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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".