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Record W4389248758 · doi:10.1182/blood-2023-182171

Multiparametric Flow Cytometry Based Quantification of the Marrow Hematopoietic Stem and Progenitor Compartment in Post Allogeneic Stem Cell Transplant Recipients with Poor Graft Function to Evaluate Hematopoietic Stem Cell Quality and Predict Clinical Outcomes: A Pilot Study

2023· article· en· W4389248758 on OpenAlexaff
Niranjan Khaire, Murtaza S. Nagree, Mason Boulanger, Carol Chen, John E. Dick, Jonas Mattsson, Stephanie Z. Xie

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCytopeniaStem cellCD34TransplantationHaematopoiesisMedicineHematopoietic stem cell transplantationProgenitor cellCord bloodBone marrowHematopoietic stem cellInternal medicineImmunologyBiology

Abstract

fetched live from OpenAlex

Poor graft function (PGF) is a poorly defined, commonly occurring (estimated 5 to 27%) 1 and difficult to treat complication of allogeneic stem cell transplantation (Allo-HSCT). Thrombopoietin agonists such as eltrombopag (EPAG) or CD34-selected cell infusions are treatments offered for persistent cytopenias. However, with limited knowledge of pathophysiology, there is a lack of reliable prognostic biomarkers. We aimed to investigate if assessment of the BM hematopoietic stem and progenitor cell (HSPC) landscape by multiparametric flow cytometry (MP-FCM) could help evaluate stem cell quality and predict clinical outcomes in PGF patients. We screened the UHN biobank repository to identify banked BM samples of patients who underwent diagnostic BM aspirate for PGF post allo-HSCT. Patients who had progressive cytopenia in one or more lineages after achieving engraftment while having full donor chimerism were included. Relapse as a cause of cytopenia was excluded. Controls in 1:1 ratio were Allo-SCT recipients with stable counts with banked day 60 BM aspirate. The blinded BM samples were analyzed with a 14-parameter FCM adapted from panels previously utilized to assess HSPC and mature lineages in cord blood xenograft studies (2). We selected 12 each of PGF and controls. The mean age of all recipients was 50 years and donors was 30.5 years. 23/24 cases had AlloSCT for hematological malignancies with 15 having AML or MDS. There were 14 unrelated (9 matched, 5 mismatched) and 10 related (5 matched sibling, 5 haploidentical) donors. Mean cell dose of infused graft was 6.66(± 1.98)x 10E6/kg CD34 cells. The significant difference between PGF and controls were recipient age (median 57y vs 28y), allo-SCT number (3 vs 0 with 2 nd transplant), intensity of conditioning regimen (myeloablative in 16% PGF vs 83% controls), and incidence of ≥ grade 3 acute graft vs host disease (GVHD) (n=3 vs 0). Most common causes of PGF were GVHD (n=6), infections (n=4) and drug effects (n=5). 11 PGF patients were treated with EPAG with 5 patients (A3, B2, B3, B5, C4) showing complete response, 3 (A6, B6, C5) showing partial response, and 3 (A2, A4, C9) showing no response. Patient C1 showed complete recovery without the use of EPAG, while C9, who initially did not respond to EPAG, showed a slow spontaneous recovery almost a year later. Amongst non-responders A2 and A4 received CD34 boost; with A4 showing a good response to the second boost. Analyzing the HSPC population by MP-FCM, we found that the PGF vs control groups show significant differences in the percentage of CD34+CD19+ B-Lymphoid precursors (B-LP) and CD19+ B lymphocytes. The mean ± SD percentage of B Lymphocytes and B-LPs in the PGF cohort were 6.76% ± 0.096% and 0.39% ± 0.005% respectively, while in the control group they were 20.09% ± 0.136% and 1.17% ± 0.012% respectively. When the 50 th centile value of the B-LPs (0.375%) or CD19+ B cells (10.89%) was investigated as a cut off to predict the clinical syndrome, this strategy achieved a 75% success rate with 3 PGF (blue bars) or control (black bars) samples misclassified (Fig1). Next, we investigated if these HSPC fractions could help predict the clinical outcome of PGF cases. The cases A2 and A4 who demonstrated no count recovery and needed CD34 boost lie in the lower quartile of both B Lymphocytes as well as B Lymphoid precursors content (Fig 1 red arrows). Conversely the three PGF cases in the upper 50 centile of these cell populations (patient B3, C4 and C9) all showed either a spontaneous or EPAG induced recovery of counts. We further investigated if the proportion of immunophenotypic CD34+CD38-CD45RA-CD90+CD49f+ long-term HSC (LT-HSC) could provide additional prognostic information (Fig2). Again, patients A2 and A4 had LT-HSC in the lower 50 th centile. Patient C1 who showed a spontaneous recovery of counts without the need of EPAG had the highest proportion of LT-HSCs despite lying in the second quartile of the cohort for both B cells as well as B-LPs. In conclusion, MP-FCM based analysis of the HSPC landscape in PGF marrow could be a robust prognostic biomarker in Allo-SCT patients. References 1. Prabahran A, Koldej R, et al. Clinical features, pathophysiology, and therapy of poor graft function post-allogeneic stem cell transplantation. Blood Adv. 2022 Mar 22;6(6):1947-1959. 2. Kaufmann KB, Zeng AGX, et al. A latent subset of human hematopoietic stem cells resists regenerative stress to preserve stemness. Nat Immunol. 2021;22(6):723-734.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.336
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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