T-cell gene therapy for perforin deficiency corrects cytotoxicity defects and prevents hemophagocytic lymphohistiocytosis manifestations
Bibliographic record
Abstract
Background Mutations in the perforin 1 (PRF1) gene account for up to 58% of familial hemophagocytic lymphohistiocytosis syndromes. The resulting defects in effector cell cytotoxicity lead to hypercytokinemia and hyperactivation with inflammation in various organs. Objective We sought to determine whether autologous gene-corrected T cells can restore cytotoxic function, reduce disease activity, and prevent hemophagocytic lymphohistiocytosis (HLH) symptoms in in vivo models. Methods We developed a gammaretroviral vector to transduce murine CD8 T cells in the Prf −/− mouse model. To verify functional correction of Prf −/− CD8 T cells in vivo , we used a lymphocytic choriomeningitis virus (LCMV) epitope–transfected murine lung carcinoma cell tumor model. Furthermore, we challenged gene-corrected and uncorrected mice with LCMV. One patient sample was transduced with a PRF1 -encoding lentiviral vector to study restoration of cytotoxicity in human cells. Results We demonstrated efficient engraftment and functional reconstitution of cytotoxicity after intravenous administration of gene-corrected Prf −/− CD8 T cells into Prf −/− mice. In the tumor model infusion of Prf −/− gene–corrected CD8 T cells eliminated the tumor as efficiently as transplantation of wild-type CD8 T cells. Similarly, mice reconstituted with gene-corrected Prf −/− CD8 T cells displayed complete protection from the HLH phenotype after infection with LCMV. Patients' cells showed correction of cytotoxicity in human CD8 T cells after transduction. Conclusion These data demonstrate the potential application of T-cell gene therapy in reconstituting cytotoxic function and protection against HLH in the setting of perforin deficiency.
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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 | 0.001 |
| 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".