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Record W2983745227 · doi:10.1182/blood-2019-126828

Genetic and Transcript Changesin Cereblon in IMiD-Treated Myeloma Patients

2019· article· en· W2983745227 on OpenAlexaff
Sarah Gooding, Naser Ansari‐Pour, Fadi Towfic, María Ortiz, Dan Rozelle, Victoria Zadorozhny, Michael Amatangelo, Erin Flynt, Kao-Tai Tsai, Paola Neri, Nizar J. Bahlis, Paresh Vyas, Anjan Thakurta

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCereblonPomalidomideLenalidomideMultiple myelomaMedicineOncologyDrug resistanceCancer researchInternal medicineImmunologyBiologyGeneticsGeneUbiquitin ligaseUbiquitin

Abstract

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Myeloma survival has been significantly improved by novel therapies over the past two decades. Immunomodulatory agents (IMiDs, Lenalidomide (LEN) and Pomalidomide (POM)) are a backbone of current treatment strategies. But myeloma (MM) remains incurable, because patients ultimately relapse. IMiD drug resistance mechanisms are multifactorial, and can be either dependent or independent of IMiDs binding to Cereblon (CRBN) (a component of an E3 ligase) in tumor and immune cells. A small series of relapsed patients demonstrated sub-clonal mutation rates of 12% in CRBN and 10% in other CRBN pathway genes (Kortum et al, Blood 2016), but the clinical implication of this observation is unknown. In contrast, baseline gene expression of CRBN was not associated with clinical outcome to POM-DEX therapy (Qian et al, Leukemia & Lymphoma 2018). An integrated assessment of the burden of different mechanisms of CRBN function loss, the selective pressure for their survival, and their effects on response to CRBN-modulating agents, is lacking. For patients that progress on IMiD-based therapies, there is a need to develop drugs that will overcome their resistance. To appropriately target the right novel agents to the right patients, resistance mechanisms must be understood. A deeper understanding of the mechanistic basis for IMiD-specific resistance mechanisms is critical for differentiating new Cereblon modulating agents (eg CELMoDs) from the IMiDs. Here, we present the largest comprehensive analysis of the burden of CRBN mutation or transcript variants in relapsed refractory myeloma (RRMM) patients. We analysed WGS and RNASeq data from 298 MM samples from 268 patients across 4 clinical trials (CC-4047-MM-010 (N=226), CC-4047-MM-013 (N=17), CC-220-MM-001 (N=45) and CC-122-ST-001MM2 (N=10), for whom outcome data were available. All patients had been exposed to LEN-based therapy and a subset (69/268) were also exposed to POM-based therapy. The overall incidence of single nucleotide variants in CRBN was 17/298 (5.7%). The incidence of at least monoalleleic deletion at the CRBN gene locus was 21/298 (5.5%). CRBN has different transcript isoforms (Gandhi et al, BJHaem 2014). As high levels of isoform ENST00000424814.5, with deletion of exon 10, was previously correlated with poorer survival (Neri et al, Blood 2016), we assessed incidence of a high ratio (>2) of exon 10-deleted CRBN transcript to full length CRBN transcript. 92 samples had sufficient purity (>90% tumor cells) for this analysis. 13/92 samples (14.1%) had a high exon10-deleted transcript ratio. Overall, 43/268 (16.0%) patients had genetic or transcript variants in CRBN. In contrast, 27/514 (5.2%) newly diagnosed myeloma (NDMM) patients from the Myeloma Genome Project had genetic or transcript variants in CBRN; 2/514 (0.4%) had CRBN mutations, 11/514 (2.1%) had CRBN gene deletion and 14/514 (2.7%) had a high exon10-deleted transcript ratio. Thus, there was an increase in CRBN variants from NDMM to RRMM. In patients exposed to LEN but not POM (219/268), there were 5 CRBN mutations, 14 monoallelic CRBN deletions and 13/92 patients with high exon10-deleted transcript ratio. In 69/268 patients exposed to POM (baseline from CC-220-MM-001 (N=35), CC-122-ST-001MM2 (N=10), or follow up samples from CC-4047-MM-010 (N=24)), there were 12 CRBN mutations in 8 patients (11.6%) and 7 CRBN deletions (10.1%), approximately double the incidence seen in the whole cohort. 3 CRBN deletions were homozygous, which was not observed in non-POM-exposed individuals. Sample purity was insufficient to measure transcript ratios. In summary, 16.0% of RRMM patients that received LEN or POM have genetic or transcript variants in CRBN, a higher proportion than in NDMM. The impact of these aberrations on CRBN function, especially related to binding of CRBN-modulating drugs, remains to be ascertained. Analysis of the correlation between CRBN variation and response to therapy, clinical outcomes, and the incidence and effect of mutation or copy loss of CRBN interactors (E3 Ligase members and regulators, CRBN substrates) is underway and will be presented. Disclosures Gooding: Celgene: Research Funding. Ansari-Pour:Celgene Corporation: Consultancy. Towfic:Celgene Corporation: Employment, Equity Ownership. Ortiz:Celgene Corporation: Employment, Equity Ownership. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Zadorozhny:Celgene Corporation: Other: Contractor for Celgene. Amatangelo:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Tsai:Celgene Corporation: Employment, Equity Ownership. Neri:Celgene, Janssen: Consultancy, Honoraria, Research Funding. Bahlis:AbbVie: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Vyas:Novartis: Research Funding, Speakers Bureau; Pfizer: Speakers Bureau; Daiichi Sankyo: Speakers Bureau; Astellas: Speakers Bureau; Abbvie: Speakers Bureau; Celgene: Research Funding, Speakers Bureau; Forty Seven, Inc.: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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
Published2019
Admission routes1
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