PF607 MAINTENANCE WITH WEEKLY CARFILZOMIB IN ELDERLY NEWLY DIAGNOSED MULTIPLE MYELOMA (IFM 2012–03)
Bibliographic record
Abstract
Background: Continuous therapy, such as maintenance, appears to be a major therapeutic change in multiple myeloma, improving response rate and overall survival. Novel agents widen the range of treatment options, but still Lenalidomide (IMIiD) is widely used in this indication. Even though usually well tolerated it remains a daily treatment, and thus can lead to some side effects on a long term basis. Carfilzomib, a second generation PI, allows interesting response rate and prolonged survival, with manageable adverse events. Nevertheless, only few clinical trials focused on its use in maintenance rather than in first or second line treatment. Aims: Thereforewe, we chose to study maintenance Carfilzomib for elderly newly diagnosed multiple myeloma (eNDMM). Methods: IFM 2012–03 is a multicenter phase I study for eNDMM (patients aged 65 years old and more) that determined the maximal tolerated dose of weekly carfilzomib, associated with melphalan and prednisone (KMP), at 70 mg/m2. The following results will concern the second phase of the study using intravenous Carfilzomib monotherapy in maintenance. K was administered at 36 mg/m2 for 13 cycles on an every 2 weeks schedule. Results: Thirty eNDMM were recruited in IFM 2012–03. Median age is 75, with 56% R‐ISS 2 or 3 and 11% high‐risk cytogenetic. With K weekly from 36 to 70 mg/m2, OS is reported at 93.3%, including 46.7% ≥CR; median PFS is 35.8 months and median OS was not reached. Twenty‐two (73%) patients started K maintenance and 16 (73%) completed it. Four patients progressed and 2 stopped for AEs (renal amylosis, sensory neuropahty) during the maintenance phase. At maintenance completion, 50% were ≥CR. From the start of maintenance, in landmark analysis, median PFS is 28.1 months and the estimated 36‐months OS approximately 70%. Summary/Conclusion: Maintenance Carfilzomib enables deep responses as well as prolonged survival for patients over 65 years old presenting with a newly diagnosed multiple myeloma. With promising results, a fixed duration and a satisfying safety profile it could integrate new treatment strategies for ineligible to transplant NDMM. Surely patients could benefit of its efficacy in addition to simple administration modality. Further studies should still bring additional informations in order to confirm our results. Nowadays, multiple myeloma treatment landscape and clinical practice is constantly evolving leading to significant improvement for our patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".