A novel isoflavone, ME-344, targets the cytoskeleton in acute myeloid leukemia
Bibliographic record
Abstract
// Danny V. Jeyaraju 1 , Rose Hurren 1 , Xiaoming Wang 1 , Neil MacLean 1 , Marcela Gronda 1 , Aisha Shamas-Din 1 , Mark D. Minden 1 , Guri Giaever 2 , Aaron D. Schimmer 1 1 Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada 2 Department of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada Correspondence to: Aaron D. Schimmer, email: aaron.schimmer@utoronto.ca Keywords: tubulin, leukemia, synergy, mitochondria, reactive oxygen species Received: October 19, 2015 Accepted: June 26, 2016 Published: July 06, 2016 ABSTRACT The isoflavone ME-344 is a potent anti-cancer agent with preclinical and clinical efficacy in solid tumors. Yet, the mechanism of action of ME-344 has not been fully defined and the preclinical efficacy in leukemia has not been established. Therefore, we investigated the anti-leukemic properties and mechanism of action of ME-344. In a panel of 7 leukemia cell lines, ME-344 was cytotoxic with an IC 50 in the range of 70–260 nM. In addition, ME-344 was cytotoxic to primary AML patient samples over normal hematopoietic cells. In an OCI-AML2 xenograft model, ME-344 reduced tumor growth by up to 95% of control without evidence of toxicity. Mechanistically, ME-344 increased mitochondrial ROS generation in leukemic cells. However, antioxidant treatment did not rescue cell death, suggesting that ME-344 had additional targets beyond the mitochondria. We demonstrated that ME-344 inhibited tubulin polymerization by interacting with tubulin near the colchicine-binding site. Furthermore, inhibition of tubulin polymerization was functionally important for ME-344 induced death. Finally, we showed that ME-344 synergizes with vinblastine in leukemia cells. Thus, our study demonstrates that ME-344 displays preclinical efficacy in leukemia through a mechanism at least partly related to targeting tubulin polymerization.
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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".