New considerations in hematopoietic stem cell transplantation for severe combined immunodeficiency: how did newborn screening change our field, and can we finally brake the glass ceiling for haploidentical transplantation?
Bibliographic record
Abstract
Pioneered in 1968, hematopoietic stem cell transplantation (HSCT) first cured a patient with severe combined immune deficiency (SCID) transplanted from a matched sibling, bringing hope for this previously fatal disease. Since then, HSCT has become the standard of care treatment for SCID with thousands of patients transplanted successfully worldwide. Initially successful mainly in patients with a matched sibling donor and in specific easier to transplant types of SCID, nowadays, most patients with SCID undergo successful transplantation due to HSCT technique advances. These include refined human leukocyte antigen (HLA)-tissue typing, use of alternative donors, availability of new stem cell sources such as umbilical cord blood, less toxic chemotherapeutic conditioning, as well as improved graft-versus-host disease (GvHD) prophylaxis. Other factors contributing to the success of transplantation include the improvement of supportive care by molecular detection of viral infections, enabling preemptive antiviral treatment before organ damage occurs. Increased awareness for primary immunodeficiency disorders (PID), leading to earlier diagnosis and referral to specialist centers, has been another important factor in successfully transplanting SCID patients. A major game changer in the last decade has been the implementation of neonatal screening for SCID. This increased early diagnosis, allowing for this disease to be almost universally diagnosed soon after birth in countries which included this test in their newborn screening program. As a result, early and optimal transplant timing and conditions could be achieved. However, very early diagnosis also raised new questions regarding SCID patients with a “leaky” phenotype, as well as dilemmas regarding transplant and conditioning regimens in very young infants. With improved diagnosis and treatment options, overall survival has increased to over 90% for SCID babies with a genoidentical donor and similar results are emerging for matched unrelated donor HSCT. Due to new advances, we hope to achieve similar results for those given HSCT from haploidentical donors as well. This review will focus on the new considerations in HSCT seen in recent years, and examines the effect they have had on treatment options and outcomes for SCID patients. Statement of novelty: The field of HSCT has advanced considerably since the first successful SCID bone marrow transplant in 1968. However, success rates have been limited due to delayed diagnosis and poor outcome of patients for which a HLA-matched donor could not be found. This review will discuss recent advances occurring in the last decade in HSCT for SCID, and our hopes to bring cure to this once fatal disease.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".