Practical considerations for optimising homologous recombination repair mutation testing in patients with metastatic prostate cancer
Bibliographic record
Abstract
Analysis of the genomic landscape of prostate cancer has identified different molecular subgroups with relevance for novel or existing targeted therapies. The recent approvals of the poly(ADP-ribose) polymerase (PARP) inhibitors olaparib and rucaparib in the metastatic castration-resistant prostate cancer (mCRPC) setting signal the need to embed molecular diagnostics in the clinical pathway of patients with mCRPC to identify those who can benefit from targeted therapies. Best practice guidelines in overall biospecimen collection and processing for molecular analysis are widely available for several tumour types. However, there is no standard protocol for molecular diagnostic testing in prostate cancer. Here, we provide a series of recommendations on specimen handling, sample pre-analytics, laboratory workflow, and testing pathways to maximise the success rates for clinical genomic analysis in prostate cancer. Early involvement of a multidisciplinary team of pathologists, urologists, oncologists, radiologists, nurses, molecular scientists, and laboratory staff is key to enable optimal workflow for specimen selection and preservation at the time of diagnosis so that samples are available for molecular analysis when required. Given the improved outcome of patients with mCRPC and homologous recombination repair gene alterations who have been treated with PARP inhibitors, there is an urgent need to incorporate high-quality genomic testing in the routine clinical pathway of these patients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".