Functional Deconvolution of the Leukemia Stem Cell Pool Uncovers Co-Existing Alternative Stem Cell Driven Hierarchies
Bibliographic record
Abstract
Leukemia stem cells (LSC) are not functionally homogenous, rather constitute a complex reservoir of multiple subtypes with distinct genetics and functional potential, that fuels disease progression in acute myeloid leukemia (AML). Understanding the intricacies within the LSC pool is critical for informed design of LSC targeting therapies. However, such in-depth studies are hampered by several technical challenges such as the scarcity of LSC and the lack of reliable cell surface markers to isolate viable LSC or distinct types of LSC. To overcome these challenges, we turned to the patient-derived OCI-AML22 model. This model includes functionally, transcriptionally, and epigenetically characterized LSC broadly representative of LSC extracted from primary AML samples. Focusing on the pool of LSC, we deconvoluted the multi-layered heterogeneity, using a panel of in vivo limiting dilution and single cell assays combined with single cell multiome analysis. We uncovered the co-existence of alternative hierarchies driven by distinct LSC subtypes that differ in quiescence depth, differentiation potential, repopulation capacities and that can be prospectively enriched. Notably, we captured the transcriptomic footprint of these newly discovered LSC subtypes within large primary AML cohorts, and even concomitantly within the same AML patient across different LSC fractions, highlighting the broad biological relevance of our findings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".