Prospective Evaluation of Clinical Outcomes Using a Multiplex Liquid Biopsy Targeting Diverse Resistance Mechanisms in Metastatic Prostate Cancer
Bibliographic record
Abstract
PURPOSE Nearly all men with prostate cancer treated with androgen receptor (AR) signaling inhibitors (ARSIs) develop resistance via diverse mechanisms including constitutive activation of the AR pathway, driven by AR genomic structural alterations, expression of AR splice variants (AR-Vs), or loss of AR dependence and lineage plasticity termed neuroendocrine prostate cancer. Understanding these de novo acquired ARSI resistance mechanisms is critical for optimizing therapy. MATERIALS AND METHODS A novel liquid biopsy technology was used to collect mRNA from circulating tumor cells (CTCs) to measure expression of AR-Vs, AR targets, and neuroendocrine prostate cancer markers. An institutional review board–approved prospective cohort (N = 99) was used to identify patterns of gene expression. Two prospective multicenter phase II clinical trials of ARSIs for men with castration-resistant prostate cancer (ClinicalTrials.gov: NCT01942837 [enzalutamide, N = 21] and NCT02025010 [abiraterone, N = 27]) were used to further validate these findings. RESULTS Hierarchical clustering of CTC transcripts identified two distinct clusters. Cluster 2 (C2) exhibited increased expression of AR-regulated genes and was associated with worse overall survival (median 8.6 v 22.4 months; P < .01; hazard ratio [HR] = 3.45 [1.9 to 6.14]). In multivariable analysis, C2 was prognostic independent of other clinicopathologic variables. AR-V status was not significant when accounting for C2. Upon further validation in pooled multicenter phase II trials, C2 was associated with worse overall survival (15.2 months v not reached; P < .01; HR = 8.43 [2.74 to 25.92]), prostate-specific antigen progression-free survival (3.6 v 12 months; P < .01; HR = 4.64 [1.53 to 14.11]), and radiographic progression-free survival (2.7 v 40.6 months; P < .01; HR = 4.64 [1.82 to 17.41]). CONCLUSION We demonstrate that a transcriptional profile detectable in CTCs obtained from liquid biopsies can serve as an independent prognostic marker beyond AR-V7 in patients with metastatic prostate cancer and can be used to identify the emergence of multiple ARSI resistance mechanisms. This is currently being investigated in additional prospective trials.
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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 | 0.002 |
| 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 | 0.001 | 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".