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Record W4389235299 · doi:10.1182/blood-2023-181138

Second Line Treatment and Outcomes of Patients with Multiple Myeloma: A Real World Multi-Institutional Report from the Canadian Myeloma Research Group (CMRG) Database

2023· article· en· W4389235299 on OpenAlexaffabout
Arleigh McCurdy, Engin Gul, Donna Reece, Christopher P. Venner, Darrell White, Jiandong Su, Michael P. Chu, Víctor H. Jiménez‐Zepeda, Kevin Song, Hira Mian, Michaël Sébag, Debra Bergstrom, Julie Stakiw, Tony Reiman, Rami Kotb, Muhammad Aslam, Rayan Kaedbey, Martha Louzada, Richard LeBlanc

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsHôpital Maisonneuve-RosemontLondon Health Sciences CentreJewish General HospitalUniversité de MontréalUniversity of SaskatchewanMemorial University of NewfoundlandConcordia UniversityJuravinski Cancer CentrePrincess Margaret Cancer CentreUniversity of CalgaryVancouver General HospitalMcGill UniversityUniversity of AlbertaQueen Elizabeth II Health Sciences CentreDalhousie UniversitySaint John Regional HospitalBC Cancer AgencyCancerCare ManitobaSaskatchewan Cancer AgencyOttawa HospitalUniversity of British Columbia
Fundersnot available
KeywordsLenalidomideMultiple myelomaMedicineBortezomibRegimenInternal medicineAutologous stem-cell transplantationOncologyTransplantationRetrospective cohort studyDatabaseSurgery

Abstract

fetched live from OpenAlex

Introduction: The treatment of multiple myeloma (MM) has evolved rapidly, with various combinations of immunomodulators (IMiDs), proteasome inhibitors (PIs), corticosteroids and autologous stem cell transplantation (ASCT) utilized in newly diagnosed (ND) patients; in some jurisdictions, monoclonal antibodies (MABs) are also incorporated in first-line treatment. Over the last decade, the standard of care in Canada for NDMM in fit younger patients has consistently been induction (CyBorD/more recently RVD) followed by ASCT and maintenance lenalidomide (len) until progression. In transplant-ineligible patients, bortezomib- or len-based treatments in doublets or triplets have been used frontline. At relapse, options have included re-induction followed by 2nd ASCT or doublet/triplet combinations until progression. Limited comparative data are available to support any one approach. The aim of this retrospective study was to assess the treatment patterns and outcomes of MM patients treated at 1st relapse in a real-world setting. This data may be useful to myeloma stakeholders when evaluating the potential impact of even newer novel immunotherapeutic agents in patients in who have had 1 prior line of treatment. Methods: We performed a retrospective observational study using the Canadian Myeloma Research Group Database (CMRG-DB), which is a prospectively maintained disease-specific database with ≥ 9000 patients enrolled from 16 academic sites across Canada. All MM patients who initiated second-line therapy between 01/12/2010 - 30/06/2022 were included and results were analyzed up to 23/05/2023. We aimed to evaluate the following outcomes for each second-line regimen: overall response rate (ORR), progression-free survival (PFS) and overall survival (OS), calculated from the start of second-line therapy. Survival was estimated using Kaplan-Meier methods and compared between groups using the log rank test. Results: A total of 3569 patients were identified: 1638 (45.9%) who had prior ASCT and 1931 (54.1%) non-ASCT patients. 2715 (76%) patients were bortezomib-exposed in first line, 1052 (30%) were lenalidomide-exposed; 22% had high-risk cytogenetics. The most commonly used second-line regimens were Rd in 1325 (37%) of patients, DRd in 346 (10%), CyBorD in 291 (8%), 2nd ASCT in 270 (8%), RVd in 201 (6%) and DVd in 167 (5%). Outcomes including ORR, ≥ VGPR rate, PFS and OS from second-line treatment for regimens with >50 patients are presented in Table 1. Among all patients, the highest ORRs were seen with DRd (90%), 2nd ASCT (89%), Kd/KCd (79%), RVd (78.1%) and KRd (74%); DRd had the highest ≥ VGPR rate (66%). In patients who had received bortezomib-based first line treatment, the second line ORRs were 96%, 98% and 95% for VRd, KRd and DRd, respectively, with a corresponding median PFS of 20 months (VRd), 19 months (KRd) and 28 months (DRd). For patients who had len-based first line treatment, the second line ORRs were 80% for Kd/KCd and 95% for DVd with a median PFS of 16 months (Kd/KCd) and 11 months (DVd), respectively. The median PFS was 41 months after 2 nd ASCT; of these patients, 68% were exposed to bortezomib in first line and 34% were len exposed in first line. The median PFS was 30 months in those who had len maintenance after their 1 st transplant compared to 60 months in those with no initial maintenance. Conclusion: In this real-world observational study we demonstrate that Canadian patients achieved results comparable those noted in prospective clinical trials leading to the approval of these agents in the second-line setting. Triplet combinations with an IMID backbone, including DRd, KRd and Ixa-Rd, had high response rates, with DRd offering the longest PFS. Patients undergoing 2 nd ASCT had ORRs and PFS comparable to DRd, although these results were likely influenced by selection of a population with a favorable response to 1st ASCT . Given the expanding use of IMID, PI and MAB combinations in first-line, our results highlight the need for better modalities at the time of 1 st relapse. Earlier integration of “novel” agents, including CAR-T therapy, bi-specifics, conjugated antibodies and CEL-MoDs-even in the second-line setting–is likely required to improve on these outcomes. The current analysis provides efficacy benchmarks to guide their implementation. Financial Support: CMRG received financial support from Janssen Inc. for the conduct of this study

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)0.005
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 score0.113
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.070
GPT teacher head0.342
Teacher spread0.272 · 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
Published2023
Admission routes2
Has abstractyes

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