Molecular Predictors of Outcome and Drug Response in Multiple Myeloma: An Interim Analysis of the Mmrf CoMMpass Study
Notice bibliographique
Résumé
Abstract The Multiple Myeloma Research Foundation (MMRF) CoMMpass trial (NCT145429) is a longitudinal study of 1147 patients with newly-diagnosed multiple myeloma from clinical sites in the United States, Canada, Spain, and Italy. Each patient receives a treatment regimen containing a proteasome inhibitor, immunomodulatory agent, or both. Clinical parameters are collected at study enrollment and every three months through the eight-year observation period. To identify molecular determinants of clinical outcome each baseline and progression tumor specimen is characterized using Whole Genome Sequencing, Exome Sequencing, and RNA sequencing. Data available as of January 1, 2016 is included in this first formal interim analysis, which includes 995 enrolled patients of whom 851 are molecularly characterized. This cohort of patients includes 74 patients with at least two sequential samples, plus 15 patients with characterized tumor samples from the bone marrow and peripheral blood. The median follow-up of the cohort is 66 weeks, which identified a median PFS of 36 months for the cohort. The median OS was not reached but 76% are still alive at 3 years. Although the age at enrollment by gender is uniform, there is a significant difference in PFS and OS, with males performing worse than females, p=0.001 and p=0.0004 respectively. Analysis of the exome sequencing data from the 746 baseline BM localized tumors identified a median of 122 non-immunoglobulin related mutations per patient, with an interquartile range of 96-155. There is a group of highly mutated (>481 mutations [mean+1SD]) patients who frequently have MAF family translocations (66%) and/or mutations in the DNA repair genes MSH2, MSH3, MSH4, MSH6, or ATM (38%). Across the cohort 21/53 of the DNA repair gene mutations reside in these 21 patients compared to 14/47 MAF family translocations. Analysis of the somatic mutations identified 20 significant genes, which are recurrently mutated and the mutated allele is detectably expressed; BRAF, CYLD, DIS3, FAM46C, FCF1, FGFR3, FUBP1, KRAS, MAX, NFKBIA, NRAS, PRKD2, RASA2, RB1, SAMHD1, SP140, TGDS, TP53, TRAF2, and TRAF3. Integration of the copy number data and the mutation data identified an association between TP53 deletion and mutation, suggesting many patients present with homozygous loss of TP53. Patients with one or two functional TP53 alleles had similar PFS and OS but the patients with zero functional alleles had a significantly reduced OS (p<0.05). Therefore, the existing association between 17p deletion and outcome is driven by the subpopulation of patients with bi-allelic TP53 loss. Analysis of the whole genome sequencing data available from the 719 baseline BM localized tumors for immunoglobulin translocations identified the expected canonical translocations along with events targeting MYC in 14.6% of patients. Unlike the canonical translocations the MYC translocations are enriched in hyperdiploid tumors (22.3%) and often involve light chain loci, particularly the lambda locus. Within the hyperdiploid patients, those with a MYC translocation have a reduced PFS. To identify molecular subtypes we performed unsupervised clustering using a consensus clustering approach on the 613 baseline BM tumors with RNA sequencing data, which identified 12 distinct subtypes. This analysis confirmed previous studies identifing distinct subtypes associated with WHSC1, MAF/MAFA/MAFB translocations and two subtypes associated with CCND1/CCND2/CCND3 subtypes, which can be separated by CD20 expression. Five subtypes are associated with hyperdiploid myeloma, one group is nearly devoid of chromosome 11 gains while this event is ubiquitous in the remaining hyperdiploid groups. The genomic profiles of the paired tumors isolated from the peripheral blood and bone marrow share 87% of the observed events with unique events being more common in the PB compartment suggesting the PB compartments are often derived from a closely related progenitor clone of the bulk BM tumor. Applying our bayesian clonal analysis method to the serial samples identified multiple clones in all patients with some showing no changes in clonal populations while the majority show significant shifts in clonal burden. These analyses have identified tumor initiating mutations and new subtypes of myeloma, which are associated with distinct molecular events and clinical outcomes. Disclosures Niesvizky: Celgene: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy, Research Funding, Speakers Bureau; Onyx: Consultancy, Research Funding, Speakers Bureau. Wolf:Takeda: Honoraria; Telomere Diagnostics: Consultancy; Amgen: Honoraria; Celgene: Honoraria; Pharmacyclics: Honoraria. Lonial:Onyx: Consultancy; BMS: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Janssen: Consultancy; Merck: Consultancy; Onyx: Consultancy; Celgene: Consultancy; Millenium: Consultancy; Novartis: Consultancy; BMS: Consultancy; Celgene: Consultancy.
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 | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».