Data from KIF15-Mediated Stabilization of AR and AR-V7 Contributes to Enzalutamide Resistance in Prostate Cancer
Bibliographic record
Abstract
Abstract The new generation androgen receptor (AR) pathway inhibitor enzalutamide can prolong the survival of patients with metastatic prostate cancer. However, resistance to enzalutamide inevitably develops in these patients, and the underlying mechanisms of this resistance are not fully defined. Here we demonstrate that the kinesin family member 15 (KIF15) contributes to enzalutamide resistance by enhancing the AR signaling in prostate cancer cells. KIF15 directly bound the N-terminus of AR/AR-V7 and prevented AR/AR-V7 proteins from degradation by increasing the protein association of ubiquitin-specific protease 14 (USP14) with AR/AR-V7. In turn, the transcriptionally active AR stimulated KIF15 expression. KIF15 inhibitors alone or in combination with enzalutamide significantly suppressed enzalutamide-resistant prostate cancer cell growth and xenograft progression. These findings highlight a key role of KIF15 in enabling prostate cancer cells to develop therapy resistance to enzalutamide and rationalize KIF15 as a potential therapeutic target. Significance: These findings demonstrate how reciprocal activation between KIF15 and AR contributes to enzalutamide resistance in prostate cancer and highlights cotargeting KIF15 and AR as a therapeutic strategy for these tumors.
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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 | 0.001 |
| 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) | 0.009 | 0.002 |
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".