Identification of leukemia stem cell subsets with distinct transcriptional, epigenetic and functional properties
Bibliographic record
Abstract
Abstract The leukemia stem cell (LSC) compartment is a complex reservoir fueling disease progression in acute myeloid leukemia (AML). The existence of heterogeneity within this compartment is well documented but prior studies have focused on genetic heterogeneity without being able to address functional heterogeneity. Understanding this heterogeneity is critical for the informed design of therapies targeting LSC, but has been hampered by LSC scarcity and the lack of reliable cell surface markers for viable LSC isolation. To overcome these challenges, we turned to the patient-derived OCI-AML22 cell model. This model includes functionally, transcriptionally and epigenetically characterized LSC broadly representative of LSC found in primary AML samples. Focusing on the pool of LSC, we used an integrated approach combining xenograft assays with single-cell analysis to identify two LSC subtypes with distinct transcriptional, epigenetic and functional properties. These LSC subtypes differed in depth of quiescence, differentiation potential and repopulation capacity and could be isolated based on CD112 expression. A majority of AML patient samples had transcriptional signatures reflective of either LSC subtype, and some even showed coexistence within an individual sample. This work provides a framework for further investigation of the LSC compartment in 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".