Transcriptional profiling to identify a program of enzalutamide extreme non-response in lethal prostate cancer.
Bibliographic record
Abstract
5084 Background: The androgen receptor pathway inhibitor (ARPI) enzalutamide is one of the principal treatments for metastatic hormone-naïve and castration-resistant prostate cancer (CRPC). Most patients respond to enzalutamide. However, tumors from a subset of patients exhibit extreme non-response and are primary refractory to treatment. We sought to understand the gene expression program of enzalutamide extreme non-response (ENR) and identify alternate therapeutic approaches for tumors driven by this program. Methods: We analyzed gene expression by RNA-sequencing in pre-treatment metastatic biopsies from men with CRPC treated on a prospective enzalutamide clinical trial (NCT02099864). We focused on those with ENR (progression within 3 months) vs. long-term response (progression after 24 months) and identified a gene program linked to enzalutamide ENR. We validated the utility of this program in additional patient cohorts using a multivariable analysis and in preclinical models. Results: Unsupervised clustering correctly classified ENR patients whose tumors harbored proliferative, epithelial-to-mesenchymal transition, and stemness genes sets. Using a supervised approach, we developed a gene signature to measure the ENR program. High expression of this program in CRPC patient validation cohorts was independently associated with poor tumor control with AR targeting in multivariable analysis. Conversely, high expression of the program was independently associated with benefit with docetaxel chemotherapy, suggesting the ENR program is predictive and not merely prognostic. In support of our findings, high expression of the ENR program was strongly linked to docetaxel sensitivity in a large panel of CRPC models. Finally, we identified putative regulators of the ENR program—several of which can be targeted pharmacologically with agents that are FDA-approved or in clinical trials. Conclusions: The enza ENR program we identified is independently predictive of ENR to AR targeting. However, patients whose tumors harbor this program may be good candidates for docetaxel chemotherapy or clinical trials testing agents that block putative regulators of this program.
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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 | 0.000 |
| 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) | 0.001 | 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".