Canonical and Nuclear mTOR Specify Distinct Transcriptional Programs in Androgen-Dependent Prostate Cancer Cells
Bibliographic record
Abstract
mTOR is a serine/threonine kinase that controls prostate cancer cell growth in part by regulating gene programs associated with metabolic and cell proliferation pathways. mTOR-mediated control of gene expression can be achieved via phosphorylation of transcription factors, leading to changes in their cellular localization and activities. mTOR also directly associates with chromatin in complex with transcriptional regulators, including the androgen receptor (AR). Nuclear mTOR (nmTOR) has been previously shown to act as a transcriptional integrator of the androgen signaling pathway in association with the chromatin remodeling machinery, AR, and FOXA1. However, the contribution of cytoplasmic mTOR (cmTOR) and nmTOR and the role played by FOXA1 in this process remains to be explored. Herein, we engineered cells expressing mTOR tagged with nuclear localization and export signals dictating mTOR localization. Transcriptome profiling in AR-positive prostate cancer cells revealed that nmTOR generally downregulates a subset of the androgen response pathway independently of its kinase activity, while cmTOR upregulates a cell cycle-related gene signature in a kinase-dependent manner. Biochemical and genome-wide transcriptomic analyses demonstrate that nmTOR functionally interacts with AR and FOXA1. Ablation of FOXA1 reprograms the nmTOR cistrome and transcriptome of androgen responsive prostate cancer cells. This works highlights a transcriptional regulatory pathway in which direct interactions between nmTOR, AR and FOXA1 dictate a combinatorial role for these factors in the control of specific gene programs in prostate cancer cells. IMPLICATIONS: The finding that canonical and nuclear mTOR signaling pathways control distinct gene programs opens therapeutic opportunities to modulate mTOR activity in prostate cancer cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".