Identification of Actionable Mutations in Metastatic Castration-Resistant Prostate Cancer Through Circulating Tumor DNA: Are We There Yet?
Bibliographic record
Abstract
Circulating tumor DNA (ctDNA) analysis has emerged as a powerful and minimally invasive approach for genomic profiling of metastatic castration-resistant prostate cancer (mCRPC), enabling real-time detection of tumor-derived mutations that guide therapy. Approximately 20% of mCRPC patients harbor alterations in homologous recombination repair (HRR) genes, most commonly BRCA1/2 and ATM, which are actionable with different poly-(ADP-ribose) polymerase inhibitors (PARPIs) used as monotherapy or in combination with androgen receptor signaling inhibitors (ARSIs). A smaller subset of patients with mismatch repair deficiency (MMRd) or microsatellite instability-high (MSI-high) tumors may benefit from immune checkpoint blockade with pembrolizumab. Different FDA-approved liquid biopsy assays detect these actionable alterations when tissue biopsies are unavailable or insufficient. This review summarizes current evidence on ctDNA-based genotyping in mCRPC, highlighting clinically actionable mutations, corresponding targeted therapies, and technical and analytical considerations for clinical implementation. By capturing DNA shed from multiple metastatic sites, ctDNA profiling provides a comprehensive view of tumor heterogeneity and enables serial monitoring of molecular evolution. Overall, ctDNA analysis represents a transformative advance in precision oncology, supporting personalized treatment selection and ongoing assessment of therapeutic response in mCRPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".