Aberrant T-cell exhaustion in severe combined immunodeficiency survivors with poor T-cell reconstitution after transplantation
Bibliographic record
Abstract
Background Severe combined immunodeficiency (SCID) comprises rare inherited disorders of immunity that require definitive treatment through hematopoietic cell transplantation (HCT) or gene therapy for survival. Despite successes of allogeneic HCT, many SCID patients experience incomplete immune reconstitution, persistent T-cell lymphopenia, and poor long-term outcomes. Objective We hypothesized that CD4 + T-cell lymphopenia could be associated with a state of T-cell exhaustion in previously transplanted SCID patients. Methods We analyzed markers of exhaustion in blood samples from 61 SCID patients at a median of 10.4 years after HCT. Results Compared to post-HCT SCID patients with normal CD4 + T-cell counts, those with poor T-cell reconstitution showed lower frequency of naive CD45RA + /CCR7 + T cells, recent thymic emigrants, and TCR excision circles. They also had a restricted TCR repertoire, increased expression of inhibitory receptors (PD-1, 2B4, CD160, BTLA, CTLA-4), and increased activation markers (HLA-DR, perforin) on their total and naive CD8 + T cells, suggesting T-cell exhaustion and aberrant activation, respectively. The exhaustion score of CD8 + T cells was inversely correlated with CD4 + T-cell count, recent thymic emigrants, TCR excision circles, and TCR diversity. Exhaustion scores were higher among recipients of unconditioned HCT, especially when further in time from HCT. Patients with fewer CD4 + T cells showed a transcriptional signature of exhaustion. Conclusions Recipients of unconditioned HCT for SCID may develop late post-HCT T-cell exhaustion as a result of diminished production of T-lineage cells. Elevated expression of inhibitory receptors on their T cells may be a biomarker of poor long-term T-cell reconstitution.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".