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Enregistrement W2983305723 · doi:10.1182/blood-2019-125810

Treatment of Myeloma Patients Progressing on Lenalidomide-Based Maintenance after ASCT Performed As Part of 1st, 2nd or 3rd Line Therapy: Real-World Results from the National Myeloma Canada Research Network (MCRN) Database

2019· article· en· W2983305723 sur OpenAlexaffabout
Donna Reece, Esther Masih‐Khan, Eshetu G. Atenafu, Víctor H. Jiménez‐Zepeda, Arleigh McCurdy, Kevin Song, Richard LeBlanc, Michaël Sébag, Darrell White, Hannah Cherniawsky, Tony Reiman, Julie Stakiw, Martha Louzada, Rami Kotb, Muhammad Aslam, Engin Gul, Christopher P. Venner

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensLondon Health Sciences CentreSaint John Regional HospitalUniversity of AlbertaQueen Elizabeth II Health Sciences CentreDalhousie UniversityUniversity of OttawaMcGill University Health CentreHôpital Maisonneuve-RosemontUniversity of New BrunswickLeukemia & Lymphoma Society of CanadaUniversity of British ColumbiaUniversity of CalgaryPrincess Margaret Cancer CentreCancerCare ManitobaOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésLenalidomideMultiple myelomaMedicineMaintenance therapyOncologyThalidomidePomalidomideInternal medicineChemotherapy

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Phase 3 trials and a meta-analysis have demonstrated that Lenalidomide (Len) administration as maintenance improves the PFS and OS after ASCT performed as part of 1st line therapy. In addition, retrospective and registry studies have suggested efficacy when post-ASCT maintenance is administered after salvage ASCT in relapsed patients. Len maintenance has become the standard approach in many centres, including in Canada where it is routinely funded after 1st line ASCT and Len-base regimens are often used as maintenance after salvage ASCT in pts who have not progressed on this agent previously. The optimal management of patients (pts) progressing on this highly effective agent is less clear, however. We utilized the MCRN Database to investigate which regimens were utilized after pts progressed on Len-based therapy (post-Len Rx) and the outcomes observed with such regimens in the real-world Canadian setting. We included pts receiving Len-based regimens as maintenance following ASCT performed as part of 1st, 2nd or 3rd line therapy in this analysis, as ASCT pts are relatively young and generally tolerate combination regimens for relapsed myeloma well. METHODS: The MCRN Database is an ongoing national Canadian web-based repository of detailed information on over 6000 myeloma patients from 13 academic centres dating back to 2007. In this analysis, we evaluated the characteristics of pts progressing on post-ASCT Len-based maintenance following a transplant performed as part of the first 3 regimens administered. Pts were treated between 01/2007 until 04/2019; 305 pts had undergone 1 ASCT, 59 pts had 2 ASCTs. The next -line therapy administered at this time (post-Len Rx) was classified based on the novel agent utilized. Overall response rates (ORR), PFS from start of post-len Rx to next progression and OS (calculated from post-Len Rx to death) was calculated for these pts. Categorical comparisons were performed using the chi-square test and continuous ones using ANOVA. Survival rates were calculated using the Kaplan-Meier product-limit method; comparisons among regimens were made using the log-rank statistic. RESULTS: We identified 364 pts who had progressed on Len-based regimens given for maintenance, after ASCT, including 306 after 1st line ASCT, 51 after 2nd line ASCT and 7 after 3rd line ASCT. Len-based maintenance included Len alone in 318, Len + dex in 24 and other Len-combinations in 22. Median age was 59 yrs, 58% were male, 43.6% of 243 evaluable pts had high-risk FISH cytogenetics (del 17p, t[4;14], t[14;16]). Median creatinine was 85 µmol/L (32-832), LDH 187 U/L (55-906) and B2M 295 nmol/L (1.8-3805). Post-Len Rx was based on bortezomib (BTZ) in 56 (15%), carfilzomib (CFZ) in 44 (12%), daratumumab (Dara) in 45 (12%), pomalidomide (POM) in 28 (8%), ixazomib (IXA) in 23 (6%), and other regimens in 27 (7%). Thirty-seven (10%) did not receive any post-Len therapy while 104 (28%) received further Len-based therapy with the addition of Dex alone in 73, or with an additional agent (BTZ, cyclophosphamide [Cy] and/or clarithromycin) in 31 (Table 1). The only significant difference in pt characteristics was a higher LDH value in the IXA and POM groups (p=0.0015). The median PFS of 327 patients who received treatment for progression on Len-based maintenance was 11 months (mos) (95% CI 8.1-13.9); OS was 39 mos (95% CI 31-60) from post-Len Rx. For the 92 (42%) pts with high-risk FISH, the median PFS was 7.9 mos (95%CI 5.3 -11.5) and OS 33 mos (95%CI > 23.7) compared with 11.9 mos (95%CI 8.1 -14.2) and 39.7 mos (95% CI>23.9) respectively, for standard-risk pts. The ORR, median PFS and OS of each regimen from the time of post-Len Rx are summarized in Table 1, Fig 1. CONCLUSIONS: 1) the median PFS with post-Len Rx in pts progressing on Len-based maintenance after ASCT as part of 1st, 2nd or 3rd line treatment was almost 1 year for all treated pts, with longer benefit observed in pts treated with the newer proteasome inhibitors or Dara-based regimens; 2) since there was potentially confounding overlap among the agents in used in each treatment group studied-- and the 1st, 2nd and 3rd line transplant settings involved different time points in the disease course--further subset analyses of specific regimens/settings are ongoing; 3) nevertheless, the overall real-world results observed with newer agents approximate the outcomes reported in clinical trials in pts progressing on Len-based regimens after 1-3 prior lines of therapy. Disclosures Reece: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Otsuka: Research Funding; Amgen: Consultancy, Honoraria, Research Funding. McCurdy:Celgene: Honoraria; Janssen: Honoraria. Song:Celgene: Honoraria, Research Funding; Janssen: Honoraria; Amgen: Honoraria; Takeda: Honoraria. Leblanc:Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees. Sebag:Amgen: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. White:Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria. Stakiw:Janssen: Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria; Amgen: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Roche: Research Funding; Lundbeck: Honoraria; Sanofi: Honoraria. Louzada:Janssen: Consultancy, Honoraria; Bayer: Honoraria; Celgene: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Kotb:Merck: Honoraria, Research Funding; Janssen: Honoraria; Amgen: Honoraria; Karyopharm: Equity Ownership; Celgene: Honoraria; Takeda: Honoraria. Venner:Sanofi: Honoraria; Janssen: Honoraria; Amgen: Honoraria, Research Funding; Takeda: Honoraria; Celgene: Honoraria; J&J: Research Funding. OffLabel Disclosure: This abstract describes several combinations not specifically approved by the FDA but utilized in the real-world setting. However, all of the individual drugs in these combinations have been approved as single agents or in other combinations.

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)0,002
score de la tête « metaresearch » (Gemma)0,007
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 enseignants0,887
Score d'incertitude au seuil0,228

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,011
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
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 Opus0,072
Tête enseignante GPT0,350
Écart entre enseignants0,278 · 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
Publié2019
Routes d'admission2
Résumé présentoui

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