Cellular Reprogramming Allows Generation of Autologous Hematopoietic Progenitors From AML Patients That Are Devoid of Patient-Specific Genomic Aberrations
Bibliographic record
Abstract
Current treatments that use hematopoietic progenitor cell (HPC) transplantation in acute myeloid leukemia (AML) patients substantially reduce the risk of relapse, but are limited by the availability of immune compatible healthy HPCs. Although cellular reprogramming has the potential to provide a novel autologous source of HPCs for transplantation, the applicability of this technology toward the derivation of healthy autologous hematopoietic cells devoid of patient-specific leukemic aberrations from AML patients must first be evaluated. Here, we report the generation of human AML patient-specific hematopoietic progenitors that are capable of normal in vitro differentiation to myeloid lineages and are devoid of leukemia-associated aberration found in matched patient bone marrow. Skin fibroblasts were obtained from AML patients whose leukemic cells possessed a distinct, leukemia-associated aberration, and used to create AML patient-specific induced pluripotent stem cells (iPSCs). Through hematopoietic differentiation of AML patient iPSCs, coupled with cytogenetic interrogation, we reveal that AML patient-specific HPCs possess normal progenitor capacity and are devoid of leukemia-associated mutations. Importantly, in rare patient skin samples that give rise to mosaic fibroblast cultures that continue to carry leukemia-associated mutations; healthy hematopoietic progenitors can also be generated via reprogramming selection. Our findings provide the proof of principle that cellular reprogramming can be applied on a personalized basis to generate healthy HPCs from AML patients, and should further motivate advances toward creating transplantable hematopoietic stem cells for autologous AML therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".