Evolution of Castration-Resistant Prostate Cancer in ctDNA during Sequential Androgen Receptor Pathway Inhibition
Bibliographic record
Abstract
Abstract Purpose: Cross-resistance renders multiple lines of androgen receptor (AR) signaling inhibitors increasingly futile in metastatic castration-resistant prostate cancer (mCRPC). We sought to determine acquired genomic contributors to cross-resistance. Experimental Design: We collected 458 serial plasma cell-free DNA samples at baseline and progression timepoints from 202 patients with mCRPC receiving sequential AR signaling inhibitors (abiraterone and enzalutamide) in a randomized phase II clinical trial (NCT02125357). We utilized deep targeted and whole-exome sequencing to compare baseline and posttreatment somatic genomic profiles in circulating tumor DNA (ctDNA). Results: Patient ctDNA abundance was correlated across plasma collections and independently prognostic for sequential therapy response and overall survival. Most driver alterations in established prostate cancer genes were consistently detected in ctDNA over time. However, shifts in somatic populations after treatment were identified in 53% of patients, particularly after strong treatment responses. Treatment-associated changes converged upon the AR gene, with an average 50% increase in AR copy number, changes in AR mutation frequencies, and a 2.5-fold increase in the proportion of patients carrying AR ligand binding domain truncating rearrangements. Conclusions: Our data show that the dominant AR genotype continues to evolve during sequential lines of AR inhibition and drives acquired resistance in patients with 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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".