Heterogeneity of Acute Myeloid Leukemia at the Stem Cell Level.
Bibliographic record
Abstract
Abstract Leukemias are considered hierarchically organized being maintained by a leukemia stem cell (LSC). Whereas LSCs become the primary focus for targeted therapies, little is known about the pathways regulating LSC fidelity. Using retroviral gene transfer of MN1, NUP98HOXD13 (ND13), or HOXA9 oncogenes and limiting-dilution transplantation we modelled leukemias with different LSC frequencies, and characterized critical signaling pathways by loss-of-function analysis. Here we establish the concept that LSCs are heterogeneous based on the number of activated transcriptional networks, and functionally characterize downstream targets that are critical for LSC activity. Constitutive expression of the very potent myeloid oncogene MN1 with the ND13 fusion gene in murine bone marrow cells results in acute myeloid leukemia (AML) that is phenotypically very similar to MN1-induced AML. However, limiting dilution analysis showed that the LSC frequency was 33 fold higher in MN1+ND13 cells compared to MN1 cells, and disease latency at the limiting dilution was significantly shorter in the combination model (p=.009). Whereas MN1-LSCs expanded 68-fold over a period of 6 days, MN1+ND13-LSCs expanded 131-fold more than MN1-LSCs as determined by the competitive repopulation unit (CRU) assay. To screen for functional differences of the two models we screened for differential cytokine responses in vitro. Interestingly, MN1+ND13 expressing cells proliferated in response to GM-CSF, whereas MN1 cells or ND13 cells did not. This was confirmed as well for MN1+HOXA9 expressing cells and their MN1+CTL or HOXA9+CTL expressing counterparts. We found that Stat1, Stat3, Stat5, and Erk1/2 were selectively phosphorylated upon cytokine stimulation in MN1+ND13 and MN1+HOXA9 cells compared to single-oncogene transduced cells. To test the role of Stat1 and Stat5b for LSC fidelity Stat1 −/− and Stat5b −/− cells were co-transduced with MN1 and HOXA9 and compared to wildtype cells in vitro and in vivo. Stat1 −/− cells transduced with MN1+HOXA9 proliferated slower than wildtype cells in response to GM-CSF but not with IL3/IL6/SCF. Proliferation of Stat5b −/− cells transduced with MN1+HOXA9 proliferated slower than wildtype cells in response to both GM-CSF and IL3/IL6/SCF (p<.05). CRU assays with MN1+HOXA9-transduced Stat1 −/− and Stat5b −/− cells demonstrated that the day 6 CRU was 6 and 77 fold reduced, respectively, compared to wildtype cells. As MN1 and HOXA9 are upregulated in distinct subsets of normal karyotype AML we speculated that their combined overexpression may model subsets of complex karyotype AML. We performed gene set enrichment analysis on cytogenetic subsets of previously published gene expression data from 285 AML patients. 12 of 13 Stat-related pathways were enriched in complex karyotype patients compared to 4 and 8 of 13 Stat-related pathways in inv(16) and normal karyotype AML, respectively, thus supporting a critical role of Stat activation in LSCs of AML with multiple active pathways like complex karyotype AML. In conclusion we demonstrate considerable heterogeneity of LSC fidelity depending on the number of activated oncogenes and establish a critical role of Stat1 and Stat5b in mediating this LSC fidelity. Stat1 and Stat5b may become important therapeutic targets in complex karyotype AML.
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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".