Reprogramming of Acute Myeloid Leukemia Patients Cells: Harboring Cancer Mutations Requires Targeting of AML Hierarchy
Bibliographic record
Abstract
Screening of primary patient acute myeloid leukemia (AML) cells is challenging based on intrinsic characteristics of human AML disease and patient-specific conditions required to sustain AML cells in culture. This is further complicated by inter- and intra-patient heterogeneity, and "contaminating" normal cells devoid of molecular AML mutations. Derivation of induced pluripotent stem cells (iPSCs) from human somatic cells has provided approaches for the development of patient-specific models of disease biology and has recently included AML. Although reprogramming patient-derived cancer cells to pluripotency allows for aspects of disease modeling, the major limitation preventing applications and deeper insights using AML-iPSCs is the rarity of success and limited subtypes of AML disease that can be captured by reprogramming to date. Here, we tested and refined methods including de novo, xenografting, naïve versus prime states and prospective isolation for reprogramming AML cells using a total of 22 AML patient samples representing the wide variety of cytogenetic abnormalities. These efforts allowed us to derive genetically matched healthy control (isogenic) lines and capture clones found originally in patients with AML. Using fluorescently activated cell sorting, we revealed that AML reprogramming is linked to the differentiation state of diseased tissue, where use of myeloid marker CD33 compared to the stem cell marker, CD34, reduces reprogramming capture of AML+ clones. Our efforts provide a platform for further optimization of AML-iPSC generation, and a unique library of iPSC derived from patients with AML for detailed cellular and molecular study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".