Transcriptional profiling clarifies a program of enzalutamide extreme non-response in lethal prostate cancer
Bibliographic record
Abstract
The androgen receptor inhibitor enzalutamide is one of the principal treatments for metastatic prostate cancer. Most patients respond. However, a subset is primary refractory. Seeking to understand enzalutamide extreme non-response (ENR), we analyzed RNA-sequencing in biopsies from men treated prospectively on an enzalutamide clinical trial. We focused on those with ENR (progression within 3 months) vs. long-term response (progression after 24 months). We identified an ENR program linked to proliferation, epithelial-to-mesenchymal transition, and stemness. High expression of this program in additional datasets was independently linked to poor tumor control with AR targeting but favorable tumor control with docetaxel, another standard treatment. CDK2 was implicated in the ENR program. CDK2 suppression reduced the ENR program and viability of ENR program-high prostate cancer models. The ENR gene program is predictive of non-response to AR targeting. Patients whose tumors harbor this program may be good candidates for docetaxel or CDK2 inhibitor clinical trials.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".