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Record W2985424375 · doi:10.1182/blood-2019-125479

Treatment of Multiple Myeloma Patients Progressing on Lenalidomide-Based Regimens

2019· article· en· W2985424375 on OpenAlexaffabout
Donna Reece, Esther Masih‐Khan, Eshetu G. Atenafu, Víctor H. Jiménez‐Zepeda, Arleigh McCurdy, Kevin Song, Michaël Sébag, Richard LeBlanc, Martha Louzada, Darrell White, Julie Stakiw, Tony Reiman, Rami Kotb, Muhammad Aslam, Engin Gul, Christopher P. Venner

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsSaint John Regional HospitalQueen Elizabeth II Health Sciences CentreDalhousie UniversityLondon Health Sciences CentreHôpital Maisonneuve-RosemontUniversity of OttawaMcGill University Health CentreUniversity of New BrunswickLeukemia & Lymphoma Society of CanadaUniversity of British ColumbiaUniversity of CalgaryPrincess Margaret Cancer CentreCancerCare ManitobaOttawa Hospital
Fundersnot available
KeywordsLenalidomideCarfilzomibMedicineRegimenMultiple myelomaIxazomibBortezomibInternal medicineThalidomideOncologySurgeryGastroenterology

Abstract

fetched live from OpenAlex

Lenalidomide in combination with dex (Len + dex) was introduced as treatment for relapsed/refractory myeloma in Canada over a decade ago; more recently, Len has been routinely available as part of first-line therapy, both in transplant-eligible patients-- as post-ASCT maintenance--and in transplant-ineligible patients as the Len + dex combination, and is typically given until disease progression. The management of patients progressing on Len regimens is evolving as newer anti-myeloma drugs become available. Many questions remain regarding the sequencing of treatments to obtain the longest periods of disease control between repeated relapses. We utilized the national Myeloma Canada Research Network (MCRN) Database to analyze therapy administered immediately after progression on a Len-based regimen in real-world practice. The MCRN Database contains disease-specific information on over 6000 patients reported from 13 academic centres in Canada. Between 2007 and 2019, 1482 patients (pts) progressed on Len-containing regimens in lines 1-3. 57% were male, 23% had light chain myeloma, 38% had high-risk FISH and 43% had undergone prior ASCT. Median values (range) for other pt characteristics included: age 64 range (31-92) yrs, creatinine 93 µmol/L (32-2700), B2M 329 nmol/L (range 1-7193), Hb 104 g/L (3-169), WBC 5.7 (0.12-107.6), platelets 211 (10-832), LDH 191 U/L (55-1908). The next regimen was based on bortezomib (BTZ) in 370, as part of a triplet in 278 (CyBorD, VMP); carfilzomib (CFZ) in 100, triplet in 62(KCD, KRD, KPD); ixazomib (IXA) in 75, triplet in 63 (IxaCD, IxaRD, IxaPomD); daratumumab (Dara) in 80, triplet in 75 (DaraCD, DaraRD, DaraPomD, DaraVD, DaraKD); pomalidomide (POM) in 195, triplet in 79 (PomCD/P, PomVD, PomKD); and continuation of Len in 212, triplet in 124 (RCD, RVD, RVCD). 346 (23%) did not receive any further therapy, including 79 (15%) after 1st line Len, 199 (26%) after 2nd line Len and 68 (33%) after 3rd line Len; other regimens were used in 6%, 18% and 11% of each group, respectively. The overall response rates (ORR)/median duration of treatment in months (mos) for each of the regimens included: BTZ 67%-5.8 mos; CFZ 70%-5.6 mos; IXA 60%-6.7 mos; Dara 86%-6.9 mos; POM 36%-4.0 mos; Len 39%-5.7 mos; and other 48%-3.3 mos. PFS by regimen is shown in Figure 1. 514 pts had progressed after Len as 1st line therapy, 766 after Len as 2nd line therapy and 202 after Len as 3rd line therapy. Median follow-up was 15 mos (1-130) after 1st progression and 55 mos (1-278) from diagnosis. The median PFS (95% CI) and OS (95% CI) after progression following Len as 1st line therapy were 14.5 mos (12.1-17.3) and 31.2 mos (25.3-39.0), as 2nd line therapy 8.6 mos (7.3-9.8) and 14.1 mos (11.6-16.5); and as 3rd line therapy 8.0 mos (6.7-9.9) and 11.0 mos (6.9-14.1), respectively. Figure 2 shows PFS by prior Len subgroup. More detailed analyses to assess outcomes for specific regimens after Len progression in different lines of therapy are in progress. In summary, this analysis provides an overview of treatment patterns following progression on Len in Canada, as well as the ORR, PFS and OS of different regimens. Results appeared better for proteasome inhibitor and Dara regimens after progression on Len, although some pts derived benefit from further Len regimens as well as POM-based ones; further exploration of specific treatment subgroups is ongoing. These data provide benchmarks for real-world outcomes that can be utilized as newer therapies, such as those based on immunotherapy, become available. Disclosures Reece: Amgen: Consultancy, Honoraria, Research Funding; Merck: Research Funding; BMS: Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Otsuka: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. McCurdy:Janssen: Honoraria; Celgene: Honoraria. Song:Janssen: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Takeda: Honoraria. Sebag:Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Leblanc:Janssen: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Louzada:Celgene: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Bayer: Honoraria; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. White:Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Stakiw:Janssen: Honoraria, Research Funding, Speakers Bureau; Roche: Research Funding; BMS: Honoraria; Novartis: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Celgene: Honoraria, Speakers Bureau; Lundbeck: Honoraria; Sanofi: Honoraria. Kotb:Karyopharm: Equity Ownership; Amgen: Honoraria; Merck: Honoraria, Research Funding; Celgene: Honoraria; Janssen: Honoraria; Takeda: Honoraria. Venner:J&J: Research Funding; Takeda: Honoraria; Sanofi: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Amgen: Honoraria, Research Funding. OffLabel Disclosure: This abstract describes several combinations not specifically approved by the FDA but utilized in the real-world setting. However, all of the individual drugs in these combinations have been approved as single agents or in other combinations.

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)0.001
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 score0.164
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.291
Teacher spread0.267 · 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
Published2019
Admission routes2
Has abstractyes

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