Mir-34a Sensitizes Multiple Myeloma (MM) Cells to the Proteasome Inhibitor Bortezomib
Bibliographic record
Abstract
Abstract Abstract 138 Background: miRNA are small non-coding RNAs that regulate mRNA at the post-transcriptional level and their deregulation contributes to the initiation and progression of several cancers. In MM, an association between high-risk disease and global elevation of miRNA expression has been reported. In addition, miRNAs involvement in the clonal evolution of plasma cells from MGUS to MM stages and correlation with specific MM molecular subgroups were also described. Therapeutically, overexpression of selected miRNAs are shown to reduce MM cells growth and viability. In the current study, we have investigated the role miRNAs play in MM cells sensitivity to the proteasome inhibitor Bortezomib. Methods and results: Microarray profiling, followed by short stem loop-PCR confirmation, of miRNA signatures of MM cell lines with differential sensitivity to bortezomib (3 fold δ in IC50) identified 24 differentially expressed miRNAs and clustered MM cell lines into 2 distinct groups. In particular, mir-34a had high expression in sensitive (S: MM1S, RPMI8226, NCI-H929 and U266 with an IC50 < 1.25nM), in comparison to relatively resistant cells (R: KMS11, INA6 and OPM2 with an IC50 ∼ 5nM). In all cell lines with low miR-34a expression, epigenetic silencing of miR-34a through its promoter methylation was identified with bisulfite conversion based PCR and treatment with the demethylating agent 5'-Azacytidine did upregulate miR34a expression. Furthermore, and since p53 is reported to activate miR-34a expression, we have correlated miR-34a levels with the TP53 mutational status and del17p.13 (by FISH) in MM cells. Mutations in exons 5 and 8 of TP53 were identified in all cell lines with low miR-34a. In particular del17p.13 by FISH was noted in KMS11 cells, consistent with their very low or undetectable miR-34a. We next sought to determine if restoration of mir-34a levels would increase sensitivity to bortezomib. While lentivirus-mediated stable expression of miR-34a in KMS11 and OPM2 cells had a minimal effect on their viability (∼ 5% increase in Annexin V staining compared to infection with mock lentiviral control), it significantly increased (∼ 20% increase in Annexin V staining) their sensitivity to bortezomib. Using western blot analysis, we confirmed that several predicted targets of miR-34a are altered with a dramatic reduction in Bcl-2, CDK4, CDK6, CEBPα and YY1 in cells infected with lentivirus expressing miR-34a. Lastly, we examined in vivo in SCID mice xenografted with KMS11 cells expressing miR-34a (KMS11-34a) or empty vector (KMS11-EV) the effects of miR-34a on tumor growth and response to bortezomib treatment. The growth kinetics of KMS11-34a xenografts was significantly slower compared to that of KMS11-EV (tumor volumes 1000 mm3 vs 2000 mm3 respectively on day 10 post KMS11 cells implantation) and this effects was even more evident in bortezomib treated mice (tumor volume 3000 mm3 on day 14 in KMS11-EV versus 1200 mm3 in KMS11-34a on day 14). Conclusion: TP53 loss/mutation or epigenetic silencing through promoter methylation reduces miR-34a expression in MM cells. Conversely, restored miR-34a expression downregulates Bcl2, CDK4/6 and YY1 and sensitizes MM cells to bortezomib. These results support a potential therapeutic role for miR-34a in MM. Disclosures: Neri: Celgene: Honoraria, Research Funding. Bahlis:Celgene: Honoraria, Speakers Bureau.
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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".