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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».