Modulation of de Novo Lipogenesis Improves Response to Enzalutamide Treatment in Prostate Cancer
Bibliographic record
Abstract
De novo lipogenesis (DNL) is now considered as a hallmark of cancer. The overexpression of key enzymes of DNL is characteristic of both primary and advanced disease and may play an important role in resistance to therapies. Here, we showed that DNL is highly enhanced in castrate resistant prostate cancer (CRPC) cells compared to hormone sensitive and enzalutamide resistant cells. This observation suggests that this pathway plays an important role in the initiation of aggressive prostate cancer and in the development of enzalutamide resistance. Importantly, here we show that both prostate cancer cells sensitive and resistant to enzalutamide are dependent on DNL to proliferate. We next combined enzalutamide with an inhibitor of Stearoyl CoA Desaturase 1 (SCD1), an important enzyme in DNL, and observed significantly reduced tumor growth caused by the important change in tumoral lipid desaturation. Our findings suggest that the equilibrium between monounsaturated fatty acids and saturated fatty acids is essential in the establishment of the more aggressive prostate cancer phenotype and that the combination therapy induces a disruption of this equilibrium leading to an important decrease of cell proliferation. These findings provide new insights into the role of DNL in the progression of prostate cancer cells. The study also provides the rationale for the use of an inhibitor of SCD1 in combination with enzalutamide to improve response, delay enzalutamide resistance and improve disease free progression.
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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".