Concordance of <i>BRCA1</i>, <i>BRCA2</i> (BRCA), and <i>ATM</i> mutations identified in matched tumor tissue and circulating tumor DNA (ctDNA) in men with metastatic castration-resistant prostate cancer (mCRPC) screened in the PROfound study.
Bibliographic record
Abstract
26 Background: Not all mCRPC patients have available or sufficient tissue for multigene molecular testing. In the Phase 3 PROfound study, olaparib significantly improved radiographic progression-free survival compared with physician’s choice of abiraterone or enzalutamide in men with homologous recombination repair (HRR)-gene-mutated mCRPC (de Bono et al. N Engl J Med 2020). Overall, 31% of patients’ tissue samples failed molecular screening during the study, showing the need for additional testing methods to detect patients with HRR-gene-mutated cancers. We evaluated the utility of plasma-derived ctDNA to identify deleterious BRCA and ATM mutations in screened patients from PROfound. Methods: Tumour samples were prospectively tested at Foundation Medicine, Inc (FMI) using an investigational next-generation sequencing test (based on FoundationOne CDx) to inform trial eligibility. Matched ctDNA samples were sequenced at FMI with the FoundationOne Liquid CDx assay. Tissue samples were clinically heterogeneous regarding location and timing of collection; plasma samples were collected as part of screening in PROfound. Results: 81% (503/619) of ctDNA samples tested yielded a result, of which 491 had a tumour result. BRCA and ATM status in tissue compared with ctDNA reported 81% (95% CI 75–87%) positive percentage agreement (PPA) and 92% (95% CI 89–95%) negative percentage agreement (NPA), with tissue as reference (Table). Further concordance and discordance measures will be presented. Conclusions: High concordance between tumour tissue and ctDNA supports the development of ctDNA testing as a minimally invasive method to identify patients with HRR-gene-mutated mCRPC and guide treatment decisions, particularly for those with insufficient tissue for genomic analyses. Clinical trial information: NCT02987543. [Table: see text]
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".