Abstract B12: EIF4E deregulation drives simultaneous expression of B-cell lymphoma oncogenes.
Bibliographic record
Abstract
Abstract The eukaryotic translation initiation factor 4E (eIF4E) is an oncogene elevated in a large number of cancers. The oncogenic potential of eIF4E arises from its critical roles in the cytoplasm in the mRNA translation and in the nucleus in the mRNA export of specific subset of transcripts. These transcripts can be regulated at the cytoplasmic, nuclear export or at both levels. We therefore analyzed eIF4E expression in 105 cases of DLBCLs by IHC, and found that is expressed in almost all the tumors (88%) and for the majority of cases (72%), eIF4E was expressed in both cellular compartments. Since elevated eIF4E activity leads to increased protein production that favors a subset of transcripts including prominent DLBCL oncogenes such as MYC and BCL2, we analyzed eIF4E expression in “double-hit” and “triple hit” DLBCLs. All the pts samples and DLBCL cell lines harboring two or more oncogenic mutations expressed eIF4E at high levels, suggesting a potential mechanistic association. Cells engineered to overexpress wild-type eIF4E, or mutants active in translation but not export (S53A) or mutants active in export but not translation (W73A), confirmed that eIF4E regulates the nuclear export and translation of MYC and BCL2, and discovered that this mechanism regulates additional DLBCL oncogenes such as BCL6. EIF4E immunoprecipitation from triple-hit DLBCL nuclear fractions followed by mRNA amplification by QPCR (RIP-QPCR) determined that MYC, BCL2 and BCL6 were direct eIF4E mRNA export targets. Polysomal profiling demonstrated that these oncogenes are also preferentially translated by eIF4E cytosolic activity. Accordingly, treatment of triple-hit DLBCL cell lines with the eIF4E competitive inhibitor ribavirin significantly increased the nuclear entrapment of BCL6, MYC and BCL2 transcripts, decreased their proportion of heavy polysomes and lead to a decrease in their protein abundance. In an extended panel of six double- and triple-hit DLBCL cell lines, ribavirin induced cell killing in all of them at clinically achievable concentrations. To assess the anti-lymphoma effect of ribavirin in a more clinically relevant context, we established a patient derived xenograft (PDX) in NSG mice. The specimen was isolated from a naïve treatment patient harboring a triple-hit ABC-type DLBCL featuring BCL6 translocation (3q27), BCL2 translocation and MYC amplification. The patient presented a treatment-refractory disease. We then expanded PDX into 10 NSG mice and when tumors were palpable, mice were randomized to receive vehicle or ribavirin for 10 days. We found a significant reduction in tumor growth in PDX. We also found that ribavirin induced mRNA nuclear entrapment of BCL2 and BCL6 and decrease protein levels of BCL2, BCL6 and MYC by day 10. We demonstrated that Hsp90 activity is required to maintain eIF4E-containing heavy polysomes in these oncogene transcripts. Accordingly, Hsp90 inhibition with PU-H71 rapidly promotes ribosomal disassembly leading to increased 40S, 60S and monosomes and decreased heavy polysomes. EIF4E target transcripts such as MYC, BCL6 and BCL2 were more affected, consequently decreasing the protein levels of these transcripts. The combination of Hsp90 and eIF4E inhibition reduced more efficiently the protein levels of these transcripts. To determine whether this molecular effect translates into an improved anti-lymphoma effect, we xenografted two triple-hit DLBCL cell lines in SCID mice and we treated them with vehicle control, ribavirin, PU-H71 or the combination for 10 days. We found that the combination of ribavirin with PU-H71 suppressed lymphoma growth more profoundly in both models. This effect was associated with a decrease in proliferation rate (measured by Ki67 staining) and reduced BCL6 expression. There were no macroscopic or microscopic evidence of toxicity in these mice. In summary, our data provides a novel and potentially non-toxic mechanistically-based approach to target B-cell lymphomas harboring multiple oncogene activation. Given the prevalence of simultaneous chromosomal aberrations in other hematological malignancies we envision a wider role for this strategy. Citation Format: Biljana Kraljacic-Culkjovic, Tharu Fernando, Rebecca Goldstein, Charles Mctavish, Jayeshkumar Patel, Shaoning Yang, Fabrizio Tabbo, Ari Melnick, Giorgio Inghirami, Katherine LB Borden, Leandro Cerchietti. EIF4E deregulation drives simultaneous expression of B-cell lymphoma oncogenes. [abstract]. In: Proceedings of the AACR Special Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(17 Suppl):Abstract nr B12.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".