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Platelet-Coated Circulating Tumor Cells Are a Predictive Biomarker in Patients with Metastatic Castrate-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) includes a subset of patients with particularly unfavorable prognosis characterized by combined defects in at least two of three tumor suppressor genes: PTEN, RB1, and TP53 as aggressive variant prostate cancer molecular signature (AVPC-MS). We aimed to identify circulating tumor cells (CTC) signatures that could inform treatment decisions of patients with mCRPC with cabazitaxel–carboplatin combination therapy versus cabazitaxel alone. Liquid biopsy samples were collected prospectively from 79 patients for retrospective analysis. CTCs were detected, classified, enumerated through a computational pipeline followed by manual curation, and subjected to single-cell genome-wide copy-number profiling for AVPC-MS detection. On the basis of immunofluorescence intensities, detected rare cells were classified into 8 rare-cell groups. Further morphologic characterization categorized CTC subtypes from 4 cytokeratin-positive rare-cell groups, utilizing presence of mesenchymal features and platelet attachment. Of 79 cases, 77 (97.5%) had CTCs, 24 (30.4%) were positive for platelet-coated CTCs (pc.CTCs) and 25 (38.5%) of 65 sequenced patients exhibited AVPC-MS in CTCs. Survival analysis indicated that the presence of pc.CTCs identified the subset of patients who were AVPC-MS–positive with the worst prognosis and minimal benefit from combination therapy. In AVPC-MS–negative patients, its presence showed significant survival improvement from combination therapy. Our findings suggest the presence of pc.CTCs as a predictive biomarker to further stratify AVPC subsets with the worst prognosis and the most significant benefit of additional platinum therapy. Implications: HDSCA3.0 can be performed with rare cell detection, categorization, and genomic characterization for pc.CTC identification and AVPC-MS detection as a potential predictive biomarker of mCRPC. Graphical Abstract