Towards identification and targeting of Polycomb signaling pathways in leukemia
Bibliographic record
Abstract
The development of leukemia is a multistep process that can be caused by multiple genetic and epigenetic changes which affect normal growth and differentiation of hematopoietic stem and progenitor cells and ultimately lead to full leukemic transformation. Despite most leukemia patients initially achieving successful remission after intensive treatment, the persistence of a rare population of chemotherapy-resistant leukemic stem cells (LSCs) can result in relapse of disease in a relatively large cohort of patients. The studies presented in this thesis were aimed to identify Polycomb signaling pathways in leukemia and whether they can be used to target and eradicate LSCs. Our data suggest an essential role for non-canonical PRC1.1 in controlling distinct gene sets involved in unique cell biological processes required for the maintenance of leukemic cells. We identified that USP7 is part of non-canonical PRC1.1 and its enzymatic activity is critically important to maintain complex integrity and function. Targeting of PRC1.1 strongly reduced cell proliferation of (primary) leukemic cells in vitro and delayed leukemogenesis in vivo. Next, we aimed to obtain insights into the mechanisms by which PRC1.1 might affect transcriptional control in leukemic cells. We observed that PRC1.1 is associated with restrictive and permissive chromatin states, indicating that transcriptional control is a complicated multifactorial process and that beyond PRC1.1 other regulators play clearly important roles as well. Future work will focus on the underlying mechanisms and cross-talk with chromatin regulators and transcriptional machinery. Furthermore, we established a human leukemia mouse model which allowed us to study timing of gene knockdown on the efficacy of leukemia treatment. These findings suggested that it is critical to study gene function during the development of leukemia to find potential new targets for leukemia treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".