miRNA Expression in Multiple Myeloma as Predictive Model of Response to Bortezomib.
Bibliographic record
Abstract
Abstract Abstract 4918 Background Bortezomib therapeutic efficacy is well established in multiple myeloma (MM) however response to this therapy remains difficult to predict with resistant disease observed in nearly 20% of MM patients. Through DNA microarrays, predictive models of response to stem cell transplant and Bortezomib were reported correlating mRNA expression data with disease outcomes and response to therapy. MicroRNAs (miRNAs) are a key class of small, non-coding RNA molecules that modulate post-transcriptional regulation of gene expression and were recently described to be involved in deregulation of gene expression in many cancers including MM. Little evidence however is available concerning the role of miRNA expression in the prediction of response to Bortezomib in MM. We aimed to assess the expression of miRNAs in a panel of Bortezomib highly sensitive and relatively resistant MM cell lines as well as primary MM cells and identify miRNA expression patterns that are associated with response to Bortezomib. Methods We have used miRNA microarrays (Affymetrix miRNA GeneChip) as well as liquid phase Luminex microbead miRNA profiling (Flexmir, Luminex) to profile miRNA expression in MM cell lines (MM1S, KMS11, INA6, U266) and sorted CD138+ bone marrow PCs from MM patients prior to treatment with Bortezomib (n=5; 3 sensitive and 2 resistant) and PCs from a healthy normal donor (n=1). The MM cell lines included in this analysis were classified as sensitive (S) or resistant (R) based on their Bortezomib IC50 at 48 hours (IC50 for MM1S and KMS11 ∼ 5 nM versus INA6 and U266 ∼ 20nM). For the microarray studies the hybridization signal values for the multiple probes for each miRNA were obtained and normalized with the use of miRNA QC tool (Affymetrix) and analyzed using Partek Genomics Suite software. Thereafter, filters were applied to identify the miRNA probes whose normalized signal were at least 2 folds differentially expressed between sensitive (MM1S) and resistant (INA6) cell lines with a P value < 0.05 (ANOVA) and a FDR of 0.05. Bortezomib sensitive (n=3) and resistant (n=2) primary MM samples were subjected to the same miRNA array analysis and filtering. Liquid phase Luminex microbead miRNA profiling (FlexmiR) was used for the confirmation (MM1S and INA6) and validation of the array results in other MM cell lines KMS11 (IC50 5nM) and U266 (IC50 20nM). Results Using Affymetrix miRNA GeneChip we identified 22 differentially expressed miRNA with overexpression of miR-155, miR-342-3p, miR-181a and b, miR-128, miR-20b and downregulation miR-let-7b, miR-let-7i, miR-let-7d, miR-let-7c, miR-222, miR-221, miR-23a, miR-27a and miR-29a in bortezomib relatively resistant (INA6) versus sensitive (MM1S) cell line. These results were confirmed in INA6 and MM1S cells with the use of Luminex microbead miRNA profiling and validated to be similarly differentially expressed between KMS11 (sensitive) and relatively U266 (resistant) cell lines. Furthermore, TargetScan algorithms and Ingenuity Pathway Analysis software were used to identify predicted miRNAs-targeted mRNAs or potentially regulated networks and included genes involved in cell cycle regulation, cell growth, apoptosis and ubiquitin-conjugation pathways. Lastly to further investigate the clinical relevance of miRNAs in MM in terms of prediction of response and outcome to Bortezomib, we correlated miRNA expression profile of sorted CD138+ bone marrow PCs from Bortezomib sensitive (n=3) and resistant (n=2) MM patients with their response to therapy. Unsupervised analysis of the data revealed that the Bortezomib sensitive MM patients clustered with MM1S cell line while resistant patients segregated into the INA6 cluster. Conclusion In summary, we have described a MM miRNA signature, which includes miRNAs that modulate the expression of proteins critical to myeloma pathogenesis and is predictive of response to Bortezomib. Further validation of this miRNA signature in a larger cohort of Bortezomib-treated MM patients is ongoing. Disclosures Stewart: Glaxo-Smith-Kline: Research Funding.
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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 | 0.000 | 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".