CD200 expression marks leukemia stem cells in human AML
Bibliographic record
Abstract
The leukemia stem cell (LSC) populations of acute myeloid leukemia (AML) exhibit phenotypic, genetic, and functional heterogeneity that contribute to therapy failure and relapse. Progress toward understanding the mechanistic basis for therapy resistance in LSCs has been hampered by difficulties in isolating cell fractions that enrich for the entire heterogeneous population of LSCs within individual AML samples. We previously reported that CD200 gene expression is upregulated in LSC-containing AML fractions. Here, we show that CD200 is present on a greater proportion of CD45dim blasts compared with more differentiated CD45high cells in AML patient samples. In 75% (49 of 65) of AML cases we examined, CD200 was expressed on ≥10% of CD45dim blasts; of these, CD200 identified LSCs within the blast population in 9 of 10 (90%) samples tested in xenotransplantation assays. CD200+ LSCs could be isolated from CD200+ normal HSCs with the use of additional markers. Notably, CD200 expression captured both CD34- and CD34+ LSCs within individual AML samples. Analysis of highly purified CD200+ LSC-containing fractions from NPM1-mutated AMLs, which are commonly CD34-, exhibited an enrichment of primitive gene expression signatures compared with unfractionated cells. Overall, our findings support CD200 as a novel LSC marker that is able to capture the entire LSC compartment from AML patient samples, including those with NPM1 mutation.
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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 | 0.000 | 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".