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Enregistrement W2582009911 · doi:10.1182/blood.v126.23.3961.3961

Long-Term Outcomes, Secondary Malignancies, and Stem Cell Collection Following Bendamustine in Patients with Previously Treated Indolent Non-Hodgkin Lymphoma

2015· article· en· W2582009911 sur OpenAlexaff
Peter Martin, Zhengming Chen, Bruce D. Cheson, Tricia Ellis, Katherine Robinson, Michael E. Williams, Saurabh Rajguru, Jonathan W. Friedberg, Richard H. van der Jagt, Ann S. LaCasce, Robin Joyce, Kristen N. Ganjoo, Nancy L. Bartlett, Bernard Lemieux, Ari M. Vander Walde, Jordan A. Herst, Jeff Szer, Michael Bär, Fernando Cabanillas, Anthony J. Dodds, Paul G. Montgomery, Bryn Pressnail, Mitchell R. Smith, John P. Leonard

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensRoyal Victoria Regional Health CentreSudbury Regional HospitalCentre Hospitalier de l’Université de MontréalOttawa HospitalDalhousie University
Organismes subventionnairesnon disponible
Mots-clésBendamustineMedicineLymphomaStem cellOncologyInternal medicinePathologyRituximabBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Background Given the widespread use of bendamustine, data on long-term outcomes are essential for patients and clinicians to understand potential risks and benefits of therapy. Despite a long history as treatment for indolent non-Hodgkin lymphoma (iNHL), such information has been limited. We retrospectively reviewed the registration SDX-105-01 and SDX-105-03 trials (bendamustine 120 mg/m2 days 1+2 q21 days) and SDX-105-02 trial (bendamustine 90 mg/m2 days 1+2 plus rituximab 375 mg/m2 day 1 q28 days) to characterize long-term toxicity and efficacy of patients treated with bendamustine. Methods Patient level data was retrospectively collected from patients treated on the SDX-01, 02, and 03 trials. Descriptive statistics were used to summarize patient characteristics and events. The Kaplan-Meier method was used to report time-to-event outcomes. Wilcoxon Rank sum test was used to test the difference between events for continuous variables. Results Out of the total 245 subjects at 45 sites, data were available for 149 subjects (60 men, 89 women; SDX-01 N = 40, SDX-02 N = 43, SDX-03 N = 66) at 21 sites (included based on willingness to participate). The median age was 60 years at the start of bendamustine (range 39-84). The histologies included grade 1-2 follicular lymphoma (FL; N = 73), grade 3 FL (N = 23), SLL (N = 20), marginal zone lymphoma (N = 15), mantle cell lymphoma (N = 9), transformed lymphomas (N = 5), lymphoplasmacytic lymphoma (N = 2), and not reported (N = 2). The average time from diagnosis to study entry was 41 months (range 2-229). The median number of therapies prior to bendamustine was 2.5 (range 1-8). Patients received a median of 6 cycles and a median total dose of bendamustine of 1408 mg (max 5216, min 240). With a median follow up of 8.8 years after study entry, 80 patients had experienced progression. The median PFS was 18.4 months (95% C.I. 11.9-27.8); the 3-year PFS was 37%. During follow up, 93 patients had died at a median time of 22.3 months after the start of bendamustine. The median OS after start of bendamustine was 65.9 months (95% C.I. 38.8-91.8). The causes of death were lymphoma (N = 45), bendamustine toxicity (N = 2), subsequent treatment toxicity (N = 8), MDS/AML (N = 5), other cancer (N = 2), other (N = 6), and unknown (N = 25). A total of 98 patients received a median of 2 therapies following bendamustine (range 1-9), with the first treatment occurring a median of 13.2 months (range 0-111.3) following the final dose of bendamustine. The reported best response to the first subsequent treatment was CR (N = 11), PR (N = 6), SD (N = 21), PD (N = 12), not evaluable (N = 25), and unknown (N = 22) and the median OS of these patients was 51.3 months (95% C.I. 33.4-80.3). Fourteen patients had attempted stem cell collection following bendamustine, 10 of which had stem cells collected successfully. Eight patients had stem cells collected with GCSF alone (N = 7) or GCSF plus chemotherapy (N = 1). Twenty-three patients developed 25 cancers following bendamustine. Six patients developed MDS and 2 more developed AML. The median time to MDS/AML following bendamustine was 24 months (range 10-103) with an annualized incidence rate of 0.52%/year. One of patient had a prior myeloid neoplasm and one had a prior germ cell tumor. In univariate analysis, neither age at lymphoma diagnosis (P=0.438), nor total number of systemic regimens (P=0.443), nor total dose of bendamustine (P=0.291) was associated with MDS/AML. Other cancers included adenocarcinoma (colon N = 2; prostate N = 2; lung N = 2; breast N = 1), non-melanoma skin cancer (N = 6), squamous cell carcinoma (N = 2), hepatocellular carcinoma (N = 1), and bladder cancer (N = 1). None of these occurred in the 12 patients with a history of solid tumor before bendamustine. Conclusions With a median follow up of survivors of > 8 years, there was no evidence that bendamustine in the setting of previously treated iNHL was associated with a high rate of long-term bone marrow toxicity. Rates of MDS/AML and failure to collect stem cells were lower than expected. However, roughly half of all patients died within 5 years of starting bendamustine, thereby limiting the long-term follow up. A small but meaningful number of patients achieved durable remissions following bendamustine. These rigorously collected, patient-level, long-term follow up data provide reassurance that bendamustine or bendamustine plus rituximab is associated with efficacy and safety for many patients with relapsed or refractory iNHL. Disclosures Martin: Janssen: Consultancy, Honoraria; Acerta: Consultancy; Gilead: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Bayer: Consultancy. Cheson:AstraZeneca: Consultancy; Ascenta: Research Funding; Spectrum: Consultancy; Astellas: Consultancy; MedImmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Teva: Research Funding; Celgene: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Roche/Genentech: Consultancy, Research Funding. Williams:Celgene: Consultancy, Other: Research funding to my institution; Takeda: Consultancy, Other: Research Funding to my institution; Genentech: Other: Research funding to my institution. Bartlett:Gilead: Consultancy, Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding; Pfizer: Research Funding; Novartis: Research Funding; Millennium: Research Funding; Colgene: Research Funding; Medimmune: Research Funding; Kite: Research Funding; Insight: Research Funding; Seattle Genetics: Consultancy, Research Funding; MERC: Research Funding; Dynavax: Research Funding; Idera: Research Funding; Portola: Research Funding; Bristol Meyers Squibb: Research Funding; Infinity: Research Funding; LAM Theapeutics: Research Funding. Szer:Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Australia: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Smith:celegene, spectrum, genentech: Honoraria. Leonard:Weill Cornell Medical College: Employment; Genentech: Consultancy; Medimmune: Consultancy; AstraZeneca: Consultancy; Spectrum: Consultancy; Boehringer Ingelheim: Consultancy; Vertex: Consultancy; ProNAI: Consultancy; Biotest: Consultancy; Seattle Genetics: Consultancy; Pfizer: Consultancy; Mirati Therapeutics: Consultancy; Gilead: Consultancy; Novartis: 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 enseignants0,001
Score d'incertitude au seuil0,007

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
Bibliométrie0,0000,001
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 Opus0,013
Tête enseignante GPT0,225
Écart entre enseignants0,213 · 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
Publié2015
Routes d'admission1
Résumé présentoui

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