Abstract 2819: Metabolic determinants of ferroptosis in B-cell malignancies
Bibliographic record
Abstract
Abstract Although the development of targeted approaches has been effective for the treatment of B-cell malignancies, outcomes remain poor in the relapsed and refractory settings. To expand the portfolio of B-cell-selective drugs, we developed an interactive computational tool (lymphoblasts.org) and identified ferroptosis, a form of cell death driven by iron-dependent membrane lipid peroxidation, as a previously unrecognized selective vulnerability in B-cell malignancies. Although ferroptosis has shown potential in therapy-resistant tumors, no potent ferroptosis inducers are available clinically, and a better understanding of the factors regulating ferroptosis is required to allow its therapeutic targeting and the development of clinical grade ferroptosis inducers. Our comparative analyses of CRISPR dependency (DepMap) and drug (CTD, GDSC) screens revealed that B-cell malignancies are particularly sensitive to ferroptosis and depend on several anti-ferroptotic molecules that notably include components of the glutathione synthesis machinery (GCLC, GCLM, GSS) and selenoprotein synthesis (SEPHS2, LRP8). Strikingly, this analysis also identified pro-ferroptotic genes involved in iron (TFRC) and polyunsaturated fatty acid (PUFA; ACSL4) metabolism as selective B-cell dependencies, suggesting that addiction to these pro-ferroptotic processes makes B-cell malignancies intrinsically vulnerable to ferroptosis. Accordingly, iron chelation treatment showed a much higher toxicity in B-cell malignancies compared to solid tumors, and Cre-mediated ablation of Tfrc in murine models of B-cell acute lymphoblastic leukemia (B-ALL) and B-cell lymphoma induced rapid cell death. Cre-mediated loss of ACSL4, while making cells almost completely resistant to ferroptosis, significantly compromised murine B-ALL cell viability, suggesting a key role of ACSL4 and PUFA in B-cell malignancy survival. An important role of ACSL4 and PUFA metabolism in B-cell malignancies was also suggested by analysis of clinical data from the diffuse large B-cell lymphoma MMMLNP trial, which revealed that greater than median expression of ACSL4 is associated with significantly worse survival (p = 0.0007).To uncover new ferroptotic regulators in B-cell malignancies, we performed whole-genome CRISPR knockout screens under the selective pressure of the ferroptosis inducers RSL3, Erastin, and FINO2, which highlighted several known ferroptosis regulators and uncovered new ferroptosis-related genes and pathways including sphingolipid metabolism (KDSR), phosphatidylcholine synthesis (FLVCR1, PCYT1A), inositol metabolism (ITPK1, IPMK), and lipid membrane remodelling (ATP8B2). Taken together, our findings uncover iron and PUFA metabolism as selective B-cell dependencies that likely contribute to their exquisite sensitivity to ferroptosis and identifies novel regulators of ferroptosis in B-cell malignancies. Citation Format: Etienne Leveille, Eden Bramson, Mark Robinson, Thierry Bertomeu, Andrew Chatr-Aryamontri, Shalin Kothari, Markus Müschen. Metabolic determinants of ferroptosis in B-cell malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2819.
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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) | 0.006 | 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".