Abstract 3972: MicroRNAs as potential therapeutic agents for AML: Targeting the AML1-ETO Oncogene by pre-miR-520 and -373
Bibliographic record
Abstract
Abstract Acute myeloid leukemia (AML) is a heterogeneous neoplasm characterized by the accumulation of poorly differentiated myeloid cells in the bone marrow and blood of patients. Differentiation therapy is an attractive therapeutic approach for treating patients with AML. All-Trans Retionic Acid, induces differentiation of patients with the PML/RARa oncogene, but is ineffective in treating other more frequent types of AML. Here we describe the use of microRNAs to target the oncogene formed as a result of the (8;21) translocation, The goals was to identify miRNAs that could target the breakpoint region of the fusion protein, and determine if this would reduce expression of AML1-ETO and promote differentiation and/or apoptosis. Bioinformatics analysis was used to identify 28 different miRNAs that could potentially target AML1-ETO transcripts. Among those, miRNA-520 and -373 showed the highest degree of complimentary to AML1-ETO transcripts at the braikpoint. To look for miR-520 and -373 efficacy, we transfected two AML cell lines, Kasumi 1 and SKNO1, which have AML1-ETO translocation, with pre-miR-520 and -373 LNA probes, as well as HL60 cells which lack AML1-ETO. Expression of the LNA pre-miR-520/373 in Kasumi-1 and SKNO-1 cell lines decreased AML1-ETO transcripts and led to a significant reduction of AML1-ETO protein levels. The inhibition of AML1-ETO fusion protein also induced apoptosis of leukemic cells in vitro, but had no effect on HL60 cells. Moreover, the administration of LNA pre-miR-520 in an AML xenografts murine model increased apoptosis of leukemic cells and reduced tumor burden without obvious toxicity. We are currently testing the effects of miR-520 and -373 on differentiation and proliferation of primary blasts from patients with the AML1-ETO translocation. Our results suggest that small molecules such as miRNAs can be identified that directly target a frequent AML oncogene. miRNAs are naturally accruing molecules and have the potential advantage of producing modest side effects, with highly significant specificity. The approaches we developed in this study can be used to evaluate and optimize other pre-miRs and anti-miRs as therapeutic agents to target other types oncogenes not currently amenable to small molecule drugs. Citation Format: Patricia A. Toniolo, Patrick M. Pilarski, Christian Bach, James D. Griffin, Sophia Adamia. MicroRNAs as potential therapeutic agents for AML: Targeting the AML1-ETO Oncogene by pre-miR-520 and -373. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3972. doi:10.1158/1538-7445.AM2015-3972
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".