Metabolic Determinants of Ferroptosis in B-Cell Lymphoma
Bibliographic record
Abstract
Background and Significance: Approaches for targeted elimination of B-cells as the root-cause of disease have been highly effective in the treatment of B-cell lymphoma (rituximab, ibrutinib). To expand the portfolio of B-cell-selective drugs, we developed an interactive computational tool (www.lymphoblasts.org) and identified ferroptosis previously unrecognized selective vulnerability in B-cell lymphomas. Ferroptosis, a non-apoptotic form of cell death driven by iron-dependent membrane lipid peroxidation, has shown therapeutic potential in highly therapy-resistant tumors. However, no potent ferroptosis inducers are available clinically, and genetic ablation of Gpx4, the main anti-ferroptotic defense, leads to lethal renal and neurological toxicities in mice. A better understanding of the factors regulating ferroptosis is therefore required to allow its therapeutic targeting and the development of clinical grade ferroptosis inducers. Results : Compound screening data (CTD, GDSC), together with gene-dependency scores from CRISPR and RNAi screens, were re-analyzed and integrated for B-ALL/mature B-cell lymphomas compared to myeloid leukemia and solid tumors. Most notably, among 4,518 compounds screened, we identified 5 top-ranking compounds that converge on targeting the ferroptosis pathway: Based on our computational approach (www.lymphoblasts.org), we identified ferroptosis inducers as particularly lethal to B-cell lymphoma compared to other tumor types. This was the case for multiple classes of ferroptosis inducers, including GPX4 inhibitors (RSL3, ML162, ML210), the cysteine-glutamate antiporter (system xc-, SLC7A11) inhibitor Erastin, and the iron oxidizer and GPX4 inactivator FINO2. Analysis of gene dependency in 89 B-cell lymphoma compared to 779 myeloid leukemia and solid tumor cell lines based on CRISPR and RNAi data (DepMap) revealed a selective dependence of B-cell lymphomas on anti-ferroptotic molecules. These genes notably include components of the glutathione synthesis machinery (GCLC, GCLM, GSS) and selenoprotein synthesis (SEPH2, LRP8, SELENOI). Interestingly, B-cell malignancies also showed marked dependence on molecules that sensitize to ferroptosis, including the transferrin receptor (TFRC; iron import) and ACSL4 (polyunsaturated fatty acid, PUFA). This suggests that B-cells might be addicted to iron- and PUFA-metabolism that make them intrinsically vulnerable to ferroptosis. Accordingly, iron chelation treatment showed a much higher toxicity in B-cell lymphoma compared to solid tumors. Based on transduction of mature splenic B-cells from Tfrc-fl/fl mice with MYC, BCL2 and dominant-negative p53 as B-cell lymphoma model, we confirmed that Cre-mediated ablation of Tfrc induced rapid cell death, selectively in mature B-lymphoma. In addition, analysis of clinical data from the DLBCL MMMLNP trial cohort revealed that greater than median expression of ACSL4 is associated with significantly worse survival (P=0.003). The opposite was seen with ACSL3 (P=0.0004), which counteracts ACSL4-dependent PUFA-metabolism. Combination of these two gene expression profiles strongly predicted prognosis, as individuals with the ACSL4high/ACSL3low gene expression signature had much higher mortality than those with the ACSL4low/ACSL3high signature (hazard ratio for mortality by log-rank test: 2.484, 95% confidence interval: 1.709 - 3.609). Whole-genome CRISPR screens in B-cell malignancies under the selective pressure of RSL3, Erastin, and FINO2 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). Conclusions: Integrating CRISPR dependency and drug sensitivity data revealed that B-cell lymphomas are uniquely sensitive to ferroptosis and highly depend on anti-ferroptotic defense mechanisms. This likely reflects the intrinsic dependency of B-cell lymphomas on iron import and PUFA-metabolism, at the cost of increased vulnerability to ferroptosis. Furthermore, our CRISPR screens performed with the ferroptosis inducers RSL3, Erastin, and FINO2 revealed phosphatidylcholine synthesis (FLVCR1, PCYT1A) and lipid membrane remodelling (ATP8B2) as central mechanistic targets in B-cell lymphomas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".