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PMID: 42049996 Published · epublish English

Multi-modal circulating cell-free DNA profiling to predict response to docetaxel in metastatic castration-resistant prostate cancer.

NPJ precision oncology ·Vol. 10 ·No. 1 ·2026-04-28

Chen DD, Zimmer A, Yang DD, Francini E, Patton R, Crowdis J, Chandra P, Bin Riaz I, Hanratty B, Rickles-Young M, Tsuji J, Cibulskis C, Fleharty M, Whelpley B, Reardon B, Park J, Nelson PS, Huang FW, Van Allen EM, Ha G, Choudhury AD

Abstract

There are currently no clinically validated markers for taxane sensitivity in metastatic castration-resistant prostate cancer (mCRPC), so we aimed to predict docetaxel response from circulating cell-free DNA. We identified 180 patients with pre-treatment plasma specimens collected within 12 months of starting docetaxel for mCRPC at our institution. 138 underwent ultra-low pass whole genome sequencing (ULP-WGS), and tumor fractions (TFx) and copy number alterations (CNAs) were derived using ichorCNA. 79 samples with TFx > 0.04 underwent targeted panel sequencing (TPS). TP53 mutation was significantly associated with docetaxel non-response (p = 0.018); deletions involving bands located in arms 11p, 11q, 10q and 3p were enriched in responders, and amplifications in regions of 1p and 6q were enriched in non-responders. Transcription factor (TF) binding activity was inferred using Griffin, which identified TFs (ZSCAN4, CTCF, PHOX2B) with trends towards increased activity in non-responders (n = 22) and others (including PBX1, MYBL2, OSR2, PDX1 and ZIC2) in responders (n = 24). A combined ensemble binary classifier generated through XGBoost integrating these feature sets to predict docetaxel response outperformed models derived from any single feature set, achieving a training area-under-the-ROC curve of 0.87. Pre-cabazitaxel specimens, representing a docetaxel-resistant population, were used for external validation, with a concordance of 79.6% for predicting non-response.

Article Info
Journal
NPJ precision oncology
Abbr.
NPJ Precis Oncol
ISSN
2397-768X
Published
2026-04-28
Language
English
Country/Region
England
NLM ID
101708166
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