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Record W2949446669 · doi:10.1182/blood-2019-126867

Outcomes of Patients with t(11;14) Multiple Myeloma: An International Myeloma Working Group Multicenter Study

2019· article· en· W2949446669 on OpenAlexaff
Shaji Kumar, Jin Lu, Yang Terry Liu, Max Bittrich, Juan Du, Hartmut Goldschmidt, Charalampia Kyriakou, Donna Reece, Kihyun Kım, María‐Victoria Mateos, Verónica González‐Calle, Wen-Ming Chen, Heinz Ludwig, Giampaolo Merlini, Silvia Mangiacavalli, Meletios Α. Dimopoulos, Eftathios Kastritis, Chang‐Ki Min, Graça Esteves, Andrew J. Yee, Noopur Raje, Emily Rosta, Anja Haltner, Chris Cameron, Brian G.M. Durie

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMultiple myelomaMedicineInternal medicineRegimenChromosome abnormalityCohortLenalidomideBortezomibStage (stratigraphy)Median follow-upOncologyGastroenterologySurgeryKaryotypeChemotherapyChromosomeBiology

Abstract

fetched live from OpenAlex

Background: Multiple myeloma (MM) is a heterogeneous disease with varying survival outcomes depending on the presence of certain genetic abnormalities. Common abnormalities include trisomies, translocations involving the chromosome 14, and amplifications or deletions of chromosomes 1, 13, and 17. t(11;14), occurring in 15% of patients with myeloma, had been considered a standard risk abnormality, but recent data suggest inferior outcome. This is important as new therapeutic options such as the BCL-2 inhibitor venetoclax has been shown to be particularly effective in t(11;14) patients. Methods: This was a multicenter study to identify the outcomes of patients with t(11;14), using a retrospectively assembled cohort. Patients with MM diagnosed between 2005 and 2015 with t(11;14) identified on FISH performed within six months of diagnosis, and with treatment details available and if alive, a minimum of 12 months of follow up, were enrolled. Results: The current analysis includes 1216 patients; median age of 62.56 years; 58.7% male. The median follow-up from diagnosis for the entire cohort was 51.9 months; 69.1% of the patients were alive at the last follow up. ISS stage distribution included: Stage I (35.7%), Stage II (34.0%) and Stage III (15.1%), data was missing for the rest. The distribution of concurrent FISH abnormalities included: trisomies (3.5%), del 13q (13.3%), 1q amp (8.8%), and del 17p or monosomy 17 (5.8%). Initial regimen included: 27.2% had an immunomodulatory (IMiD), 45.9% had a proteasome inhibitor (PI), 17.7% had both, and 9.0% had no novel agent. The drug classes by line of therapy are shown in Table 1. An early stem cell transplant (defined as within 12 months of start of first line treatment) was used in 49.4% of patients. The median time to next treatment (TTNT) after starting initial treatment was 26.6 (95% CI: 23.9 to 29.2) months. The median overall survival (OS) from diagnosis for the entire cohort was 95.1 (95% CI: 85.9 to 105.9) months; 4-year estimates for those diagnosed from January 2005 to December 2009, and from January 2010 to December 2014 were 77.5% and 78.6%, respectively. The median OS for those with any one high risk FISH lesion (del 17p/ 1q amp) was 67.5 (55.2, 97.1) versus 101.7 (89.7, 107.3) months. Patients with early SCT (within 12 months of diagnosis) had better OS: 108.3 (103.8, 133.0) vs. 69.8 (61.5, 80.3) months. Conclusion: Patients with t(11;14) without high risk FISH abnormalities have an excellent survival. Patients receiving a PI + IMiD combination and those receiving autologous SCT as part of initial therapy had best survival. Though numbers are limited, patients in the later lines receiving newer drugs such as venetoclax and daratumumab had high response rates and durable responses. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding. Bittrich:Celgene: Other: Travel Funding, Research Funding; Else Kröner Fresenius Foundation: Research Funding; Otsuka Pharmaceuticals Europe: Other: N/A; SANOFI Aventis: Membership on an entity's Board of Directors or advisory committees, N/A, Research Funding; University Hospital Wuerzburg: Employment; Bristol Myers Squibb: Research Funding; Pfizer: Other: Travel Funding; AMGEN: Other: Travel Funding; JAZZ Pharmaceuticals: Other: Travel Funding; Wilhelm Sander Foundation: Research Funding; German Research Foundation (DFG): Other: N/A; University of Würzburg: Other: N/A. Goldschmidt:Mundipharma: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; John-Hopkins University: Research Funding; Dietmar-Hopp-Stiftung: Research Funding; Janssen: Consultancy, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Research Funding; Molecular Partners: Research Funding; John-Hopkins University: Research Funding; Amgen: Consultancy, Research Funding; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Chugai: Honoraria, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Reece:Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Otsuka: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; BMS: 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; Takeda: Consultancy, Honoraria, Research Funding; Merck: Research Funding. Mateos:Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmamar: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Adaptive: Honoraria; EDO: Membership on an entity's Board of Directors or advisory committees. Ludwig:Celgene: Speakers Bureau; Amgen: Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau; PharmaMar: Consultancy; Janssen: Speakers Bureau; BMS: Speakers Bureau. Mangiacavalli:celgene: Consultancy; Amgen: Consultancy; Janssen cilag: Consultancy. Dimopoulos:Sanofi Oncology: Research Funding. Kastritis:Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; Pfizer: Honoraria; Prothena: Honoraria; Genesis: Honoraria. Yee:Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Karyopharm: Consultancy; Adaptive: Consultancy. Raje:Amgen Inc.: Consultancy; Bristol-Myers Squibb: Consultancy; Celgene Corporation: Consultancy; Takeda: Consultancy; Janssen: Consultancy; Merck: Consultancy. Rosta:Cornerstone Research Group: Employment. Haltner:Cornerstone Research Group: Employment. Cameron:Cornerstone Research Group: Employment, Equity Ownership. Durie:Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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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Citations7
Published2019
Admission routes1
Has abstractyes

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