Abstract C051: Alteration of protein translation by eIF4A1 inhibition exquisitely primes lymphoma cells to induction of ferroptosis
Bibliographic record
Abstract
Abstract Background: Ferroptosis induction as a cancer therapy faces challenges establishing clinical therapeutic windows. Cells employ diverse mechanisms to prevent iron-mediated oxidation of membrane polyunsaturated fatty acids that causes ferroptosis, but populations with low rates of protein synthesis such as hematopoietic stem cells are more susceptible. Therefore, we hypothesized protein synthesis inhibitors might enhance therapeutic ferroptosis induction. Rocaglates, inhibitors targeting eIF4A1, have entered clinical trials with promising potential against diffuse large B-cell lymphoma (DLBCL), which also exhibits pre-clinical susceptibility to ferroptosis induction. Up to 40% of patients with DLBCL, the most common lymphoma type, have poor outcomes requiring new approaches to address these persistent unmet clinical needs. Our results reveal striking synergy of rocaglates, including the clinical compound zotatifin (eFT226), with ferroptosis inducers. TMT-pSILAC and RNA-seq analyses revealed profound deregulation of ferroptotic protection resulting from rocalate effects on protein synthesis. Methods: We used RNA-seq and TMT-pSILAC followed by mass spectrometry to analyze transcriptome and protein output in SU-DHL-10 DLBCL cells treated with zotatifin or DMSO for 24h. Enrichment analyses (Enrichr platform, ChIP-X Enrichment Analysis 3, GSEA) identified differentially expressed genes, proteins and transcription factors. We explored synergy between zotatifin and ferroptosis modulators in DLBCL cell lines and measured a wide variety of associated cellular endpoints including lipid peroxidation, glutathione levels, and glutathione peroxidase 4 (GPX4) expression. Results: TMT-pSILAC translatome assessment during rocaglate exposure revealed expression alteration(log2FC ≥ 1.5) of numerous key proteins involved in oxidative stress (i.e. SDHB, SDHA, TRAP1, SCO1, UQCRH, ETFA, COX7C) and enrichment of factors indicating induction of an antioxidant response (CD98HC, NRF1, ATF2, SP2, and NFE2L). These changes led to high synergy of zotatifin in combination with a wide variety of ferroptosis inducers (Bliss δ synergy score > 10) and antagonism with ferroptosis inhibitors (Bliss δ synergy score < - 10). These results were consistent across multiple DLBCL mouse and human experimental systems, and preliminarily appear present also in models of T-cell lymphomas. Lipid peroxidation levels were significantly enhanced when zotatifin was combined with erastin (p-value < 0.0005). Moreover, zotatifin treatment resulted in elevated levels of reduced glutathione (GSH), an effect overcome by addition of ferroptosis inducers (p-value < 0.005). Consistently, GPX4 expression was also impaired. Conclusions: Rocaglates induce novel alterations in oxidative stress factors and associated response pathways, sensitizing B and T lymphomas to therapeutic ferroptosis induction. These findings provide valuable insights for using rocaglates in combination with ferroptosis induction to overcome heterogeneous resistance pathways in high-risk lymphoma patients. Citation Format: Paola Manara, Alexa Marie Barroso, Abdessamad Youssfi Alaoui, Olivia Barbara Lightfuss, Tyler Andrew Cunningham, Kyle Hoffman, Caroline Alice Coughlin, Jonathan Harry Schatz. Alteration of protein translation by eIF4A1 inhibition exquisitely primes lymphoma cells to induction of ferroptosis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C051.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".