Androgen Receptor Gene Aberrations in Circulating Cell-Free DNA: Biomarkers of Therapeutic Resistance in Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Although novel agents targeting the androgen-androgen receptor (AR) axis have altered the treatment paradigm of metastatic castration-resistant prostate cancer (mCRPC), development of therapeutic resistance is inevitable. In this study, we examined whether AR gene aberrations detectable in circulating cell-free DNA (cfDNA) are associated with resistance to abiraterone acetate and enzalutamide in mCRPC patients. EXPERIMENTAL DESIGN: Plasma was collected from 62 mCRPC patients ceasing abiraterone acetate (n = 29), enzalutamide (n = 19), or other agents (n = 14) due to disease progression. DNA was extracted and subjected to array comparative genomic hybridization (aCGH) for chromosome copy number analysis, and Roche 454 targeted next-generation sequencing of exon 8 in the AR. RESULTS: On aCGH, AR amplification was significantly more common in patients progressing on enzalutamide than on abiraterone or other agents (53% vs. 17% vs. 21%, P = 0.02, χ(2)). Missense AR exon 8 mutations were detected in 11 of 62 patients (18%), including the first reported case of an F876L mutation in an enzalutamide-resistant patient and H874Y and T877A mutations in 7 abiraterone-resistant patients. In patients switched onto enzalutamide after cfDNA collection (n = 39), an AR gene aberration (copy number increase and/or an exon 8 mutation) in pretreatment cfDNA was associated with adverse outcomes, including lower rates of PSA decline ≥ 30% (P = 0.013, χ(2)) and shorter time to radiographic/clinical progression (P = 0.010, Cox proportional hazards regression). CONCLUSIONS: AR gene aberrations in cfDNA are associated with resistance to enzalutamide and abiraterone in mCRPC. Our data illustrate that genomic analysis of cfDNA is a minimally invasive method for interrogating mechanisms of therapeutic resistance in mCRPC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".