Genomic alterations in neuroendocrine prostate cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer. We performed a systematic review and meta‐analysis to evaluate the prevalence of genomic alterations in NEPC and better understand its molecular features to potentially inform precision medicine. Methods EMBASE, PubMed, and Cochrane Central Register of Controlled Trials databases were searched for eligible studies until March 2022. Study qualities were assessed using the Q‐genie tool. The prevalence of gene mutations and copy number alterations (CNAs) were extracted, and meta‐analysis was performed using R Studio with meta package. Results A total of 14 studies with 449 NEPC patients were included in this meta‐analysis. The most frequently mutated gene in NEPC was TP53 (49.8%), and the prevalence of deleterious mutations in ATM/BRCA was 16.8%. Common CNAs in NEPC included RB1 loss (58.3%), TP53 loss (42.8%), PTEN loss (37.0%), AURKA amplification (28.2%), and MYCN amplification (22.9%). RB1/TP53 alterations and concurrent RB1 and TP53 alterations were remarkably common in NEPC, with a prevalence of 83.8% and 43.9%, respectively. Comparative analyses indicated that the prevalence of (concurrent) RB1/TP53 alterations was significantly higher in de novo NEPC than in treatment‐emergent NEPC (t‐NEPC). Conclusions This study presents the comprehensive prevalence of common genomic alterations and potentially actionable targets in NEPC and reveals the genomic differences between de novo NEPC and t‐NEPC. Our findings highlight the importance of genomic testing in patients for precision medicine and provide insights into future studies exploring different NEPC subtypes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".