Abstract 5349: Discovery of retinoic acids as low affinity inhibitors of the leukemia stem cell target NR2F6
Bibliographic record
Abstract
Abstract Many human cancers are composed of heterogeneous populations of cells. While most cancer cells have a limited ability to divide, growth of the tumor is carried out by a small population of cancer stem cells, and hence represent the true target of effective anti-cancer therapy. To identify potential therapeutic targets, we previously studied the molecular signature of leukemia stem cells. Our work has led to the discovery of the orphan nuclear receptor NR2F6 (EAR-2) as a potential leukemia stem cell therapeutic target. We have previously shown that NR2F6 is over expressed in patients with acute leukemia and related bone marrow cancers, and that exogenous expression of NR2F6 in mouse bone marrow cells causes acute leukemia in vivo. Furthermore, silencing of NR2F6 expression in human and mouse-leukemia cell lines causes terminal differentiation and death by apoptosis. The discovery of the role of NR2F6 in leukemogenesis and the effects of silencing NR2F6 expression on leukemia cells suggests that this protein is a logical candidate as a therapeutic target in acute leukemia and related disorders for differentiation therapy. This forms the basis of the therapeutic concept that we wished to develop further, by identifying small molecule modulators of NR2F6 function. We hence developed a screening tool that was used to survey the ability of known nuclear receptor ligands to modulate NR2F6 activity. We constructed a hybrid receptor, comprising the NR2F6 ligand binding domain fused to the Gal4 DNA-binding domain. The hybrid receptor was co-transfected into HeLa cells along with a reporter plasmid in which a Gal4 regulatory sequence was placed upstream of a luciferase gene running off a constitutive promoter. The transfected cells were treated with candidate ligands and assayed for luciferase activity by luminometer. Of the 20 nuclear receptor ligands assessed none showed a significant effect at low concentrations (up to 1μM). However, we identifying 9-cis retinoic acid and, to a lesser extent, all-trans retinoic as low affinity ligands for NR2F6 since they were able to show a dose-dependent effect that we began to observe at 20μM. Retinoic acid and its analogues have long been studied in the context of chemoprevention and differentiation therapy based on their anti-proliferative, pro-apoptotic, and ability to induce differentiation. We have now commenced the search for a high affinity small molecular inhibitor of NR2F6 activity by screening commercially available libraries that contain compounds with similar chemical structure to retinoic acid using the luciferase-based assay developed here. Citation Format: Christine V. Ichim, Lap Shu Alan Chan, Dzana Dervovic, David Koos, Richard A. Wells, Thomas Ichim. Discovery of retinoic acids as low affinity inhibitors of the leukemia stem cell target NR2F6. [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 5349. doi:10.1158/1538-7445.AM2015-5349
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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 | 0.001 |
| 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".