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Inhibition of the Proteasome in Bone Marrow-Derived CD138+ Tumor Cells Following Carfilzomib Administration in Relapsed or Refractory Myeloma Patients.

2009· article· en· W2977283561 on OpenAlexaff
Suzanne Trudel, Susan Lee, Christopher J. Kirk, Nashat Gabrail, Sagar Lonial, Lühua Wang, Robert Z. Orlowski, Vishal Kukreti, A. Keith Stewart, Sundar Jagannath, Kevin T. McDonagh, Francis K. Buadi, Jeffrey A. Zonder, Mark K. Bennett

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCarfilzomibBortezomibMultiple myelomaBone marrowProteasomeProteasome inhibitorMedicinePeripheral blood mononuclear cellPharmacologyCancer researchInternal medicineChemistryIn vitroBiochemistry

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.265
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2009
Admission routes1
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