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Enregistrement W2900337270 · doi:10.1182/blood-2018-99-118857

Depletion of Alloreactive T Cells after Haploidentical HSCT: Comparison of Outcomes for Ex Vivo Versus In Vivo Treatment Strategies

2018· article· en· W2900337270 sur OpenAlexaffabout
Steven M. Devine, Stephan Mielke, Eduardo Olavarría, Bert Tuk, Kees Meewisse, Andrew Sandler, Denis‐Claude Roy

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueHematopoietic Stem Cell Transplantation
Établissements canadiensHôpital Maisonneuve-Rosemont
Organismes subventionnairesnon disponible
Mots-clésMedicineEx vivoIn vivoHematopoietic stem cell transplantationT cellGraft-versus-host diseaseCyclophosphamideStem cellImmune systemTransplantationClinical trialImmunologyInternal medicineBiologyChemotherapy

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The use of haploidentical allogeneic hematopoietic stem cell transplantation (haplo-HSCT) has increased owing to therapeutic advances that have mitigated the main barriers such as high incidence of graft-versus-host disease (GVHD) and non-relapse mortality (NRM). This is primarily attributable to the elimination of alloreactive T cells in either the patient or the graft. Such T-cell depletion can be performed in vivo early after T-cell-replete haplo-HSCT using post-transplant cyclophosphamide (PTCy). Alternatively, T-cell-depleted haplo-HSCT can be supplemented with T-lymphocytes that are depleted ex vivo of their alloreactive component in the form of ATIR101 (Kiadis Pharma). Patient exposure to PTCy to eliminate donor alloreactive cells can be performed easily and at low cost but may cause patient toxicity and increase relapse rates, and it requires post-transplant immune suppression. Although ATIR101 requires cell manufacturing and is more expensive, it limits toxicity to the patient, enables haplo-HSCT without the use of immunosuppressants, and may reduce relapse rates. Both strategies are promising, but no attempt has yet been made to compare clinical results in similar patient populations to delineate key features of alloreactive T-cell depletion performed either ex vivo or in vivo. Methods: Data from published retrospective studies (single-site or registry data) were analyzed to assess clinical outcomes of haplo-HSCT plus PTCy. The 1-year outcomes from these studies were compared with results from a pooled analysis of 2 phase II clinical trials of a single dose of ATIR101 (N=37, all patients with AML/MDS/ALL [CR-AIR-007, CR-AIR-008]). Studies in which PTCy was used in patient populations with >50% AML/MDS/ALL were identified (Ciurea 2015, Piemontese 2017, Solomon 2012, Ciurea 2012, Devillier 2015, Di Stasi 2014, Esquirol 2016, Sugita 2015). The 1-year rates of relapse, relapse-related mortality (RRM), NRM, GVHD, and overall survival (OS) for the ATIR101 clinical trials were compared with the weighted average of these outcomes for the identified studies. OS is known to correlate with disease risk index (DRI; Armand 2014); therefore, publications reporting both OS and DRI (McCurdy 2017, Ciurea 2015, Devillier 2015, Sugita 2015) were identified to compare OS. Differences in DRI between PTCy and ATIR101 study populations were adjusted according to the relationship between DRI and OS. Finally, PTCy studies reporting GVHD-free and relapse-free survival (GRFS) were identified (Solh 2016, McCurdy 2017, Santoro 2017). There is a clinically relevant and statistically significant correlation between GRFS and DRI, so 1-year GRFS rates from the 2 studies reporting DRI status (Solh 2016, McCurdy 2017) were also normalized according to the DRI profile in the ATIR101 clinical trials to allow comparison. Results: The weighted average of PTCy (N=571) outcomes in populations with >50% AML/MDS/ALL vs ATIR101 patient outcomes were: 29% vs 8% for relapse; 18% vs 8% for RRM; 22% vs 33% for NRM; 5% vs 5% for acute GVHD grade III/IV; 24% vs 3% for chronic GVHD; and 60% vs 58% for OS. The OS in DRI-adjusted studies for PTCy (N=561) was similar to that in ATIR101 clinical trials (63% vs 58%, respectively). The GRFS-reporting studies included a total of 708 patients (Sohl 2016, N=128; McCurdy 2017, N=372; Santoro 2017, N=208); 1-year GRFS rates for PTCy in these studies were 33% (95% CI: 25-41), 45% (95% CI: 40-50), and 33% (average), respectively. In the 2 studies reporting DRI (N=500), the DRI profile was more favorable than in the ATIR101 studies and the 1-year GRFS rates normalized in line with the ATIR101 studies were reduced to 30% (Sohl 2016) and 40% (McCurdy 2017). In patients intended to receive a single dose of ATIR101 after haplo-HSCT, Kaplan-Meier estimate of 1-year GRFS was 53% (95% CI 39-72) (Table 1). Conclusion: This is not a head-to-head comparison, so data should be interpreted with caution. However, in these cross-study analyses, first insights into a potential advantage of ex vivo (ATIR101) over in vivo (PTCy) depletion of alloreactive T cells is suggested, including but not limited to rates of relapse, chronic GVHD, and GRFS. A large, phase III, randomized control trial is thus underway to assess the relative safety and efficacy of ATIR101 after T-cell-depleted haplo-HSCT versus PTCy after T-cell-replete haplo-HSCT (CR-AIR-009 HATCY; NCT02999854). Disclosures Devine: Kiadis Pharma: Consultancy. Mielke:Kiadis Pharma: Other: Travel grants, Research Funding. Tuk:Kiadis Pharma: Consultancy. Meewisse:Kiadis Pharma: Employment. Sandler:Kiadis Pharma: Employment. Roy:University of Montreal: Patents & Royalties: Author on patent; Kiadis Pharma: Other: Travel support; Hopital Maisonneuve Rosemont: Patents & Royalties: Author on patent.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,036
Tête enseignante GPT0,335
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission2
Résumé présentoui

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