Small RNA Next Generation Sequencing (NGS) of CD138+ Plasma Cells from Multiple Myeloma Patients and Comparison to the 70-Gene mRNA-Based Prognostic Risk Score
Notice bibliographique
Résumé
Abstract Background: Small-RNAs (including microRNAs) are a novel class of molecules with functions that include the regulation of coding genes as well as development, proliferation, apoptosis and differentiation of myeloma cells. The presence of these genes has been detected inside cells and also in the extracellular environment, suggesting they may be useful for risk stratification and treatment monitoring. We sought to explore the small-RNA profiles of bone marrow specimens submitted for MyPRS analysis and identify candidate molecules associated with a patient's 70-gene (mRNA) prognostic risk score. Method and Results: 32 bone marrow aspirate samples from patients with multiple myeloma, 3 whole-blood control samples and the MM cell line NCI-H929 were included in the present study. Plasma cells were isolated from the bone marrow aspirates as per standard MyPRS protocols. Total RNA was then extracted using the miRNeasy Mini Kit protocol (Qiagen, Canada). All miRNA libraries were prepared using the Illumina TruSeq Small RNA protocol following the manufacturer's instructions with 11-15 cycles of PCR amplification. Individual libraries were prepared using a unique index primer to allow for pooling of multiple samples. After amplification, Novex TBE PAGE gel electrophoresis was used to select for fragments sized 145- 160 nt, corresponding to mature miRNA's and other small RNA molecules. Libraries were validated and quantified using an Agilent 2100 Bioanalyzer High Sensitivity DNA chip, sequenced on an Illumina NextSeq 500 and analyzed using Illumina BaseSpace Onsite. After adapter trimming, an average of 3.7, 17.7 and 0.5 million reads were generated from the multiple myeloma, normal blood and NCI-H929 cell line libraries, respectively. When comparing the MM vs. control data, 757/6041 total miRNA's passed a low-count filter and 535 of these were found to be differentially expressed. The top 10 miRNA families with the largest difference between sample types according to DESEq2 were mir-1285, let-7, mir-1248, mir-1303, mir-1260b, mir-1301, mir-10, mir-128, mir-129 and mir-130. Within the MM sample group, 620 individual miRNAs were reliably detected and compared between GEP70 high and low risk disease, with 14 passing a differential expression filter. Hierarchal clustering of patients using all 620 genes did not separate patients into high and low risk groups and only 2/411 miRNA families (mir-130 and mir-17) were found to differ between these classes. Serum levels of miR-130a in MM patients have been shown to be associated with extramedullary disease and miR-17 is thought to regulate the Myc oncogene. Conclusion: In this study we identified a number of novel microRNAs with patterns of expression in patient bone marrow aspirates associated with the extensively validated 70-gene risk score available commercially as 'MyPRS'. Two particular miRNA's were identified that appear to be associated with high risk behavior, one of which correlates with the Myc oncogene whose relationship to clinically aggressive MM is well described. Further work is planned to expand the number of patients in the study and to investigate whether these microRNA's are present in the extracellular bone marrow environment and peripheral fluids. Disclosures van Laar: Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Zielinski:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment.
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 | 0,001 | 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) | 0,001 | 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 ».