Copy number analysis to identify tumor suppressor genes associated with enzalutamide (Enza) resistance and poor prognosis in metastatic castration-resistant prostate cancer (mCRPC) patients.
Bibliographic record
Abstract
5011 Background: Although enza prolongs life in mCRPC pts, the development of drug resistance and subsequent disease progression is nearly universal. Seeking to clarify molecular mechanisms that underlie enza resistance, we analyzed whole genome sequencing (WGS) and RNA sequencing (seq) of tumors obtained from patients with enza-naive or -resistant mCRPC. Methods: One hundred and one men with mCRPC who underwent image-guided biopsy and subsequent WGS were included (n = 64 with enza-naive and n = 37 with enza-resistant mCRPC). The differential copy number alteration (CNA) events enriched in enza-resistant vs. naïve samples were determined, and the prognostic significance of differential CNAs was assessed. RNA-seq data were evaluated to confirm that CNAs correlated with changes in gene expression of relevant loci and to identify potentially druggable targets selectively activated in tumors with specific CNAs. Results: Copy number loss was more common than gain in enza-resistant tumors. Specifically, we identified 123 protein-coding genes that were more commonly lost in enza-resistant samples—eight of which were previously described tumor suppressor genes. There was a strong concordance of copy number loss and reduced mRNA expression of these genes. We identified one gene from this list of eight genes whose copy number loss was associated with poor overall survival (median overall survival from date of CRPC was 19.1 months in tumors with gene loss vs. 42.0 months in intact tumors, hazard ratio 3.8 [1.46–9.8], log-rank p = 0.003). Finally, Master Regulator analysis determined that tumors with copy number loss of this poor prognosis gene had activation of several potentially targetable factors, including the kinases Akt and PLK1. Conclusions: Copy number loss of specific tumor suppressor genes is associated with enza resistance in mCRPC patients. Previously unappreciated molecular subsets of enza-resistant CRPC were identified, including one subset associated with poor clinical outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".