Immunoglobulin Lambda Translocations Identify Poor Outcome and IMiD Resistance in Multiple Myeloma and Co-Occur with Hyperdiploidy
Notice bibliographique
Résumé
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.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».