Purpose Active surveillance (AS) is an established management strategy for favorable-risk prostate cancer (PCa). In real-world practice, decisions to continue AS or active treatment (AT) are influenced by both disease-related and patient-related factors. This study aimed to identify factors associated with the transition from AS to AT in a large multicenter cohort. Materials and Methods We analyzed 716 patients with PCa managed with AS across 12 institutions. Baseline clinical characteristics, comorbidity burden assessed via the Charlson comorbidity index (CCI), performance status evaluated using the Eastern Cooperative Oncology Group (ECOG) score, and disease-related factors, including prostate-specific antigen (PSA), PSA density (PSAD), and magnetic resonance imaging findings, were collected. Cox proportional hazards regression was used to identify factors associated with conversion to AT. Results During a mean follow-up of 28.4 months, 224 patients (31.3%) transitioned from AS to AT, most frequently because of pathologic reclassification (60.3%). In multivariate analysis, maximum core involvement (hazard ratio [HR] 1.015, p=0.003) and PSAD ≥0.10 ng/mL/cm3 (HR 1.478, p=0.044) were independently associated with conversion to AT. Although ECOG performance status and CCI were not statistically significant in multivariate models, patients with poorer functional status or greater comorbidity burden remained on AS rather than proceeding to AT. Conclusions In this large multicenter study, PSAD was the strongest predictor of transition to AT during AS for PCa. Beyond disease-related factors, patient condition also appeared to influence real-world decisions about continuing AS.
Joongwon Choi, Chung-Un Lee, S. Jeon et al.· Investigative and Clinical U...· 0 citations
Despite the clinical utility of androgen receptor pathway inhibitors (ARPIs) and taxanes, a rare and highly aggressive subset of metastatic castration-resistant prostate cancer (mCRPC) exhibits inevitable dual resistance. The epigenetic drivers behind this treatment-refractory phenotype remain poorly defined. This study aimed to identify rare circulating DNA methylation signatures that predict therapeutic escape and elucidate their coordination with circulating tumor cell (CTC) transcriptomic profiles.
Using a machine learning–based random forest model applied to TCGA data (n=499), we identified three discriminative CpG loci (C2orf88, HAPLN3, APC), which were validated across four independent cohorts (AUC >0.97). Plasma-derived ctDNA and paired CTCs from 87 mCRPC patients were analyzed using methylation-sensitive high-resolution melting (MS-HRM) and droplet digital PCR (ddPCR). Correlations between methylation burden and 46 CTC-specific transcripts were evaluated to map the epigenetic–transcriptional axis in resistant states.
Methylation levels escalated significantly with metastatic progression (p <0.001). In mCRPC, elevated combined methylation scores (three-marker panel) were independently associated with shorter overall survival (HR=3.63, p <0.001) and significantly poorer radiographic progression-free survival (rPFS) in both ARPI- and docetaxel-treated cohorts. Notably, high methylation burden was positively correlated with AR-driven survival markers (PSA: r=0.568; PSMA: r=0.409) but inversely correlated with tumor suppressors (TP53: r=–0.386) and mesenchymal markers (VIM: r=–0.314). This unique signature suggests a rare epithelial-fixed, AR-reinforced phenotype that evades conventional therapeutic pressure through epigenetic stabilization rather than traditional mesenchymal transition.
Machine learning–based ctDNA methylation profiling identifies a clinically actionable molecular signature associated with rare dual-resistance phenotypes in advanced prostate cancer. These findings provide a biological framework for treatment stratification and the early identification of non-responders in the era of precision oncology.
Generative AI was used to help revise and translate the text written by the author to ensure clarity and adherence to conference guidelines.
Jae-Seung Chung, Hyungseok Cho, Da-Won Kim, Min-Hyeok Jung, Jiwon Cha Cha, Ki-Ho Han, Seok-Soo Byun, Seung-Taek Lee, Young-Joon Kim. Deciphering Rare Patterns of Dual Drug Resistance in metastatic castration resistant prostate cancer through Machine Learning–Based ctDNA Methylation and CTC Transcriptomics [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr B050.
Jae-Seung Chung, Hyungseok Cho, Da-Won Kim et al.· Cancer Research· 0 citations
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