimerism testing for accurate detection and monitoring of engraftment in allo-HSCT patients. By incorporating the cell subsets (CD3 and CD66), the sensitivity and accuracy of engraftment monitoring are significantly improved, making them an essential component of any PT program. Furthermore, the implementation of NGS-based chimerism testing shows potential to streamline high-volume transplant services and improve clinical outcomes by enabling early relapse detection and guiding timely interventions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".