Circulating Tumour DNA as a Biomarker Source in Metastatic Prostate Cancer
Bibliographic record
Abstract
Tumour molecular features are increasingly linked to treatment response and patient prognosis in advanced prostate cancer. Plasma cell-free circulating tumour DNA (ctDNA) isolated from a minimally invasive blood draw offers a convenient source of tumour material to develop clinical biomarkers. Importantly, the burden of ctDNA in the blood has strong prognostic implications at different points during the natural history of metastatic progression. In prostate cancer, the identification of somatic profiles from ctDNA requires a broad next-generation sequencing approach because of the low mutation rate and frequent structural rearrangements. Nevertheless, comparison of genomic profiles between liquid and tissue biopsies has demonstrated that ctDNA is a surrogate for tumour tissue in the metastatic setting. Our understanding of resistance to androgen receptor (AR) directed therapies has been significantly augmented by the frequent detection of AR gene amplifications, mutations, and structural rearrangements via liquid biopsy. Furthermore, early studies suggest that distinct molecular subtypes derived from ctDNA profiling can help determine the optimal therapeutic regimen for an individual patient and enable real-time monitoring for therapy response and resistance. Indeed, in clinical trials targeting the DNA damage repair pathway in prostate cancer, ctDNA-based assessment of DNA repair status is already under evaluation as a predictive biomarker. Recent advances in the study of circulating DNA fragments now make it possible to interrogate aspects of the epigenome. In this review, we describe the various applications of plasma ctDNA in metastatic prostate cancer, including its potential role as a clinically informative liquid biomarker.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".