Abstract 1031: Translatome and protein synthesis machinery remodeling by rocaglates drives antitumor activity
Bibliographic record
Abstract
Abstract Rocaglates are under active development for cancer therapy, with the first-in-class member zotatifin recently entering Phase I/II clinical trial for advanced solid tumor malignancies (NCT04092673). Initiation of this trial was based purely on rocaglates' inhibitory activity against translation initiation factor and RNA helicase eIF4A. Rapid development of these compounds towards clinical use urgently requires a complete understanding of their mechanisms. Critically, however, the full effects of rocaglates on protein synthesis are unknown, especially with regards to up-regulated proteins and rocaglate-activated translation factors that mediate their potent anticancer activity. Past rocaglate studies focused exclusively on mechanisms and targets of translational inhibition, relying exclusively on indirect RNA-based approaches that do not reliably capture proteome-level changes, a caveat proven in cancer cells responding to drugs. Formal proteomic investigations of rocaglate effects are non-existent in the literature. We addressed these vital knowledge gaps in this study. An original proteome-level analysis using metabolic pulse-labeling and TMT-mass spectrometry revealed extensive rocaglate-induced changes in protein output (translatome), including myriad rocaglate-up-regulated proteins that drive cytotoxicity through currently uncharacterized mechanisms. Specifically, we find in vitro and in vivo that rocaglates, including zotatifin, induce GEF-H1, activating anti-survival RHOA/JNK signaling. Intriguingly, these responses occur regardless of eIF4A expression. Beyond translatome remodeling, mass spectrometry-based MATRIX interrogation of the protein synthesis machinery revealed widespread rocaglate-dependent changes. As a prime example, rocaglate-augmented eEF1ϵ1 activity mediates initiator methionine-tRNAiMet delivery to drive synthesis of rocaglate-inducible proteins and phenotypes. Overall, this study transforms the current definition of rocaglates from one-dimensional eIF4A inhibitors to translation remodelers that globally reprogram protein output and the translation machinery. As human patients begin treatment with these compounds, our discovery of rocaglate-inducible proteins and rocaglate-activated translation factors that drive drug-specific responses highlights an entirely uncharacterized yet critical aspect of rocaglate potency. These timely findings emphasize systemic translational modeling as a newer concept for cancer therapy, and advocate for a systematic overhaul in our understanding of drugs traditionally defined as translation inhibitors. Citation Format: Jyun David Ho, Jr, Tyler A. Cunningham, Caroline A. Coughlin, Artavazd Arumov, Evan R. Roberts, Daniel Bilbao, Jonathan R. Krieger, Stephen Lee, Jonathan H. Schatz. Translatome and protein synthesis machinery remodeling by rocaglates drives antitumor activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1031.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".