NRF2 translation block by inhibition of cap-dependent initiation sensitizes lymphoma cells to ferroptosis and CAR-T immunotherapy
Bibliographic record
Abstract
Cancers coopt stress-response pathways to drive oncogenesis, dodge immune surveillance, and resist cytotoxic therapies. Several of these provide protection from ferroptosis, iron-mediated oxidative cell death. Here, we found dramatic sensitization to ferroptosis upon disruption of cap-dependent translation in diffuse large B-cell lymphoma (DLBCL). Specifically, rocaglate inhibitors of the eIF4A1 RNA helicase synergized with pharmacologic ferroptosis inducers, driven by a collapse of glutathione production that protects polyunsaturated fatty acids from ferroptotic oxidation. These effects occur despite initial up-regulation of specific protective factors. We find lost translation of NRF2, oncogenic master regulator of antioxidant gene-expression, is a key consequence of eIF4A1 inhibition. In vivo, combination of the clinical rocaglate zotatifin with a pharmacologically optimized ferroptosis inducer eradicated DLBCL patient derived xenografts. Moreover, we found zotatifin pre-exposure sensitized DLBCL to CD19-directed chimeric antigen receptor (CAR-19) T cells. Translational disruption therefore provides new opportunities to leverage therapeutic impacts of ferroptosis inducers including cytotoxic immunotherapies.
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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) | 0.003 | 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".