Prostate cancer resistance leads to a global deregulation of translation factors and unconventional translation
Bibliographic record
Abstract
Abstract Emerging evidence associates translation factors and regulators to tumorigenesis. However, our understanding of translational changes in cancer resistance is still limited. Here, we generated an enzalutamide-resistant prostate cancer (PCa) model, which recapitulated key features of clinical enzalutamide-resistant PCa. Using this model and poly(ribo)some profiling, we investigated global translation changes that occur during acquisition of PCa resistance. We found that enzalutamide-resistant cells exhibit an overall decrease in mRNA translation with a specific deregulation in the abundance of proteins involved in mitochondrial processes and in translational regulation. However, several mRNAs escape this translational downregulation and are nonetheless bound to heavy polysomes in enzalutamide-resistant cells suggesting active translation. Moreover, expressing these corresponding genes in enzalutamide-sensitive cells promotes resistance to enzalutamide treatment. We also found increased association of long non-coding RNAs (lncRNAs) with heavy polysomes in enzalutamide-resistant cells, suggesting that some lncRNAs are actively translated during enzalutamide resistance. Consistent with these findings, expressing the predicted coding sequences of known lncRNAs JPX, CRNDE and LINC00467 in enzalutamide-sensitive cells drove resistance to enzalutamide. Taken together, this suggests that aberrant translation of specific mRNAs and lncRNAs is a strong indicator of PCa enzalutamide resistance, which points towards novel therapeutic avenues that may target enzalutamide-resistant PCa.
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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".