ULK2 Is a Key Pro-Autophagy Protein That Contributes to the High Chemoresistance and Disease Relapse in FLT3-Mutated Acute Myeloid Leukemia
Bibliographic record
Abstract
We recently demonstrated that a small subset of cells in FLT3-mutated acute myeloid leukemia (AML) cell lines exhibit SORE6 reporter activity and cancer stem-like features including chemoresistance. To study why SORE6+ cells are more chemoresistant than SORE6− cells, we hypothesized that these cells carry higher autophagy, a mechanism linked to chemoresistance. We found that cytarabine (Ara-C) induced a substantially higher protein level of LC3B-II in SORE6+ compared to SORE6− cells. Similar observations were made using a fluorescence signal-based autophagy assay. Furthermore, chloroquine (an autophagy inhibitor) sensitized SORE6+ but not SORE6− cells to Ara-C. To decipher the molecular mechanisms underlying the high autophagic flux in SORE6+ cells, we employed an autophagy oligonucleotide array comparing gene expression between SORE6+ and SORE6− cells before and after Ara-C treatment. ULK2 was the most differentially expressed gene between the two cell subsets. To demonstrate the role of ULK2 in conferring higher chemoresistance in SORE6+ cells, we treated the two cell subsets with a ULK1/2 inhibitor, MRT68921. MRT68921 significantly sensitized SORE6+ but not SORE6− cells to Ara-C. Using our in vitro model for AML relapse, we found that regenerated AML cells contained higher ULK2 expression compared to pretreated cells. Importantly, inhibition of ULK2 using MRT68921 prevented in vitro AML relapse. Lastly, using pretreatment and relapsed AML patient bone marrow samples, we found that ULK2 expression was higher in relapsed AML. To conclude, our results supported the importance of autophagy in the relapse of FLT3-mutated AML and highlighted ULK2 in this context.
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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".