Gene therapy for primary immune deficiencies: a Canadian perspective
Bibliographic record
Abstract
The use of gene therapy (GT) for the treatment of primary immune deficiencies (PID) including severe combined immune deficiency (SCID) has progressed significantly in the recent years. In particular, long-term studies have shown that adenosine deaminase (ADA) gene delivery into ADA-deficient hematopoietic stem cells that are then transplanted into the patients corrects the abnormal function of the ADA enzyme, which leads to immune reconstitution. In contrast, the outcome was disappointing for patients with X-linked SCID, Wiskott-Aldrich syndrome and chronic granulomatous disease who received GT followed by autologous gene corrected transplantations, as many developed hematological malignancies. The malignancies were attributed to the predilection of the viruses used for gene delivery to integrated at oncogenic areas. The availability of safer and more efficient self-inactivating lentiviruses for gene delivery has reignited the interest in GT for many PID that are now in various stages of pre-clinical studies and clinical trials. Moreover, advances in early diagnosis of PID and gene editing technology coupled with enhanced abilities to generate and manipulate stem cells ex vivo are expected to further contribute to the benefit of GT for PID. Here we review the past, the present and the future of GT for PID, with particular emphasis on the Canadian perspective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".