Circulating Tumor DNA Genomics Correlate with Resistance to Abiraterone and Enzalutamide in Prostate Cancer
Bibliographic record
Abstract
Abstract Primary resistance to androgen receptor (AR)–directed therapies in metastatic castration-resistant prostate cancer (mCRPC) is poorly understood. We randomized 202 patients with treatment-naïve mCRPC to abiraterone or enzalutamide and performed whole-exome and deep targeted 72-gene sequencing of plasma cell-free DNA prior to therapy. For these agents, which have never been directly compared, time to progression was similar. Defects in BRCA2 and ATM were strongly associated with poor clinical outcomes independently of clinical prognostic factors and circulating tumor DNA abundance. Somatic alterations in TP53, previously linked to reduced tumor dependency on AR signaling, were also independently associated with rapid resistance. Although detection of AR amplifications did not outperform standard prognostic biomarkers, AR gene structural rearrangements truncating the ligand binding domain were identified in several patients with primary resistance. These findings establish genomic drivers of resistance to first-line AR-directed therapy in mCRPC and identify potential minimally invasive biomarkers. Significance: Leveraging plasma specimens collected in a large randomized phase II trial, we report the relative impact of common circulating tumor DNA alterations on patient response to the most widely used therapies for advanced prostate cancer. Our findings suggest that liquid biopsy analysis can guide the use of AR-targeted therapy in general practice. Cancer Discov; 8(4); 444–57. ©2018 AACR. See related commentary by Jayaram et al., p. 392. This article is highlighted in the In This Issue feature, p. 371
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".