Abstract 3330: Functional characterization of microRNAs identified in human acute myeloid leukemia stem cells
Bibliographic record
Abstract
Abstract Human acute myeloid leukemia (AML) is organized as a functional cellular hierarchy and is sustained by a rare population of leukemia stem cells (LSC). AML is a heterogeneous disease with a relapse rate of up to 80% depending on age of the patient and AML subtype. Recent work suggests that leukemia stem cell properties influence therapy response, overall survival, and relapse of the disease. In order develop more effective novel therapies that target this rare cell population; it is imperative that we better understand LSCs at the molecular level. Although it is generally accepted that oncogenic mutations underlie cancer initiation and progression, most studies have focused on protein coding genes. However, there is increasing recognition that non-coding RNAs can also play a role in leukemogenesis. MicroRNAs (miRNAs) are a family of small non-coding RNAs that function as important regulators of the translation of protein-coding genes. In order to identify LSC specific miRNAs, we fractionated 16 primary human AML samples into four sub-populations, each of which were xenotransplanted into immune-deficient mice to evaluate in vivo leukemia initiating capacity. Global miRNA expression profiling was performed on each population and a LSC specific miRNA signature generated by supervised analysis guided by the ability to initiate leukemia in vivo. Similarly, a human cord blood derived hematopoietic stem cell (HSC) enriched miRNA signature was also established. From these lists, we selected ten promising candidate miRNAs to assess for biological function. We have initiated a functional screen to determine the role of the candidate miRNAs using both in vivo and in vitro assays. Preliminary results show that enforced expression of two miRNA candidates strongly reduced engraftment capability of HSCs over untransduced HSCs in immune-deficient recipients. Moreover, enforced expression of three additional miRNA candidates show a competitive growth advantage of transduced HSCs over untransduced HSCs and compared to controls. Also, enforced expression of one of these three miRNAs in the surrogate LSCs of a unique leukemia cell line induces a strong proliferative advantage over untransduced LSCs in an in vitro culture setting. In conclusion, thus far we have identified five miRNAs that affect hematopoietic stem cell properties. Further in vivo and in vitro analysis will determine whether these miRNAs are suitable targets for therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3330. doi:1538-7445.AM2012-3330
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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 | 0.001 |
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".