The Imipridone ONC213 Targets α-Ketoglutarate Dehydrogenase to Induce Mitochondrial Stress and Suppress Oxidative Phosphorylation in Acute Myeloid Leukemia
Bibliographic record
Abstract
Eradication of acute myeloid leukemia (AML) is therapeutically challenging; many patients succumb to AML despite initially responding to conventional treatments. Here, we showed that the imipridone ONC213 elicits potent antileukemia activity in a subset of AML cell lines and primary patient samples, particularly in leukemia stem cells, while producing negligible toxicity in normal hematopoietic cells. ONC213 suppressed mitochondrial respiration and elevated α-ketoglutarate by suppressing α-ketoglutarate dehydrogenase (αKGDH) activity. Deletion of OGDH, which encodes αKGDH, suppressed AML fitness and impaired oxidative phosphorylation, highlighting the key role for αKGDH inhibition in ONC213-induced death. ONC213 treatment induced a unique mitochondrial stress response and suppressed de novo protein synthesis in AML cells. Additionally, ONC213 reduced the translation of MCL1, which contributed to ONC213-induced apoptosis. Importantly, a patient-derived xenograft from a relapsed AML patient was sensitive to ONC213 in vivo. Collectively, these findings support further development of ONC213 for treating AML. SIGNIFICANCE: In AML cells, ONC213 suppresses αKGDH, which induces a unique mitochondrial stress response, and reduces MCL1 to decrease oxidative phosphorylation and elicit potent antileukemia activity. See related commentary by Boët and Sarry, p. 950.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".