Altered Genomic and Epigenetic Profiling of Myeloma Bone Marrow Stromal Cells Identifies Targets for Current and Future Immunotherapeutic Approaches
Notice bibliographique
Résumé
Background: Novel drug discoveries have shifted the treatment paradigms of most hematological malignancies including multiple myeloma (MM), but minimal residual disease and drug resistance underlie relapses in MM. Although many genetic and epigenetic alterations regulate MM progression, MM cells are not autonomous. Dynamic interactions between MM cells and cells of the bone marrow (BM) microenvironment have been reported by our group and others. MM plasma cells (PCs) depend on interactions with bone marrow stromal cells (BMSCs) for their survival and growth, but little is known about the specific genetic events taking place in the MM BM microenvironment. Methods: Here we report a detailed analysis of the genetic and epigenetic events that are characteristic of MM BMSC as compared to HD-BMSC interacting with BM PCs. To evaluate genetic and epigenetic landscapes, RNA was extracted from bulk sorted populations of 16 MM-BMSC, 3 HD-BMSC, and 10 autologous MM cells. We prepared libraries for 32 samples using the NEBNext Ultra II Stranded Poly A kit, and then sequenced on the NextSeq 500, PE150. Sequencing data were analyzed using a custom computational and statistical pipeline at the Department of Biostatistics, School of Public Health and Partek software. Results: Unsupervised clustering showed that MM-BMSC samples clustered as a distinct and completely separate cluster from HD-BMSC and autologous MM cells. Gene level analyses of these three groups identified 990 genes differentially expressed (upregulated or downregulated, P< 0.005). Sequential filtration analyses of the differentially expressed genes in MM-BMSC identified significant deregulation of : transcripts in the Jak-STAT signaling pathway (JAK3, PIM1, IL6, CSF2R, AKT1/2, BCL2L1, CDKN1A and range of IL transcripts); genes encoding extracellular matrix interacting proteins (CD36, CD49, LAMA3, CD44, CD47); and various plasma membrane proteins that define different subpopulations of hematopoietic cells. These genes were deregulated in >24% of MM-BMSC samples analyzed as compared to HD-BMSC samples. These transcripts were downregulated in autologous tumor cells. Next, we interrogated the epigenomic landscape and identified the splicing signature of MM-BMSC as compared to HD-BMSC, and autologous MM cells. Comparison of the splicing patterns (exon skipping, intron retention, novel splice acceptor and/or donor activation) of these three distinct groups showed that a total of 2,100 genes were differentially expressed and 566 were alternatively spliced among the three groups (P < 0.01). These analyses identified a limited number of the transcripts with ~3% significantly spliced in MM-BMSC compared to HD-BMSC. However, comparing MM-BMSC splicing events to MM cells splicing events, we identified >30% of genes which were alternatively spliced in MM cells but not in MM-BMSC. Further, gene enrichment and pathway analyses identified a selective set of transcripts that were alternatively and differentially spliced in MM-BMSC including genes involved in MAPK and Ras signaling pathways, homologous recombination, mismatch repair, and adherens junction. Conclusions: Taken together, our studies identified marked differences between important stromal elements in MM- and HD-BM. We identified genes that were specifically upregulated/suppressed in MM-BMSC compared to MM-cells and HD-BMSC. Within MM BMSCs, we identified several splicing events on genes of signaling pathways implicated in development and progression of MM. Furthermore, altered splicing events identified on these transcripts represent potential new immunotherapeutic targets. Disclosures Chu: Gilead: Honoraria; Celgene: Honoraria; Teva: Consultancy; Amgen Inc.: Honoraria; AstraZeneca: Honoraria. Anderson:C4 Therapeutics: Other: Scientific founder ; Gilead Sciences: Other: Advisory Board; OncoPep: Other: Scientific founder ; Sanofi-Aventis: Other: Advisory Board; Janssen: Other: Advisory Board.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».