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

Genetic and Transcript Changesin Cereblon in IMiD-Treated Myeloma Patients

2019· article· en· W2983745227 sur 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

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésCereblonPomalidomideLenalidomideMultiple myelomaMedicineOncologyDrug resistanceCancer researchInternal medicineImmunologyBiologyGeneticsGeneUbiquitin ligaseUbiquitin

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,248
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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