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Enregistrement W3216633465 · doi:10.1182/blood-2021-151862

Supermobilizers with High CD34 + Cell Collection for Autologous Transplant and Impact on Survival Outcomes in Multiple Myeloma

2021· article· en· W3216633465 sur OpenAlexaff
Eyal Lebel, Katherine Lajkosz, Esther Masih‐Khan, Donna Reece, Suzanne Trudel, Rodger E. Tiedemann, Anca Prica, Vishal Kukreti, Christine I. Chen

Notice bibliographique

RevueBlood · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineMultiple myelomaBortezomibCD34Autologous stem-cell transplantationLenalidomideCyclophosphamideTransplantationInternal medicineVincristineLeukapheresisOncologyUrologyStem cellDexamethasoneSurgeryChemotherapyBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Autologous stem cell transplantation (ASCT) is standard therapy for selected patients with newly diagnosed multiple myeloma (MM). Studies in MM and lymphoma have suggested that ability to mobilize and collect a higher yield of CD34 + cells predicts for improved survival outcomes, perhaps reflecting better bone marrow reserve (Bolwell 2007, Raschle 2011). We aimed to validate this hypothesis by correlating high CD34 + cell collection ("supermobilizers") and survival outcomes in a large myeloma cohort with long follow-up. Methods: We retrospectively reviewed MM patients (pts) who underwent ASCT at our centre 2000-2010, correlating number of CD34 + cells collected with post-transplant progression-free survival (PFS) and overall survival (OS). Stem cells were mobilized using cyclophosphamide 2.5 g/m 2 IV (day 1), G-CSF 10 ug/kg/day SC (starting on day 4), and leukapheresis (day 11), targeting 4x10 6/kg but accepting a minimum of 2x10 6/kg to support a single transplant. Using a cut-off used in previous studies, pts were categorized as "supermobilizers" if ≥8x10 6/kg CD34+ cells were collected. Results: 621 pts were analyzed. Most pts (422/605; 70%) received high dose dexamethasone (HDD) alone or in combination with vincristine and adriamycin (VAD) for pre-transplant induction therapy (pre-dating the novel agent era) with only 18% (110/605) receiving more contemporary bortezomib-based induction (mostly cyclophosphamide, bortezomib and dexamethasone; CyBORD). The median number of CD34 + cells collected for all pts was 13.9x10 6/kg (range 2.1-61.8). The median CD34 + cells re-infused was 6.2x10 6/kg (range 2.1-25), as some cells were reserved for 2 nd ASCT, but median CD34+ cells collected correlated with CD34 + cells infused (Pearson coefficient 0.81, p<0.001). At a median follow-up of 74 months (m), we were surprised to report an inferior PFS of 24.1m for the supermobilizers collecting ≥8x10 6/kg vs 33.7m for the <8 group (p=0.038, Figure 1a), without differences in OS (p=0.612, Figure 1b). No further discrimination in PFS was observed when using a more extreme supermobilizer cut-off of 15x10 6/kg. To further understand the counterintuitive result of shorter PFS with higher mobilization capacity, we explored the continuous relationship between CD34 + cells and PFS, identifying another optimal cut-off of 4.5x10 6/kg. Pts collecting in the mid-range (4.5-8; n=129) achieved the best PFS of 34.5m, significantly improved over 24.1m in the ≥8 group (n=478) and 11.4m in the small group at the extreme lower collection range (n=14; ≤4.5x10 6/kg)(Figure 1c). A similar pattern was seen with OS (Figure 1d). Clinical and laboratory parameters that may impact both collection capacity and survival, such as age, ISS, and kidney dysfunction, were investigated as confounders but were similar between collection groups and did not predict for PFS in multivariable analyses. Treatment variables, however, differed between groups: the lower collection groups more often received bortezomib-based induction (29%, 31% and 14% in the ≤4.5, 4.5-8 and ≥8 groups, respectively, p<0.001) resulting in deeper responses pre-transplant (VGPR 50% in the ≥8 group vs 43% in the 4.5-8 group, p=0.024) (Table 1). Use of maintenance therapy post-ASCT also differed (50%, 40% and 28% in the ≤4.5, 4.5-8 and ≥8 groups, respectively, p=0.006). Discussion: In this large cohort of 621 MM patients, we report that "supermobilizers" who collected ≥8 x 10 6 CD34 + cells/kg exhibit inferior PFS from transplant than those with less robust mobilization. We suspected that this unexpected observation was due to confounding variables, and identified differences in treatment, primarily greater use of bortezomib-based induction and post-transplant maintenance therapy in the lower collection group. This group was able to achieve deeper responses (≥VGPR) even before transplant than the supermobilizer group, leading to improved PFS. Although bortezomib is routinely used as induction therapy pre-transplant currently and is not felt to be stem cell toxic, it may impair mobilization to a lesser degree, leading not to abject failure of collection but lowered capacity to achieve "supermobilizer" status. Although more research is needed to validate this hypothesis, we can at minimum conclude that high stem cell collection does not appear to predict for a long-term survival advantage. Figure 1 Figure 1. Disclosures Reece: Millennium: Research Funding; Sanofi: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Research Funding; GSK: Honoraria; BMS: Honoraria, Research Funding. Trudel: Amgen: Honoraria, Research Funding; BMS/Celgene: Consultancy, Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Genentech: Research Funding; Sanofi: Honoraria; Pfizer: Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Research Funding; Roche: Consultancy. Prica: Astra-Zeneca: Honoraria; Kite Gilead: Honoraria. Chen: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astrazeneca: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy.

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)0,001
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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
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)0,0020,000

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 Opus0,021
Tête enseignante GPT0,283
Écart entre enseignants0,262 · 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

Citations1
Publié2021
Routes d'admission1
Résumé présentoui

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