Accessibility Over Transposable Elements Reveals Genetic Determinants of Stemness Properties in Normal and Leukemic Hematopoiesis v2
Bibliographic record
Abstract
Despite most acute myeloid leukemia (AML) patients achieving complete remission after induction chemotherapy, two thirds of patients will relapse with fatal disease within 5 years. AML is organized as a cellular hierarchy sustained by leukemia stem cells (LSC) at the apex, with LSC properties directly linked to tumor progression, therapy failure and disease relapse. Despite the central role of LSC in poor patient outcomes, little is known of the genetic determinants of their stemness properties. Although much AML research focuses on mutational processes and their impact on gene expression programs, the genetic determinants of cell state properties including stemness expand beyond mutations, relying on the genetic architecture captured in the chromatin of each cell. As LSCs share many functional and molecular properties with normal hematopoietic stem cells (HSC), we identified genetic determinants of primitive populations enriched for LSCs and HSCs in comparison with their downstream mature progeny by investigating their chromatin accessibility. Our work reveals how distinct transposable element (TE) subfamilies are used in primitive versus mature populations, functioning as docking sites for stem cell-associated regulators of genome topology, including CTCF, or lineage-specific transcription regulators in primitive and mature populations, respectively. We further show how TE subfamilies accessible in LSCs define docking sites for several oncogenic drivers in AML, namely FLI1, LYL1 and MEIS1. Using chromatin accessibility profiles from a cohort of AML patients, we further show the clinical utility of our TE accessibility-based LSCTE121 scoring scheme to identify patients with high rates of relapse. Collectively, our work reveals how different accessible TE subfamilies serve as genetic determinants of stemness properties in normal and leukemic hematopoietic stem cells.
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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 | 0.000 | 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".