The diverse heterogeneity of molecular alterations in prostate cancer identified through next-generation sequencing
Bibliographic record
Abstract
Prostate cancer is a leading cause of global cancer-related death but attempts to improve diagnoses and develop novel therapies have been confounded by significant patient heterogeneity. In recent years, the application of next-generation sequencing to hundreds of prostate tumours has defined novel molecular subtypes and characterized extensive genomic aberration underlying disease initiation and progression. It is now clear that the heterogeneity observed in the clinic is underpinned by a molecular landscape rife with complexity, where genomic rearrangements and rare mutations combine to amplify transcriptomic diversity. This review dissects our current understanding of prostate cancer 'omics', including the sentinel role of copy number variation, the growing spectrum of oncogenic fusion genes, the potential influence of chromothripsis, and breakthroughs in defining mutation-associated subtypes. Increasing evidence suggests that genomic lesions frequently converge on specific cellular functions and signalling pathways, yet recurrent gene aberration appears rare. Therefore, it is critical that we continue to define individual tumour genomes, especially in the context of their expressed transcriptome. Only through improved characterisation of tumour to tumour variability can we advance to an age of precision therapy and personalized oncology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".