Inhibition of the Proteasome in Bone Marrow-Derived CD138+ Tumor Cells Following Carfilzomib Administration in Relapsed or Refractory Myeloma Patients.
Notice bibliographique
Résumé
Abstract Abstract 1845 Poster Board I-871 Background: Proteasome inhibition is an effective strategy for the treatment of multiple myeloma. In patients, proteasome inhibition has primarily been measured in peripheral blood samples (whole blood or mononuclear cells). However, it is unknown whether myeloma cells in the bone marrow (BM) are equally sensitive to proteasome inhibitors such as bortezomib (BTZ) and carfilzomib (CFZ). Aim: To measure proteasome inhibition in purified tumor cells from BM samples taken from patients enrolled in two ongoing Phase 2 trials of single agent CFZ in relapsed or refractory myeloma: PX-171-003 (003) and PX-171-004 (004). Methods: CFZ was administered as an IV bolus of 20 mg/m2 on Days 1, 2, 8, 9, 15 and 16 of a 28-day cycle on both trials. Bone marrow samples, from an optional sub-study of both trials, were taken during screening and Day 2 (post-treatment) and sorted into CD138+ and CD138− cells. Proteasome activity was measured by an enzymatic assay using a fluorogenic substrate (LLVY-AMC) for the chymotrypsin-like (CT-L) activity and an active site ELISA (ProCISE) to quantitate levels of the CT-L subunits of the constitutive proteasome (Beta5) and immunoproteasome (LMP7) and the immunoproteasome subunit MECL1. Results: Whole blood samples from patients treated with CFZ showed inhibition of CT-L activity of ∼80+, similar to values obtained in Phase 1 studies. A total of 10 CD138+ screening samples, 6 from 004 and 4 from 003, and 9 post-dose samples, 5 from 004 and 4 from 003, were analyzed for proteasome levels and activity. In addition, 15 CD138−screening samples, 7 from 004 and 8 from 003, and 9 post-dose samples, 5 from 004 and 4 from 003, were analyzed. When compared to the average base-line activity, CFZ treatment resulted in 88% CT-L inhibition in CD-138+tumor cells from 004 patients (P = 0.0212 by unpaired t-test) and 59% CT-L inhibition in CD-138+ tumor cells from 003 patients (P = 0.25). Baseline CT-L activity in CD138+ tumor cells was 3-fold higher in 004 than 003, which includes a more heavily pre-treated patient population with greater prior exposure to BTZ. Higher specific enzymatic activity was due to increased levels of both constitutive and immunoproteasomes in tumor cells, where immunoproteasomes account for >75% of total cellular proteasomes. No differences between trials were seen in baseline CT-L activity from non-tumor (CD138−) cells. Inhibition in CD138− cells was 84% (P = 0.0380 and 42% (P = 0.38) in 004 and 003, respectively. Using ProCISE, we measured inhibition of LMP7 (66%), beta5 (48%) and MECL1 (64%) in CD138+ tumor cells from 004 patients. Three patients from 004 and one from 003 had both a screening and post-dose tumor cell samples available for analysis. Inhibition of CT-L activity was >80% in two of the 3 patients on 004; the third patient showed no proteasome inhibition by ProCISE and was unavailable for analysis by CT-L. CT-L activity in the CD138+ tumor cells in the 003 patient was not inhibited, however, inhibition was seen in non-tumor cells. Conclusions: CFZ inhibits the proteasome activity of myeloma cells in the bone marrow of relapsed and refractory myeloma patients. The levels of inhibition were similar to those measured in whole blood samples, supporting the use of the blood-based assay as a surrogate marker for proteasome inhibition in tumor cells. CFZ treatment resulted in inhibition of both CT-L subunits as well as additional subunits of the immunoproteasome in tumor cells. Reduced baseline activity in the more heavily pretreated 003 patients may reflect reduced tumor-dependency on the proteasome and may be related to prior treatment with BTZ in these patients. More samples are needed in order to make correlations between levels of proteasome inhibition in bone marrow tumor cells and prior therapies or response. These observations support further evaluation of proteasome activity and the effects of this promising new agent in primary tumors cells from myeloma patients. Disclosures: Trudel: Celgene: Honoraria, Speakers Bureau; Ortho Biotech: Honoraria. Lee:Proteolix, Inc.: Employment. Kirk:Proteolix, Inc.: Employment. Lonial:Celgene: Consultancy; Millennium: Consultancy, Research Funding; BMS: Consultancy; Novartis: Consultancy; Gloucester: Research Funding. Wang:Proteolix, Inc.: Research Funding. Kukreti:Celgene: Honoraria. Stewart:Genzyme, Celgene, Millenium, Proteolix: Honoraria; Takeda, Millenium: Research Funding; Takeda-Millenium, Celgene, Novartis, Amgen: Consultancy. Jagannath:Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. McDonagh:Proteolix: Research Funding. Zonder:Celgene: Speakers Bureau; Pfizer: Consultancy; Seattle Genetics, Inc.: Research Funding; Amgen: Consultancy; Millennium: Research Funding. Bennett:Proteolix: Employment.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».