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Record W2898813536 · doi:10.1182/blood-2018-99-113585

Immunoglobulin Lambda Translocations Identify Poor Outcome and IMiD Resistance in Multiple Myeloma and Co-Occur with Hyperdiploidy

2018· article· en· W2898813536 on OpenAlexaff
Benjamin G. Barwick, Paola Neri, Nizar J. Bahlis, Ajay K. Nooka, Jonathan L. Kaufman, Vikas A. Gupta, Daniel Auclair, Jonathan J. Keats, Sagar Lonial, Paula M. Vertino, Lawrence Boise

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsMultiple myelomaChromosomal translocationLenalidomideInternal medicineBiologyHazard ratioOncologyImmunologyCancer researchMedicineGeneticsConfidence intervalGene

Abstract

fetched live from OpenAlex

Abstract Patients with the plasma cell malignancy multiple myeloma now benefit from treatments such as proteasome inhibitors, immunomodulatory imide drugs (IMiDs), autologous stem cell transplant, and monoclonal antibodies. However, 20% of patients still relapse or die within two years and are deemed 'high risk'. Current markers fail to identify all high-risk patients resulting in misdiagnoses, therefore additional markers for this deadly form of the disease are required. To better understand and identify high-risk myeloma, we analyzed the structural variant landscape of 826 myelomas from newly-diagnosed patients using whole genome sequencing as part of the CoMMpass trial (NCT01454297). High-confidence somatic structural variants were determined using DELLY and quality control metrics to exclude regions with sequencing anomalies. Myeloma from newly diagnosed patients had a median of 21 somatic structural variants including 7 duplications, 2 deletions, 7 inversions, and 3 translocations. The number of deletions, duplications, and translocations corresponded to poor progression-free (PFS) and overall survival (OS), with translocations being the most significant (P <6.1x10-7). The two most common translocations occurred at the IgH (41%) and MYC (23%) loci, but did not correspond with differential outcome. However, the third most commonly translocated region (10%) occurred at the IgL locus and was indicative of poor PFS and OS with hazard ratios (HR) of 1.71 and 1.81, respectively (Figure 1). IgL-translocated myeloma did not correspond with distinct clinical features such as age, stage, gender, or b2M levels; and IgL-translocated patients were treated with similar therapeutic regimens as others. Additionally, IgL-translocated myeloma did not have a distinct mutational repertoire, gene expression subtype, and did not have many unique structural genetic elements. The notable exception is that 70% of IgL-translocated myeloma co-occurred with hyperdiploidy, a marker normally associated with better prognosis. However, patients with IgL-translocated and hyperdiploid myeloma experienced poor outcome with a median PFS of 23 months compared to 42.8 months for non-t(IgL) hyperdiploid myeloma (PFS HR=2.35; OS HR=2.41). This poor outcome is partially explained by the failure of patients with IgL-translocated myeloma to benefit from IMiDs. In fact, IMiDs provided no survival benefit to patients with IgL-translocated myeloma who experienced poor outcomes commensurate with patients that did not receive IMiDs (PFS HR=1.56; OS HR=1.49). This is in contrast to patients with myeloma that harbor other translocations, such as IgH translocation, who benefited from IMiDs (PFS HR=0.83, OS HR=0.61). These data identify IgL translocation as an independent marker of poor prognosis regardless of translocation partner, and suggest this may be due to the failure of this myeloma subtype to benefit from IMiDs. One potential mechanistic explanation is that the IgL enhancer is one of the most robust enhancers of gene expression and is therefore uniquely resistant to therapeutic inhibition. Indeed, the IgL enhancer is bound by several transcription factors at some of the highest levels in the B cell / myeloma epigenome, including BRD4, MED1, and IKZF1. This last factor is particularly interesting as IKZF1 is the target of IMiDs, and thus high-levels of IKZF1 occupancy at the IgL enhancer may be more difficult to fully deplete therapeutically than other loci. This may explain why patients with IgL-translocated myeloma do not benefit from IMiDs whereas patients with IgH- or IgK-translocated myeloma do. Finally, the co-occurrence of myeloma with IgL-translocation and hyperdiploidy is particularly unfortunate, as hyperdiploidy is routinely tested for clinically, whereas IgL-translocations are rarely diagnosed, likely resulting in their misclassification as standard risk. Figure: IgL translocations portend poor prognosis. a Circos plot showing the repertoire of IgL translocations in newly diagnosed myeloma where line thickness denotes frequency (key bottom left). b Kaplan-Meier analysis of IgL translocated [t(IgL)] patients (N=81) as compared to non-t(IgL) (N=745) for progression-free (PFS; top) and overall survival (OS; bottom). P-values were calculated using a Cox proportional hazards Wald's test or permutation based P-value with 1,000 permutations based on the hazard ratio. Disclosures Neri: Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Bahlis:Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding. Nooka:Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kaufman:Janssen: Consultancy; Roche: Consultancy; Karyopharm: Other: data monitoring committee; Abbvie: Consultancy; BMS: Consultancy. Lonial:Amgen: Research Funding. Boise:Abbvie: Consultancy; AstraZeneca: Honoraria.

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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.324
Teacher spread0.292 · 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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Citations0
Published2018
Admission routes1
Has abstractyes

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