Allogeneic Transplantation for Myelodysplastic Syndrome in Adults over 50 Years Old Using Reduced Intensity/Non-Myeloablative Conditioning: Haploidentical Relative Versus Matched Unrelated Donor
Notice bibliographique
Résumé
Background: Allogeneic hematopoietic cell transplantation (HCT) has been a successful strategy to treat myelodysplastic syndrome (MDS). With only approximately one-third of patients having an HLA matched sibling, most transplants use mismatched relative (haploidentical) or unrelated donors. In the current analysis we sought to study outcomes after haploidentical related compared to HLA-matched unrelated donor HCT for MDS (de novo or therapy-related). Methods: We retrospectively studied 176 recipients of haploidentical related donor and 427 recipients of 8/8 HLA-matched unrelated donor HCT in the United States between 2012 and 2017. The primary outcome was overall survival. The effect of donor type on survival and other transplant outcomes were studied using a Cox regression model. Results: Patient and disease characteristics are presented in Table 1. Most transplants (85%) were for de novo MDS in both donor groups. Although all patients received reduced intensity regimens, the predominant conditioning regimens were confounded by donor type. Total body irradiation (TBI) 200 cGy/cyclophosphamide/fludarabine (TBI/Cy/Flu; 82%) was the predominant regimen for haploidentical HCT and fludarabine with busulfan or melphalan (Flu/Bu or Flu/Mel; 79%) without in vivo T-cell depletion was the predominant regimen for unrelated donor HCT. Similarly, graft-versus-host disease (GVHD) prophylaxis was also confounded by donor type. Posttransplant cyclophosphamide/calcineurin inhibitor/mycophenolate (PT-Cy/CNI/MMF) was the prophylaxis regimen for all haploidentical transplants. CNI/MMF (31%) or CNI/methotrexate (69%) was used for unrelated donor transplants. Peripheral blood was the predominant graft for both donor types. The median follow-up was 24 months (range 3-77) after haploidentical and 36 months (range 3-74) after unrelated donor HCT. Results of multivariate analysis, adjusted for HCT-CI, prior treatment with hypomethylating agents (HMAs), and IPPS-R did not show differences in survival by donor type (HR 0.98, p=0.85; 40% vs. 37%), Figure 1. However, the relapse rate (adjusted for prior HMAs, IPSS-R, and recipient sex) was higher after haploidentical compared to unrelated donor HCT (HR 1.60, p=0.002, 53% vs. 34%), which led to lower disease-free survival after haploidentical HCT (HR 1.30, p=0.03; 21% vs. 32%), Figure 1. To further test the effect of regimen intensity, low dose TBI regimens were compared to Flu/Bu and Flu/Mel; we did not observe a difference in relapse risk (HR 0.95, p=0.76). Non-relapse mortality did not differ by donor type (HR 0.88, p=0.46). Interval between diagnosis and transplant was also not associated with outcomes. Acute grade II-IV acute GVHD (HR 0.46, p<0.001) and chronic GVHD (HR 0.34, p<0.001) was less common after haploidentical HCT. The 1-year graft failure rate was higher after haploidentical compared to unrelated donor HCT (15% and 8%, respectively, p=0.02). Conclusion: Although the current analysis did not show differences in survival between haploidentical related and matched unrelated donor HCT, the higher relapse and consequently lower disease-free survival associated with the haploidentical HCT approach in this analysis (primarily TBI/Cy/Flu with PT-Cy/CNI/MMF) warrants caution. A more definitive comparison of the two donor types can be accomplished only if more haploidentical transplants were to use Flu/Bu or Flu/Mel conditioning. Figure 1 Disclosures Grunwald: Celgene: Consultancy; Pfizer: Consultancy; Agios: Consultancy; Merck: Consultancy; Abbvie: Consultancy; Medtronic: Equity Ownership; Incyte: Consultancy, Research Funding; Daiichi Sankyo: Consultancy; Amgen: Consultancy; Trovagene: Consultancy; Cardinal Health: Consultancy; Janssen: Research Funding; Genentech/Roche: Research Funding; Novartis: Research Funding; Forma Therapeutics: Research Funding. Bolanos-Meade:Incyte Corporation: Other: DSMB fees. Bredeson:Otsuka: Research Funding. Gupta:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sierra Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Research Funding. Mussetti:Takeda: Honoraria; BMS: Honoraria; Novartis: Honoraria; Italfarmaco: Honoraria. Nakamura:Merck: Membership on an entity's Board of Directors or advisory committees; Celgene: Other: support for an academic seminar in a university in Japan; Alexion: Other: support to a lecture at a Japan Society of Transfusion/Cellular Therapy meeting ; Kirin Kyowa: Other: support for an academic seminar in a university in Japan. Nishihori:Novartis: Research Funding; Karyopharm: Research Funding. Solh:Celgene: Speakers Bureau; Amgen: Speakers Bureau; ADC Therapeutics: Research Funding. Weisdorf:Fate Therapeutics: Consultancy; Pharmacyclics: Consultancy; Incyte: Research Funding.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 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 ».