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Enregistrement W2988589642 · doi:10.1182/blood-2019-124191

Expanded Meta-Analyses Confirms the Association between MRD and Long-Term Survival Outcomes in Multiple Myeloma (MM)

2019· article· en· W2988589642 sur OpenAlexaff
Nikhil C. Munshi, Hervé Avet‐Loiseau, Kenneth C. Anderson, Paola Neri, Bruno Paiva, Mehmet Samur, Meletios Α. Dimopoulos, Margarita Kulakova, Bart Heeg, Mahmoud Hashim, Jon Ukropec, Kevin Liu, Maria Krevvata, Annette Lam, Sarah Côté, Nizar J. Bahlis

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensInstitute of Cancer ResearchUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineOncologyHazard ratioProgression-free survivalMeta-analysisMultiple myelomaTransplantationSurrogate endpointOverall survivalConfidence interval

Résumé

récupéré en direct d'OpenAlex

Introduction: Achievement of MRD negativity in MM is associated with prolonged progression-free survival (PFS) and is being investigated as a potential surrogate for established clinical endpoints, such as PFS and overall survival (OS). Here, we evaluated the predictive utility of MRD in patients (pts) with MM for PFS and OS using a systematic literature review (SLR) and meta-analysis, and investigated how hazard ratios (HR) for PFS and OS, stratified by MRD status, changed for various pt subgroups. Methods: A SLR was conducted to identify all studies in MM reporting survival outcomes by MRD status (through 8 June 2019). In these studies, MRD was assessed by various assays (multiparametric flow cytometry [MFC], next generation sequencing [NGS], and polymerase chain reaction [PCR]), sensitivity thresholds (10-4, 10-5, and 10-6), and disease settings (relapsed/refractory MM [RRMM], transplant-eligible [TE] and transplant-ineligible [TIE] newly diagnosed MM [NDMM]). Studies with allogeneic transplant, where MRD was measured in peripheral blood or using PET-CT, or from which PFS and OS data could not be extracted were excluded from the analysis. To obtain a pooled effect estimate of MRD status on PFS and OS HRs, a meta-analysis was performed. Subgroup analyses were performed to adjust for variables expected to impact the association of MRD and PFS/OS outcomes. Variables were selected based on available qualitative evidence from studies. Statistical analyses were performed using the 'metafor' R package for meta-analyses. Results: 143 publications met the inclusion criteria; 86 publications were included in the meta-analysis based on data availability (65 PFS and 28 OS HRs). Outcomes for PFS (N = 8590) and OS (N = 3392) were significantly improved for MRD-negative pts: PFS HR 0.35 (95% confidence interval [CI], 0.31-0.39) and OS HR 0.48 (95% CI, 0.41-0.55; P <0.001 for both). This benefit was also observed in pts achieving ≥complete response, with HRs for MRD negativity being 0.44 (95% CI, 0.35-0.54) for PFS and 0.45 (95% CI, 0.28-0.71) for OS (both P <0.001), consistent with results of a previous analysis (Munshi NC, et al. 2017. JAMA Oncol). When analyzed by MRD sensitivity threshold, HRs for PFS and OS were in favor of MRD-negativity in all subgroups. Outcomes for PFS and OS improved with increasingly stringent sensitivity thresholds. HRs for PFS were 0.36 (95% CI, 0.31-0.42) at 10-4; 0.35 (95% CI, 0.30-0.41) at 10-5, and 0.26 (95% CI, 0.17-0.39) at 10-6 (all P <0.001). For OS, HRs were 0.49 (95% CI, 0.42-0.57) at 10-4 and 0.47 (95% CI, 0.34-0.65) at 10-5 (both P <0.001); analysis of OS at a 10-6 sensitivity threshold was not possible due to limited data availability. MRD analyzed by MFC showed the least benefit, with PFS HRs of 0.39 (95% CI, 0.34-0.44), 0.27 (95% CI, 0.20-0.37), and 0.26 (95% CI, 0.19-0.36) by MFC, NGS, and PCR, respectively. This trend was less pronounced for OS, likely due to reduced data availability. It is possible that older studies reporting MFC used a lower MRD sensitivity threshold, resulting in less favorable HRs. When analyzed by disease setting, MRD negativity provided superior PFS for both TE NDMM (HR, 0.39 [95% CI, 0.32-0.46]) and TIE NDMM (HR, 0.35 [95% CI, 0.29-0.42]). Consistent with PFS, the effect of MRD-negativity on OS was less pronounced in TE NDMM (HR, 0.53 [95% CI, 0.45-0.63]) than TIE NDMM (HR, 0.40 [95% CI, 0.30-0.54]). As RRMM is an aggressive disease state, achieving MRD-negativity is important for long-term outcomes; notably, our cohort of RRRM pts had a PFS HR of 0.30 (95% CI, 0.18-0.49). Benefit for achieving MRD-negativity was confirmed for PFS in pts with high-risk cytogenetics (HR, 0.44 [95% CI, 0.35-0.57]) and standard-risk cytogenetics (HR, 0.46 [95% CI, 0.32-0.66]). A similar improvement was observed in OS for high-risk (HR, 0.66 [95% CI, 0.46-0.94]) and standard-risk cytogenetic pts (HR, 0.64 [95% CI, 0.54-0.75]). Additional analyses with survival data will be presented. Conclusions: This meta-analysis, involving a large pt cohort, confirms that MRD negativity has a positive effect on both PFS and OS in both TE and TIE NDMM. Outcomes for MRD-negative pts improve with an increase in MRD assay sensitivity. Pts with RRMM and with high cytogenetic risk also have favorable outcomes for PFS and OS with MRD negativity. Disclosures Munshi: Takeda: Consultancy; Abbvie: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Adaptive: Consultancy; Oncopep: Consultancy. Avet-Loiseau:celgene: Consultancy, Other: travel fees, lecture fees, Research Funding; takeda: Consultancy, Other: travel fees, lecture fees, Research Funding. Anderson:Janssen: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Other: Scientific Founder; Oncopep: Other: Scientific Founder; Amgen: Consultancy, Speakers Bureau; Sanofi-Aventis: Other: Advisory Board. Neri:Celgene, Janssen: Consultancy, Honoraria, Research Funding. Paiva:Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Dimopoulos:Sanofi Oncology: Research Funding. Kulakova:Ingress-health: Employment. Heeg:Ingress-Health: Employment. Hashim:Ingress-Health: Employment. Ukropec:Janssen: Employment, Equity Ownership. Liu:Janssen: Employment, Equity Ownership. Krevvata:Janssen: Employment. Lam:Janssen: Employment, Equity Ownership. Cote:Janssen: Employment, Equity Ownership. Bahlis:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

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)0,019
score de la tête « metaresearch » (Gemma)0,038
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,101

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,038
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0080,035
Bibliométrie0,0040,005
Études des sciences et des technologies0,0000,001
Communication savante0,0030,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,136
Tête enseignante GPT0,377
Écart entre enseignants0,242 · 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'étudeMéta-analyse
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

Citations6
Publié2019
Routes d'admission1
Résumé présentoui

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