Brief Report: Human Acute Myeloid Leukemia Reprogramming to Pluripotency Is a Rare Event and Selects for Patient Hematopoietic Cells Devoid of Leukemic Mutations
Bibliographic record
Abstract
Induced pluripotent stem cell reprogramming has provided critical insights into disease processes by modeling the genetics and related clinical pathophysiology. Human cancer represents highly diverse genetics, as well as inter- and intra-patient heterogeneity, where cellular model systems capable of capturing this disease complexity would be invaluable. Acute myeloid leukemia (AML) represents one of most heterogeneous cancers and has been divided into genetic subtypes correlated with unique risk stratification over the decades. Here, we report our efforts to induce pluripotency from the heterogeneous population of human patients that represents this disease in the clinic. Using robust optimized reprogramming methods, we demonstrate that reprogramming of AML cells harboring leukemic genomic aberrations is a rare event with the exception of those with de novo mixed-lineage leukemia (MLL) mutations that can be reprogrammed and model drug responses in vitro. Our findings indicate that unlike hematopoietic cells devoid of genomic aberrations, AML cells harboring driver mutations are refractory to reprogramming. Expression of MLL fusion proteins in AML cells did not contribute to induced reprogramming success, which continued to select for patient derived cells devoid of AML patient-specific aberrations. Our study reveals that unanticipated blockades to achieving pluripotency reside within the majority of transformed AML patient cells. Stem Cells 2017;35:2095-2102.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".