Resistance to Lenalidomide in Multiple Myeloma Is Associated with a Switch in Gene Expression Profile
Notice bibliographique
Résumé
Abstract Background: Cereblon (CRBN) is a substrate receptor of the Cullin 4 E3 ubiquitin ligase complex CRL4CRBN and is the molecular target of the IMiD® immunomodulatory drug lenalidomide. It has been shown that cereblon is required for the anti-proliferative activity of lenalidomide in multiple myeloma (MM) and that reduction of CRBN expression is associated with resistance to IMiD® compounds. Methods: RNA-seq analysis was performed on 12 paired MM patients samples of sorted CD138+ cells obtained prior to lenalidomide treatment initiation and after development of resistance. Transcriptome sequence data was generated on an Ion Torrent Proton sequencer with at least 70 million reads per sample. The STAR aligner was used to align raw reads to the Ensembl74 reference annotation. The HTseq and eXpress algorithms were used to quantify gene and transcript counts, respectively, and the Sailfish algorithm was used to validate eXpress transcript counts. The Deseq2 algorithm was used to determine differential expression at gene and transcript levels between paired samples. Results: Of 272 genes observed to change significantly in expression at relapse (FDR < 0.05), a majority (169) were up-regulated. Inter-pathway similarity analysis based on gene set enrichment analysis (GSEA; canonical pathways) suggested 4 distinct processes were down-regulated at relapse, including Notch Signaling, Interferon Signaling and G-coupled protein receptor signaling. Conversely, patients exhibited a single dominant up-regulated process associated with proliferation. Additional GSEA analysis on more specific gene categories revealed up-regulation of the Proliferation gene cluster described in the University of Arkansas for Medical Science (UAMS) classification for newly diagnosed MM (6 of 2599 gene sets tested; FDR<0.01), which is associated with poor prognosis. This suggests that specific gene expression profiles (GEPs) identified in newly diagnosed MM patients may be enriched in relapsed samples. Further analysis of differential gene expression was performed to assess correspondence against the 10 MM GEP subgroups identified by Broyl, et al. (Blood, 2010) from newly diagnosed multiple myeloma. The output revealed significant enrichment of the Proliferation and MMSET/FGFR3 subgroup classifications (FDR<0.01) and a corresponding decrease in the NFkB subgroup classification at relapse (FDR<0.01), indicating a switch in GEP enrichment in relapse samples. Significantly changed genes common between Proliferation-MMSET/FGFR3 and NFkB classifications and contributing to the switch in GEP included BUB1B (FDR<0.001), HMMR (FDR<0.001), TAGAP (FDR<0.001), SMC4 (FDR=0.002), RRM2 (FDR=0.005) and KLF6 (FDR=0.029). Examination of genes commonly associated with lenalidomide mechanism of action revealed that in this cohort CRBN RNA was down-regulated by more than 2-fold in one patient and an enrichment of CRBN transcript lacking exon 10 was observed in another patient. Interestingly, high levels of CRBN transcripts that retain introns 6, 7 and 8 and do not encode for protein were detected in both diagnostic and relapse samples, which might explain previously observed discordant expression between CRBN mRNA and protein. Furthermore, no significant changes in gene expression at relapse was observed for Aiolos, Ikaros, c-myc or IRF4, although there was a trend for c-myc up-regulation. Ikaros and Aiolos are known to undergo extensive splicing, however, we were unable to detect changes in Aiolos or Ikaros splicing in patients at relapse. Conclusions: Taken together, this data suggests that lenalidomide resistance in patients is associated with a switch in gene expression profile from NFkB to Proliferation and MMSET/FGFR3 subgroups identified by Broyl, et al (Blood 2012). Given these GEPs were obtained from newly diagnosed patients, this yields the hypothesis that lenalidomide treatment induces a reduction in MM cells with an NFkB gene expression profile and expansion of cells exhibiting a Proliferation and/or MMSET/FGFR3 associated GEP, which appear to be resistant to therapy. Future studies to understand how individual genes in the GEP subgroups identified contribute to lenalidomide sensitivity/resistance are on-going. Disclosures Amatangelo: Celgene Corporation: Employment, Equity Ownership. Neri:Celgene: Research Funding. Ortiz:Celgene Corporation: Employment. Bjorklund:Celgene Corporation: Employment, Equity Ownership. Gandhi:Celgene: Employment, Equity Ownership. Klippel:Celgene Corporation: Employment, Equity Ownership. Bahlis:Amgen: Consultancy; Johnson & Johnson: Consultancy; Johnson & Johnson: Speakers Bureau; Johnson & Johnson: Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau. Daniel:Celgene Corporation: Employment, Equity Ownership. Chopra:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment. Thakurta:Celgene Corporation: Employment, Equity Ownership.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».