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Results of Salvage Autologous Stem Cell Transplantation (ASCT) for Relapsed Multiple Myeloma (MM) in the Era of Novel Agents: Outcome of Patients (Pts) Receiving Prior Bortezomib (BTZ)-Based Therapy

2016· article· en· W2598509493 on OpenAlexaffabout
Sara Farshchi Zarabi, Esther Masih‐Khan, Christine Chen, Vishal Kukreti, Anca Prica, Rodger E. Tiedemann, Suzanne Trudel, Donna Reece

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineBortezomibSalvage therapyAutologous stem-cell transplantationRegimenMultiple myelomaThalidomideSurgeryInternal medicineLenalidomideOncologyCyclophosphamideTransplantationMedian follow-upChemotherapy

Abstract

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Abstract Background: A second (salvage) ASCT has frequently been offered to MM patients with relapsed disease who experience benefit from the first procedure. We have previously reported that pts undergoing a salvage ASCT in the era of VAD or thalidomide (thal) have a median progression-free survival (PFS) of 19 months (mos). The best results were observed in pts who experienced ≥ 2 year benefit after their first ASCT (Jimenez-Zepeda VH et al. Biol Blood Marrow Transplant 2012; 18: 773-9). However, the utility of this approach after the introduction of novel chemotherapeutic agents--such as bortezomib (BTZ)--remains unclear. Initially, provincial funding for BTZ in Ontario was provided only for relapsed disease. However, in 2007, the combination of either BTZ + dexamethasone (BTZ-dex) or cyclophosphamide, BTZ + dex (CyBorD) was adopted as the standard induction regimen for newly diagnosed pts before ASCT performed as part of first-line therapy. We now examine the results of salvage ASCT in our centre after the availability of BTZ. Methods: We used the Princess Margaret Myeloma Database to identify and characterize patients with relapsed MM who had received a bortezomib (BTZ)-based regimen for remission induction prior to their first ASCT or for re-induction before salvage ASCT. A retrospective chart review was performed to investigate the PFS and overall survival (OS) outcomes of these pts. Results: Between 01/2005 and 07/2015, 64 pts with MM who had previously received BTZ-based therapies underwent salvage ASCT for relapsed disease at our centre (Table 1). Median age was 56.9 yrs (range 37-67.3); 37 (58%) were male. ISS stage was 1 in 32 (50%), 2 in 16 (25%), 3 in 14 (22%) and NA in 2 (3%). The median interval between first and salvage ASCT for all pts was 48.6 mos (range 26.9-130.3), reflecting our policy of preferentially offering salvage ASCT to pts with at least a 2-yr benefit from the first transplant; the median time between re- induction therapy and salvage ASCT was 6.3 mos (range 0.3-95.9). Group A pts (n=27) had received BTZ-based therapy before their first ASCT; 48% of these also received BTZ-based regimens again prior to salvage ASCT. Pts in Group B (n=37) received BTZ-based regimens before the salvage transplant only, while induction therapy before the first ASCT consisted of VAD (21), dex alone (8), thal + dex or other regimens (5). Twenty-two (34%) of the pts received maintenance therapy between the first and salvage ASCT (most often thal-based), while 35 (55%) of the pts received maintenance therapy following salvage ASCT (most frequently lenalidomide [len]-based). The survival outcomes are summarized in Table 2. Median duration of follow-up (F/U) following salvage ASCT was 19.1 mos (range 0.8-96.4). One patient (1.6%) died several days following salvage ASCT. No other transplant-related mortality occurred. The median PFS following salvage ASCT was 19.1 mos (range 0.8- 87.5) with a median OS of 26.5 mos (range 0.8-101.9) in all pts. The median PFS after salvage ASCT was 15.8 mos for Group A and 25.2 mos for Group B pts. Conclusions: Even in the era of novel agents, salvage ASCT may provide PFS benefit to pts with relapsed MM who were previously treated with a BTZ-based regimen. However, the details of the optimal approach in this setting are not certain, including the impact of maintenance therapy given after the first and/or salvage ASCT. We are performing additional analyses of this population to try to identify factors associated with the best outcomes. Disclosures Kukreti: Celgene: Honoraria; Lundbeck: Honoraria; Amgen: Honoraria. Prica:Janssen: Honoraria. Tiedemann:Novartis: Honoraria; Celgene: Honoraria; Takeda Oncology: Honoraria; BMS Canada: Honoraria; Amgen: Honoraria; Janssen: Honoraria. Trudel:Celgene: Honoraria; Novartis: Honoraria; Glaxo Smith Kline: Honoraria, Research Funding; Oncoethix: Research Funding. Reece:Merck: Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Otsuka: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.299
Teacher spread0.256 · 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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Citations2
Published2016
Admission routes2
Has abstractyes

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