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Hematopoietic cell transplantation for Wiskott-Aldrich syndrome: a PIDTC report

2025· article· en· W4417028294 on OpenAlexaff
Jessie L. Alexander, Blachy J. Dávila Saldaña, Ruta Brazauskas, Sravya Gethika Dammalapati, Linda M. Griffith, Ami J. Shah, Kristin A. Shimano, Hans D. Ochs, Jack Bleesing, Christen L. Ebens, Malika Kapadia, Andrea Bauchat, Neena Kapoor, Joseph H. Oved, Hesham Eissa, Hannah Lust, Michael D. Keller, Hilary Haines, Shanmuganathan Chandrakasan, Julie‐An Talano, Ahmad Rayes, Lisa Madden, Evan Shereck, Holly Miller, Lisa Forbes Satter, Caridad Martinez, Jacob Rozmus, Jeffrey J. Bednarski, Lolie C. Yu, Deepak Chellapandian, Victor M. Aquino, Alan P. Knutsen, Hey Chong, Ashley Chopek, Alfred P. Gillio, Avni Y. Joshi, Hemalatha G. Rangarajan, Theodore B. Moore, Jeffrey R. Andolina, Kenneth B. DeSantes, Mark T. Vander Lugt, Susan E. Prockop, David C. Shyr, Kathleen E. Sullivan, Suhag Parikh, Katja G. Weinacht, Troy R. Torgerson, Rebecca Marsh, Christopher C. Dvorak, Alice Chan, Élie Haddad, Jennifer Heimall, Michael A. Pulsipher, Jennifer W. Leiding, Donald B. Kohn, Jennifer M. Puck, Luigi D. Notarangelo, David J. Rawlings, Morton J. Cowan, Aleksandra Petrović, Sung‐Yun Pai, Lauri M. Burroughs

Bibliographic record

VenueBlood Advances · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversity of ManitobaCancerCare ManitobaBC Children's Hospital
FundersNational Cancer InstituteDaiichi Sankyo EuropeGrifolsNational Institute of Allergy and Infectious DiseasesOffice of Naval ResearchLes Laboratories Pierre FabreNational Center for Advancing Translational SciencesHealth Resources and Services AdministrationMedacPfizerIncytebluebird bioEli Lilly and CompanyCSL BehringAtara BiotherapeuticsHorizon TherapeuticsMemorial Sloan-Kettering Cancer CenterNHLBI Division of Intramural ResearchSwedish Orphan BiovitrumChildren's Hospital of PhiladelphiaNational Heart, Lung, and Blood InstituteAmgenNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsHematopoietic cellHematopoietic stem cell transplantationTransplantationHematologyHaematopoiesisMyeloidClinical trial

Abstract

fetched live from OpenAlex

ABSTRACT: Wiskott-Aldrich syndrome (WAS), an X-linked disorder characterized by immunodeficiency, thrombocytopenia, autoimmunity, and malignancy, can be effectively treated with allogeneic hematopoietic cell transplantation (HCT). Older age at HCT and mismatched donors are known to affect overall survival (OS). However, the influence of specific clinical manifestations or WAS variant class on OS and factors associated with event-free survival (EFS) remain incompletely defined. We analyzed outcomes of 308 patients with WAS who underwent HCT at 37 institutions of the Primary Immune Deficiency Treatment Consortium from 1990 to 2018. With a median follow-up of 5.3 years, the 5-year OS and EFS were 87.2% and 79.7%, respectively. Age ≥5 years, donor type, and a pre-HCT history of severe infection had a negative impact on OS and EFS, whereas pre-HCT autoimmunity had no impact. Reduced-intensity regimens were associated with lower T-cell and myeloid donor chimerism, particularly when non-busulfan-based regimens were used. Low myeloid donor chimerism was associated with lower platelet counts. Mixed chimerism was not consistently associated with post-HCT autoimmunity. Patients with class I (exon 1-2 missense and intron 5 hot spot variants) and class II variants (all others) had similar pre-HCT clinical symptom severity and no difference in OS, EFS, or platelet recovery post-HCT. In conclusion, our study showed excellent long-term OS and EFS after HCT for WAS, highlighting the importance of early HCT, before the development of severe infections. We confirmed that HCT using busulfan-based conditioning was associated with improved donor chimerism and platelet recovery. This trial was registered at www.clinicaltrials.gov as NCT02064933.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.255
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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