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Enregistrement W2979865431 · doi:10.1182/blood-2018-99-117705

Lenalidomide Maintenance Does Not Negatively Impact Overall and Progression Free Survival Using Lenalidomide-Based Regimens for Multiple Myeloma in First Relapse

2018· article· en· W2979865431 sur OpenAlexaff
Hannah Cherniawsky, Zack M. Breckenridge, Irwindeep Sandhu, Michael P. Chu, Joanne D Hewitt, Ismail Ismail, Andrew R. Belch, Linda M. Pilarski, Tatiana Nikitina, Christopher P. Venner

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésLenalidomideMedicineMultiple myelomaMaintenance therapyInternal medicineOncologyBortezomibClinical endpointAutologous stem-cell transplantationProgression-free survivalSurgeryClinical trialChemotherapy

Résumé

récupéré en direct d'OpenAlex

Abstract BACKGROUND Outcomes in multiple myeloma have improved dramatically over the last decade however, optimal sequencing of therapy remains unknown. Specifically, in an era where post-transplant lenalidomide (L) maintenance is now as established standard of care, questions remain around the utility of full dose L-based regimens in second line therapy. In this series, we sought to evaluate the impact of different regimens used at first relapse in patients who received autologous stem cell transplant (ASCT) in the frontline setting treated with and without lenalidomide maintenance (LM). We focused on the impact of L-based therapies in patients relapsing on LM. METHODS Using our prospectively maintained institutional MM database we retrospectively analyzed patients treated at the Cross Cancer Institute from January, 2005 to January, 2016 to ensure 2 years of follow-up for surviving patients. 4 categories were identified based on 2 variables: receipt of LM following 1st line therapy (yes or no) and receipt of L-based 2nd line therapy (yes or no). The primary endpoint was 2nd PFS defined as time of initiation of second line therapy to relapse, death or last follow-up. OS was defined as time of initiation of first line induction therapy to death or last follow-up. Second OS was defined as time of initiation of second line therapy to death or last follow-up. Survival statistics were determined using the Kaplan-Meier method with SPSS software. A p - value of <0.05 was considered significant. RESULTS 213 patients received standard bortezomib-based induction and ASCT of which 132 (62%) received LM. Median follow up for the LM patients was 48 months compared to 74.6 months in non-LM patients. 103 patients (48%) required treatment with second line therapy. Forty-four percent patients were treated with LM while 56% were not. Sixty-nine percent received L-based therapy at relapse, 21% received PI-based therapy and 8% were treated with a PI-IMID combination (table 1). Focusing on the cohort of relapsed patients who received LM (n=44), the median 2nd PFS was 9.3 months in those that received L-based second line therapy vs 4.1 months in those that did not (p = 0.28, figure 1b]. In patients who did not receive LM (n = 55) the median 2nd PFS was 14.0 months in those who received L-based second line therapy vs 6.9 months in those who did not (p = 0.19, figure 1a. Examining all patients who received L-based therapy at relapse there was no difference in 2nd PFS based on whether LM was given (p = 0.42). The median 2nd OS was not statistically significant between the groups (p = 0.39, figure 1b. Patients on LM had a median 2nd OS of 34 months with L-based therapy at relapse compared to 39.2 months without. The median 2nd OS in non-LM patients was 34.5 months in those receiving L-based therapy at first relapse and 23.4 months in those that did not (p=0.10). There was no statistically significant differences in median OS between the 4 groups (p = 0.83). For patients who received LM the median OS was not reached in those receiving L-based therapies at relapse and was 78.1 months in patients who did not. In patients who did not receive LM the median OS was 78.0 months in those receiving L-based therapies at relapse and 69.3 months in those who did not. CONCLUSION Our data suggests that receiving LM does not negatively impact survival outcomes after receiving full dose L-based therapy at relapse. Both median 2nd PFS and 2nd OS were similar with L-based therapies regardless of prior LM. While the 2nd PFS at relapse does fall short of recently published trials in relapsed MM there are some notable confounders here. Firstly, this real-world data includes frailer patients with potentially greater co-morbidities possibly influencing choice and duration of therapy as well as reflect more aggressive disease biology. Secondly, given the relatively short median follow-up of the relapsed LM patients to date, the cohort may be enriched with "early" relapsers (< 2-years) also potentially indicative of biologically more aggressive disease. As such, this may underestimate the true impact of L-based therapies in patients relapsing on LM. Larger series with longer follow-up are necessary to formally examine whether multi-agent L-based regimens confer additional benefit over L-Dexamethasone or non-L based regimens. Real world registries will be useful as prospective trials are unlikely to be done. Disclosures Sandhu: Novartis: Honoraria; Bioverativ: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Venner:Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: 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)0,000
score de la tête « metaresearch » (Gemma)0,001
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
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
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 Opus0,040
Tête enseignante GPT0,336
Écart entre enseignants0,296 · 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
Publié2018
Routes d'admission1
Résumé présentoui

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