Apoliprotein E-mediated ferroptosis controls cellular proliferation in chronic lymphocytic leukemia
Bibliographic record
Abstract
Unraveling vulnerabilities in chronic lymphocytic leukemia (CLL) represents a key approach to understand molecular basis for its indolence and a path toward developing tailored therapeutic approaches. In this study, we found that CLL cells are particularly sensitive to the inhibitory action of abundant serum protein, apolipoprotein E (ApoE). Physiological concentrations of ApoE affect CLL cell viability and inhibit CD40-driven proliferation. Transcriptomics of ApoE-treated CLL cells revealed a signature of redox and metal disbalance which prompted us to explore the underlying mechanism of cell death. We discover, on one hand, that ApoE treatment of CLL cells induces lipid peroxidation and ferroptosis. On the other hand, we find that ApoE is a copper-binding protein and that intracellular copper regulates ApoE toxicity. ApoE regulation tends to be lost in aggressive CLL. CLL cells from patients with high leukocyte counts are less sensitive to ApoE inhibition, while resistance to ApoE is possible in transformed CLL cells from patients with Richter syndrome (RS). Nevertheless, both aggressive CLL and RS cells maintain sensitivity to drug-induced ferroptosis. Our findings suggest a natural suppression axis that mediates ferroptotic disruption of CLL cell proliferation, building up the rationale for choosing ferroptosis as a therapeutic target in CLL and RS.
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) | 0.000 | 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 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".