Sartor, Oliver A.

Abstract Background: Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor (AR)-targeted agents is often lethal. Unfortunately, biomarkers for this deadly disease remain under investigation, and underpinning mechanisms are ill-understood. Here, we applied epigenomic approaches to identify cell-free (cf) DNA features associated with these lethal AR-resistant mCRPC patients. Methods: Plasma from 99 mCRPC patients was collected from two independent institutions, either prior to first-line AR-targeted therapy (n = 63) or during treatment (n = 36). We applied EnhanceAR-Seq to detect alterations in the AR locus including the upstream enhancer. We then applied genome-wide cfDNA epigenomics to 43 pre-treatment plasma samples and performed both fragmentomics and methylation sequencing to delineate the biology of AR/enhancer-altered lethal mCRPC. Finally, we identified stemness genes by applying CytoTRACE to external single-cell RNA-seq data from mCRPC, and applied these to cfDNA via promoter-level methylation analysis. We then assessed these stem-associated genes in tumor bulk RNA-seq data from an independent mCRPC cohort. Results: AR/enhancer alterations were detected in 44% of pre-treatment cfDNA samples and correlated with significantly worse progression-free survival (PFS) (HR = 2.12, P = 0.01) and overall survival (OS) (HR = 2.48; P = 0.02). AR/enhancer alterations were detected in 19% of on-treatment cfDNA samples and were also associated with profoundly worse PFS (HR = 15.38, P = 0.0003) and OS (HR = 15.53, P = 0.002). Further, genome-wide cfDNA fragmentomic analysis revealed that binding sites for developmentally relevant transcription factors such as FOXA1, NKX-3 and HOXB13 were significantly more accessible in AR/enhancer-altered lethal mCRPC patients. Gene set enrichment analysis of the 20 most accessible transcription factors revealed several pathways associated with stem cell development. Interestingly, the 10 most stem-associated genes obtained using CytoTRACE applied to an external cohort of single-cell RNA-seq data from mCRPC (He et al. 2021) were promoter-hypomethylated in the plasma cfDNA of AR/enhancer-altered lethal mCRPC patients (P = 0.005). Further, metagene analysis of these stem-associated genes revealed worse PFS by Kaplan-Meier analysis (P = 0.04), which was confirmed by multivariate cox proportional hazards modeling. Our stemness metagene further stratified overall survival (P = 0.03) when applied to an external cohort of 80 mCRPC patients from Abida et al. 2019. Conclusions: Alterations in AR including the upstream enhancer are remarkably prognostic in mCRPC with pre-treatment predictive potential. The mechanism can be inferred via cfDNA epigenomic analysis and appears to involve increased tumor cell stemness in AR-altered lethal mCRPC. Citation Format: Pradeep Singh Chauhan, Irfan Alahi, Savar Sinha, Ryan Mueller, Alexander L. Shiang, Jace Webster, Ha X. Dang, Debanjan Saha, Lilli Greiner, Breanna Yang, Elisa M. Ledet, Ramandeep K. Babbra, Wenjia Feng, Peter K. Harris, Faridi Qaium, Ellen B. Jaeger, Patrick J. Miller, Sydney A. Caputo, Oliver A. Sartor, Russell K. Pachynski, Christopher A. Maher, Aadel A. Chaudhuri. Epigenomic analysis of plasma cell-free DNA identifies stemness features associated with worse prognosis in AR-altered lethal metastatic castration resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 983.

Abstract Background: Many patients use artificial intelligence (AI) chatbots as a rapid source of health information. This raises important questions about the reliability and effectiveness of AI chatbots in delivering accurate and understandable information. Purpose: To evaluate and compare the accuracy, conciseness, and readability of responses from OpenAI ChatGPT-4 and Google Bard to patient inquiries concerning the novel 177Lu-PSMA-617 therapy for prostate cancer. Materials and Methods: Two experts listed the 12 most commonly asked questions by patients on 177Lu-PSMA-617 therapy. These twelve questions were prompted to OpenAI ChatGPT-4 and Google Bard. AI-generated responses were distributed using an online survey platform (Qualtrics) and blindly rated by eight experts. The performances of the AI chatbots were evaluated and compared across three domains: accuracy, conciseness, and readability. Additionally, potential safety concerns associated with AI-generated answers were also examined. The Mann-Whitney U and chi-square tests were utilized to compare the performances of AI chatbots. Results: Eight experts participated in the survey, evaluating 12 AI-generated responses across the three domains of accuracy, conciseness, and readability, resulting in 96 assessments (12 responses x 8 experts) for each domain per chatbot. ChatGPT-4 provided more accurate answers than Bard (2.95 ± 0.671 vs 2.73 ± 0.732, p=0.027). Bard's responses had better readability than ChatGPT-4 (2.79 ± 0.408 vs 2.94 ± 0.243, p=0.003). Both ChatGPT-4 and Bard achieved comparable conciseness scores (3.14 ± 0.659 vs 3.11 ± 0.679, p=0.798). Experts categorized the AI-generated responses as incorrect or partially correct at a rate of 16.6% for ChatGPT-4 and 29.1% for Bard. Bard's answers contained significantly more misleading information than those of ChatGPT-4 (p = 0.039). Conclusion: AI chatbots have gained significant attention, and their performance is continuously improving. Nonetheless, these technologies still need further improvements to be considered reliable and credible sources for patients seeking medical information on 177Lu-PSMA-617 therapy.