Interpreting immune evasion: a novel assay for HLA loss detection
Bibliographic record
Abstract
This study presents the analytical performance of a new Next-Generation Sequencing (NGS) assay designed to detect Human Leukocyte Antigen (HLA) loss. Unlike existing methods, this assay offers increased sensitivity, broader applicability, and does not require prior knowledge of specific HLA mismatches, making it a more versatile tool for post-transplant monitoring. The main goal was to determine whether this assay can reliably identify HLA loss in post-transplant patients and provide clinically actionable information for relapse management. Furthermore, the clinical utility of the assay was assessed in patients undergoing Hematopoietic Stem Cell Transplantation (HSCT) with haploidentical or HLA-mismatched unrelated donors (MMUD). The study included both artificial and clinical samples, which were analyzed using the present assay to examine insertion-deletion (indel) markers located within and adjacent to the HLA region. The results demonstrated that the new assay exhibits excellent correlation with the One Lambda Devyser Chimerism assay in samples without HLA loss, achieving a detection limit of 0.25%. Furthermore, the study showed that the markers employed in the assay can effectively identify the occurrence and location of HLA loss. These findings could potentially influence clinical decision-making, when the donor source of retransplants or Donor Lymphocyte Infusions (DLI) need to be re-considered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".